Can Long-Context Large Language Models Do Your Job?

In this post we test the abilities of long-context large language models for performing patent analysis. How do they compare with a patent partner charging £400-600 an hour?

Or have I cannibalised my job yet?

Or do we still need Retrieval Augmented Generation?

What is a Long-Context Large Language Model?

Large Language Models (LLMs)

Large Language Models (LLMs) are neural network architectures. They are normally based on a Transformer architecture that applies self-attention over a number of layers (~11?). The more capable models have billions, if not trillions, of parameters (mostly weights in the neural networks). The most efficient way to access these models is through a web Application Programming Interface (API).

Long Context

LLMs have what is called a “context window”. This is a number of tokens that can be ingested by the LLM in order to produce an output. Tokens are roughly mapped to words (the Byte-Pair Encoding – BPE – tokeniser that is preferred by most models is described here – tokens are often beginnings of words, word bodies, and word endings).

Early LLMs had a context of ~512 tokens. This quickly grew to between 2000 and 4000 tokens for commercially available models in 2023. Context is restricted because the Transformer architecture performs its matrix computations over the context; the size of the context thus fixes the size of certain matrix computations – the longer the context, the more parameters and the larger the matrices involved.

In late 2023/early 2024, a number of models with long context emerged. The context window for GPT3.5 quickly extended to 8k, then 16k, then 32k. This was then followed later in 2023 by a longer 32k context for the more capable GPT4 model, before a 128k context window was launched in November 2023 for the GPT4-Turbo model.

(Note: I’ve often found a lag between the “release” of models and their accessibility to Joe Public via the API – often a month or so.)

In January 2024, we saw research papers documenting input contexts of up to a million tokens. These appear to implement an approach called ring attention, that was described in a paper in October 2023. Anthropic AI released a model called Opus in March 2024 that appeared comparable to GPT4 and had a stable long context of 200k tokens.

We thus seem to be entering a “long context” era, where whole documents (or sets of documents) can be ingested.

What Patent Task Shall We Test?

Let’s have a look at a staple of patent prosecution: novelty with respect to the prior art.

Let’s start reasonably easy with a mechanical style invention. I’ve randomly picked WO2015/044644 A1 from the bucket of patent publications. It’s a Dyson application to a hair dryer (my tween/teenage girls are into hair these days). The prior art citations are pretty short.

A hair care appliance comprising a body having an outer wall, a duct extending
at least partially along the body within the outer wall, an interior passage
extending about the duct for receiving a primary fluid flow, a primary fluid
outlet for emitting the primary fluid flow from the body, wherein the primary
fluid outlet is defined by the duct and an inner wall of the body, wherein at least
one spacer is provided between the inner wall and the duct.

Claim 1

In the International phase we have three citations:

D1 and D2 are used to support a lack of novelty, so we’ll look at them.

Note: we will not be looking at whether the original claim is or is not novel from a legal perspective. I have purposely not looked into anything in detail, nor applied a legal analysis. Rather we are looking at how the language models compare with a European Examiner or Patent Attorney. The European Examiner may also be incorrect in their mapping. As we know, LLMs can also “hallucinate” (read: confabulate!).

Top Models – March 2024 Edition

There are two:

GPT4-turbo; and
Claude 3 Opus.

These are the “top” models from each of OpenAI and Anthropic. I have a fair bit of experience with GPT3.5-Turbo, and I’ve found anything less than the “top” model is not suitable for legal applications. It’s just too rubbish.

For the last year (since April 2024), GPT4 has been the king/queen, regularly coming 10-20% above other models in evaluations. Nothing has been close to beating it.

GPT4-turbo performs slightly worse that GPT4, but it’s the only model with a 128k token context. It is cheaper and quicker than GPT4. I’ve found it good at producing structured outputs (e.g., nice markdown headings etc.) and at following orders.

Claude 3 Opus has a 200k token context and is the new kid on the block. The Opus model is allegedly (from the metrics) at the level of GPT4.

It’s worth noting we are looking at the relatively bleeding edge of progress here.

GPT4-turbo was only released on 6 November 2023. On release it had certain issues that were only resolved with the 25 January 2024 update. We will use the 25 January 2024 version of the model. I’ve noticed this January model is better than the initially released model.

Claude 3 Opus was only released on 4 March 2024. This exercise will test it’s abilities.

Can I use a Local Model?

Short answer: no.

Longer answer: not yet.

There are a couple of 1 million token models available. See here if you are interested. I tried to run one locally.

It needed 8.8TB of RAM. (My beefy laptop has 64GB RAM and 8GB VRAM – only short 8724GB.)

Progress though is super quick in the amateur LLM hacking sphere (it’s only big matrix multiplication in an implementation). So we might have an optimised large context model by the end of the year.

Also I’ve found the performance of the “best” open-source 7B parameter models (those that I can realistically run on my beefy computers) is still a long way away from GPT4, more GPT3.5-Turbo level, which I have found “not good enough” for any kind of legal analysis. Also, I’ve found open-source models to be more tricky to control to get appropriate output (e.g., doing what you ask, keeping to task etc.).

How much?

You have to pay for API access to GPT4-Turbo and Claude 3. It’s not a lot though, being counted in pence for each query. I’ve found it’s worth paying £5-10 a month to do some experiments on the top models.

Here are some costings based on the patent example above, that has two short prior art documents.

The claim is around 100 tokens. The prior art documents (D1 and D2) are around 3000 and 6000 tokens. Throw in a bundle of tokens for the input prompts and you have around 9200 tokens input for two prior art documents.

On the output side, a useful table comparing a claim with the prior art is around 1500 tokens.

GPT4 Turbo

GPT4-Turbo has a current pricing of $10/1M tokens on the input and $30/1M tokens on the output. So we have about 10 cents ($0.092) on the input and about 5 cents on the output ($0.045). Around 15 cents in total (~12p). Or around 1s (!!!) of chargeable patent partner time.

Claude 3

The pricing for Claude is similar but a little more expensive – $15/1M on the input and $75/1M on the output (reflecting the alleged more-GPT4 than GPT4-Turbo level).

So we have about 15 cents ($0.138) on the input and about 15 cents on the output ($0.1125). Around 30 cents in total (~24p). Or around 2s (!!!) of chargeable patent partner time.

These costs are peanuts compared to the amounts charged by attorneys and law firms. It opens up the possibility of statistical analysis, e.g. multiple iterations or passes through the same material.

First Run

For our experiments we will try to keep things as simple as possible. To observe behaviour “out-of-the-box”.

Prompts

For a system prompt I will use:

You are a patent law assistant.
    
You will help a patent attorney with patent prosecution.
    
Take an European Patent Law perspective (EP).

As our analysis prompt scaffold I will use:

Here is an independent patent claim for a patent application we are prosecuting:    
---
{}
---
    
Here is the text from a prior art document:
---
{}
---
    
Is the claim anticipated by the prior art document?
* Return your result with a markdown table with a feature mapping
* Cite paragraph numbers, sentence location, and/or page/line number to support your position
* Cite snippets of the text to demonstrate any mapping

The patent claim gets inserted in the first set of curly brackets and the prior art text gets inserted in the second set of curly brackets.

We will use the same prompts for both models. We will let the model choose the columns and arrangement of the table.

Getting the Text

To obtain the prior art text, you can use a PDF Reader to OCR the text then save as text files. I did this for both prior art publication PDFs as downloaded from EspaceNet.

You can also set up Tesseract via a Python library, but it needs system packages so can be fiddly and needs Linux (so I sometimes create a Docker container wrapper).
Python PDF readers are a little patchy in my experience. There are about four competing libraries with stuff folding and being forked all over the place. They can struggle on more complex PDFs. I think I use pyPDF. I say “I think” because you did have to use pyPDF2, a fork of pyPDF, but then they remerged the projects, so pyPDF (v4) is a developed version of pyPDF2. Simples, no?
You can also use EPO OPS to get the text data. But this is also a bit tricky to set up and parse.
It’s worth noting that the OCRed text is often very “noisy” – it’s not nicely formatted in any way, often has missing or misread characters, and the whitespace is all over the place. I’ve traditionally struggled with this prior to the LLM era.

The claim text I just copied and pasted from Google patents (correctness not guaranteed).

Simple Client Wrappers

Nothing fancy to get the results, just some short wrappers around the OpenAI and Anthropic Python clients:

def compare_claim_with_prior_art_open_ai(claim: str, prior_art: str, system_msg: str = SYSTEM_PROMPT, model: str = OPENAI_MODEL):
    """Get the chat based on a user message."""
    completion = openai_client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": system_msg},
            {"role": "user", "content": PROMPT_SCAFFOLD.format(claim, prior_art)}
        ],
        temperature=0.3
    )
    return completion.choices[0].message.content

def compare_claim_with_prior_art_anthropic(claim: str, prior_art: str, system_msg: str = SYSTEM_PROMPT, model: str = ANTHROPIC_MODEL):
    """Get the chat based on a user message."""
    message = anthropic_client.with_options(max_retries=5).messages.create(
        model=model,
        max_tokens=4000,
        temperature=0.3,
        system=SYSTEM_PROMPT,
        messages=[
            {"role": "user", "content": PROMPT_SCAFFOLD.format(claim, prior_art)}
        ]
    )
    return message.content[0].text

Results

(In the analysis below, click on the images if you need to make the text bigger. Tables in WordPress HTML don’t work as well.)

D1 – GPT4-Turbo

Here’s GPT4-Turbo first off the blocks with D1:

Let’s compare again with the EP Examiner:

Successes:

“hair care appliance” – yes, gets this and cites the same objects as the EP Examiner (actually does a better job of referencing but hey ho).
“spacer” – while GPT4-Turbo says this is not “explicitly mentioned”, it does cite the “struts 24”, which are the same features cited by the EP Examiner.

Differences:

“outer wall” – deemed to be not explicitly present – doesn’t make the jump made by the EP Examiner to find this feature implicit in the structure of the “hair dryer 2”.
“duct…within the outer wall” – GPT4-Turbo decides to cite an inner hot air passageway formed by the fan 3 and heater 4 – on a brief look this seems possibly valid in isolation. However, there is an argument that it’s the outer passageway 12 that better extends within the outer wall.
“interior passage” – GPT4-Turbo can’t find this explicitly mentioned. Interestingly, the EP Examiner doesn’t cite anything directly to anticipate this feature, so we can maybe assume it is meant to be implicit?
“primary fluid flow outlet” – GPT4-Turbo cites the “blower opening 7”, which is an fluid outlet.
“primary fluid flow outlet defined by the duct and an inner wall of the body” – GPT4-Turbo says this is implicit saying it is defined by “inner structures”. It’s not the most convincing but looking at the picture in Figure 1, it could be argued. I do think the EP Examiner’s “cold air nozzle” is a bit of a better fit. But you could possible argue both?

We will discuss this in more detail in the next section, but for now let’s also look at Claude 3…

D1 – Claude 3 Opus

Now let’s see how new kid, Opus, performs:

Successes:

“hair care appliance” and “outer wall” – yes, gets this and cites the same objects as the EP Examiner (actually does a better job of referencing but hey ho).
“primary fluid outlet” – hedges its bets by referring to both the hot and cold air streams but slightly better matches the EP Examiners citation.

Differences:

“duct…within the outer wall” – Claude 3’s a bit more bullish than GPT4-Turbo, announcing this is not disclosed. I’d warrant that there’s more evidence for it being disclosed than not disclosed so would side more with the EP Examiner than Claude.
“interior passage” – Again, whereas GPT4-Turbo was a little more tentative, Claude 3 appears more confident in saying this is not disclosed. I don’t necessarily trust its confidence but as before the EP Examiner is silent on what explicitly anticipates this feature.
“primary fluid flow outlet defined by the duct and an inner wall of the body” – Claude 3 here says it is not disclosed, but I don’t thing this is entirely right.
“spacer” – Claude 3 says this isn’t disclosed and doesn’t mention the “struts 24”.

D1 – First Round Winner?

I’d say GPT4-Turbo won that round for D1.

It didn’t entirely match the EP Examiner’s mapping, but was pretty close.

Both models were roughly aligned and there was overlap in cited features.

I’d still say the EP Examiner did a better job.

Let’s move onto D2.

D2 – GPT4-Turbo

Here’s what the EP Examiner said about D2:

Helpful. Here’s the search report:

Also helpful.

Here’s Figure 1:

And here’s the results:

Successes:

“body having an outer wall” – yes, in isolation this can be argued.
“duct” – does say this appears to be present but does indicate the word “duct” is not explicitly used (CTRL-F says: “correct”).
“interior passage” – GPT4-Turbo cites the flow “through the casing to the grille”, where the casing is 12 in Figure 1 and the grill is 24 (using those would help GPT4-Turbo!). This I think can be argued in isolation.
“primary fluid outlet” – lines 50 to 60 of column 2 do refer to a “blow opening” as quoted and the “primary fluid flow” does go from the grille to the “blow opening”. Good work here.

Differences / Failures:

“A hair care appliance” has gone walkabout from the claim features.
“…defined by the duct and an inner wall” – GPT4-Turbo says this is not explicitly disclosed but does take a guess that it is implicitly disclosed. I would like some more detailed reasoning about what features could stand in for the duct and inner wall. But I’d also say the GPT4-Turbo is not necessarily wrong. In the Figure 1, there is a “air flow passage 33” between the “back shell 20” and the “reflector-shield 28”, which could be mapped to a “duct” and an “inner wall”?
“spacer” – GTP4-Turbo can’t find this. If you mapped the “air flow passage 33” to the “duct”, “spacers” may be implicit? A discussion on this and its merits would be useful. Checking D2, I see there is explicit disclosure of “spacer means” in line 55 of column 3. I’m surprised this is absent.

D2 – Claude 3 Opus

Successes:

“hair care appliance” and “outer wall” – yes, although I think GPT4-Turbo’s “back shell 20” is better.
“primary fluid outlet” – yes, I think element 36 and the front “grille” can be argued in isolation to be a “primary fluid outlet”

Differences / Failures:

“duct” – Claude 3 does say this is present but the cited text isn’t amazingly useful, despite being from the document. It’s not clear what is meant to be the “duct”. However, it is true you could argue something within the back and front shell is a duct.
“interior passage” – similar to “duct” above. Claude 3 says it is present but the text passage provided, while from the document, doesn’t seem entirely relevant to the claim feature.
definition of “primary fluid outlet” – Claude’s 3 reasoning here seems appropriate if you have the molded “multiple purpose element 36” as the “primary fluid outlet” but there is maybe room to argue “periphery openings 42” help define the “element 36”? Definitely room for a discussion about whether this feature is present.
“spacer” – as per GPT4-Turbo, Claude 3 says this is not present despite there being “spacer means” in line 55 of column 3.

D2 – First Round Winner?

GPT4-Turbo and Claude 3 both do a little less well on the twice-as-long D2.

They do have the disadvantage of not being able to use the figures (*yet*).

Their lack of discussion of the “air flow passage 33” formed from “openings 42” is a little worrying. As is their ignorance of the “spacer means” in line 55 of column 3.

Patent attorney and EP Examiner win here.

Repeatability

As I was running some tests (coding is iterative, you fail, then correct, then fail, then correct until it works), I noticed that there was a fair bit of variation in the mapping tables I was getting back from both models. This is interesting as a human being would expect a mapping to be relatively stable – the claims features are either anticipated, or they are not.

Here’s GPT4-Turbo again on D1:

Here’s the previous run:

We can see the following issues:

In the first analysis GPT4-Turbo thought the “outer wall” was disclosed. In the second run, it said it was not explicitly mentioned.
Also note how we have slightly different “features” for each run, and differing columns and formats.
The mapping for the “duct” is also different, with differently levels of “confidence” on the presence and the possible implicit features.
On the first run, GPT4-Turbo though the “interior passage” was “not explicitly mentioned” but on the second run thought it was implied by structures and provided paragraph references.
Different features are mapped to the “primary fluid outlet”.
It locates the “struts 24” on both runs but on the first run thinks they are “functionally similar”, while on the second run finds them to “serve a different purpose”.

Uh oh. We have quite a different mapping each time we perform the run.

Let’s look at running Claude 3 again:

As compared to the previous run:

Claude 3 seems slightly more consistent between runs. We can see that the columns have shifted around, and I don’t necessarily agree with the mapping content, but the mapping detail seems mostly conserved.

Let’s look at another run for Claude 3 on D2:

Here Claude 3 does much better than the first run. The citation column appears more relevant. And party-time, it’s found and mentioned the “spacer means”. The “interior passage” mapping is better in my opinion, and is more reflectively of what I would cite in isolation on a brief run through.

Working on the Prompt

Maybe we can overcome some of these variability problems by working on the prompt.

It may be that the term “anticipated” is nudging the analysis in a certain, more US centric, direction. Let’s try explicitly referencing Article 54 EPC, which is more consistent with us setting a “European Patent Law perspective” in the system prompt.

Also let’s try shaping the mapping table, we can specify columns we want filled in.

Here’s then a revised prompt:

Here is an independent patent claim for a patent application we are prosecuting:
---
{}
---
    
Here is the text from a prior art document:
---
{}
---
    
Is the claim novel under Art.54 EPC when compared with the prior art document?
* Return your result with a markdown table with a feature mapping
* Cite paragraph numbers, sentence location, and/or page/line number to support your position
* Cite snippets of the text to demonstrate any mapping
    
Here is the start of the table:
| # | Feature Text | In prior art? Y/N | Where in prior art? | Any implicit disclosure? | Comments |
|---| --- | --- | --- | --- | --- |

Does that help?

In short – not really!

GPT4-Turbo seems to do a little worse with this new prompt. It appears more certain about the mapping – e.g. the “duct” is deemed not in the prior art (“N”), with no implicit disclosure and simply a statement that “The prior art does not explicitly describe a duct within the outer wall of the body”. This can be compared to the first run where this was deemed present “indirectly”.

GPT4-Turbo also introduces an error into the claim mapping, which we discuss later below.

Even though we specify more columns, the amount of text generated appears roughly the same. This means that for both models our reasoning is a bit shorter, and the models tend towards more fixed statements of presence or, more often, non-presence.

Also, although our “In prior art? Y/N” column provides a nice single letter output we can parse into a structured “True” or “False”, it does seem to nudge the models into a more binary conclusion. For example, the comments tend to confirm the presence conclusion without additional detail, whereas when the model was able to pick the columns, there was a longer, more useful discussion of potentially relevant features.

I had hoped that the “Any implicit disclosure” column would be a (sub) prompt for considering implicit disclosures a bit more creatively. This doesn’t seem to be the case for both models. Only Claude 3 uses it once in the D2 mapping (although it does use it there in the way I was hoping). I think we will ditch that column for now.

This little experiment suggests that keeping any mapping table as simple as possible helps improve performance. It also shows that LLM-wrangling is often as much of an art as a science.

Does Temperature Make a Difference?

Temperature is a hyperparameter that scales the logits output by the model prior to sampling the probabilities. This is a nice explanation. Or in English, it controls how “deterministic” or “random” the model output is. Values of around 0.1 / 0.2 should have pretty consistent output without much variation, values around and above 1 will be a lot more “creative”.

I general use a temperature of somewhere between 0.3 and 0.7. I have found that higher temperatures (around 0.7) are sometimes better for logical analysis where a bit of “thinking outside the obvious” is required.

Let’s go back to a three column table with “Claim Feature”, “Prior Art Mapping”, and “Cited Portions”. Let’s then run a round with a temperature of 0.7 and a temperature of 0.1. We will at least keep the prompt the same in both cases.

From the experiments above, it may be difficult to determine the effect of temperature over and above variability inherent in the generating of responses, but let’s have a look anyway.

(Those with a proper science degree look away.)

GPT4-Turbo and D1

Temperature = 0.7

Temperature = 0.1

There doesn’t actually seem to be that much difference between the mappings here, apart from that underlying variability discussed before.

It may be that with the temperature = 0.7 run, the model is freer to diverge from a binary “yes/no” mapping.

In the temperature = 0.1 run, GPT4-Turbo has actually done pretty well, matching the EP Examiner’s conclusions on all features apart from the last feature (but at least indicating what could be mapped).

Claude 3 and D1

Temperature = 0.7

Temperature = 0.1

Here we can again see that Claude 3 seems more consistent between runs. While there are some small differences, the two runs are very similar, with often word-for-word matches.

Claude 3 does well here, pretty much matching the EP Examiner’s objection in both cases.

GPT4-Turbo and D2

Temperature = 0.7

Temperature = 0.1

Here we can see the variation of the GPT4-Turbo. With one mapping, all the features are found in the prior art; with the other mapping, nearly all the features are not found in the prior art. Which to believe?!

Claude 3 and D2

Temperature = 0.7

Temperature = 0.1

Again Claude 3 seems much more consistent across runs. But I’m not that impressed with the reasoning – e.g. compare these to the “good” GPT4-Turbo run above.

So in conclusion, temperature doesn’t seem to make a load of difference here. It is not a silver bullet, transforming “bad” mappings into “good”. The issues with performance and consistency appear to be model, rather than hyperparameter based.

Failure Cases

Missing or Modified Claim Features

With GPT4-Turbo there was a case where the claim features did not entirely sync up with the supplied claim text:

Here “[a] hair care appliance comprising” has gone walk-about.

Also GPT4-Turbo seems to paraphrase some of the claim features in the table.

The feature “an interior passage extending about the duct for receiving a primary fluid flow” becomes “interior passage for receiving a primary fluid flow“. Such paraphrasing by a trainee would give senior patent attorneys the heebie-jeebies.

Making Up Claim Features

This is an interesting failure case from GPT4-Turbo. It appears to get carried away and adds three extra claim features to the table. The claim doesn’t mention infra-red radiation anywhere…

As with the case below, it seems to be getting confused with the “claim 1” we are comparing and the “claim 1” of the prior art. It is interesting to note this occurs for the longer prior art document. It is a nice example of long document “drift”. I note how RAG offers a solution to this below.

I found similar behaviour from GPT3.5-turbo. GPT3.5-turbo was a lot worse, it often just made up the claim features, or decided to take them from the comparison text instead of the claim. Similar to if you gave the exercise to a 6 year-old.

Confusing Claim Features

Here Claude 3 does what at first site looks like a good job. Until, you realise the LLM is mapping what appears to be a claim from the prior art document, onto the prior art document.

This may be an issue I thought we’d might see in long context models. In the prompt we put the claim we are comparing first. But then we have 6000 tokens from D2. It looks like this might cause the model to “forget” the specific text of the claim but “remember” that we are mapping some kind of “claim” and so pick the nearest “claim” – claim 1 of D2.

Looking at the claim 1 of D2 this does appear to be the case:

In a hand held hair dryer having means for directing air flow toward hair to be dried, the improvement comprising, in combination:
a casing having a forward grille-like support member adapted to be faced toward the hair to be dried;
an infra-red, ring-shaped, radiator in said casing spaced rearwardly of the grille-like member;
a motor carried on the grille-like member and extending rearwardly thereof centrally of said ring shaped radiator;
shield means between the ring-shaped radiator and the motor for protecting the motor from the infrared radiation; radiation reflector means, including a portion spaced rearwardly of the ring-shaped radiator for directing reflected radiation toward and through the grille-like member;
a flat air propeller operatively associated with and driven by the motor and located spaced axially formed of the rearward portion of the reflector and rearward of the ring-shaped radiator, the propeller being operative to direct only a gentle flow of air through said grille toward the hair to be dried, to avoid destruction and disarray of the hairdo, but to move the top layers of hair sufficiently to permit radiation drying of the hair mass; and
means for introducing cooling air into the casing to cool portions of the casing and the motor.
Claim 1 of D2

It’s interesting to note that this was also a problem we found with GPT3.5-Turbo.

Conclusions and Observations

What have we found out?

Results are at a possibly-wrong, average-ability, science-graduate level.
Prompt crafting is an art – you can only learn by doing.
Temperature doesn’t matter that much.
Variability is a problem with GPT4-Turbo.
LLMs can get confused on longer material.

At the start we had three questions:

How do the models compare with a patent partner charging £400-600 an hour?
Have I cannibalised my job yet?
Do we still need Retrieval Augmented Generation?

Let’s see if we can partially answer them.

How do the models compare with a patent partner charging £400-600 an hour?

Ignoring cost, we are not there yet. Patent attorneys can sigh in relief for maybe another year.

But considering the models cost the same as 1-2s of patent partner time, they didn’t do too bad at all.

One big problem is consistency.

GPT4-Turbo has some runs and some feature mappings that I am fairly happy with. The problem is I perform a further run with the same prompt and the same parameters and I get a quite different mapping. It is thus difficult to “trust” the results.

Another big problem is apparent confidence.

Both models frequently made quite confident statement on feature disclosure. “This feature is not disclosed”. However, on the next mapping run, or by tweaking the prompt, the feature was found to be disclosed. So the confident statements are more features of the output. The models don’t seem to do accurate shades of confidence out-of-the-box.

If you are a skeptical person like myself, you might not believe what you are told by human or machine (watch the slide into cynicism though). In which case, you’d want to see and review the evidence for any statement yourself before agreeing. If you treat LLMs in this manner, like a brand new graduate trainee, sometimes helpful, sometimes off, then you are well placed.

If you are a nice trusting human being that thinks that both human beings and machines are right in what they say, you will come to harm using LLMs. LLMs are particularly slippery because they provide the most likely, not the most factually correct, output. While the two are correlated, correlation does not necessarily equal truth (see: science).

Often, a clear binary mapping (“Yes – the feature is disclosed”) leads the model to later justify that sampling (“The feature is disclosed because the feature is disclosed”) rather than provide useful analysis. We had better performance when we were less explicit in requiring a binary mapping. However, this then leads to problems in parsing the results – is the feature disclosed or not?

Have I cannibalised my job yet?

Not quite.

But if I needed to quickly brainstorm mappings for knocking out a claim (e.g., in an opposition), I might run several iterations of this method and look at the results.

Or if I was drafting a claim, I could “stress test” novelty against known prior art by iterating (e.g. 10-20 times?) and looking at the probabilities of feature mappings.

If neither model can map a feature, then I would be more confident in the robustness in examination. These would be the features it is worth providing inventive step arguments for in the specification. But I would want to do a human review of everything as well.

While I do often disagree with many of the mappings, they tend not to be completely “wrong”. Rather they are often just poor argued or evidenced, miss something I would pick up on, or the mapping is inconsistent across the whole claim. So at the level of “quick and dirty opposition”, or “frustrating examiner getting the case off their desk”.

If models do map a feature, even if I don’t agree with the mappings, they give me insight into possible arguments against my claim features. This might enable me to tweak the claim language to break these mappings.

Do we still need Retrieval Augmented Generation?

Surprisingly, I would say “yes”.

The issues with the claim feature extraction and the poorer performance on the longer document, indicate that prompt length does make a difference even for long-context models. Sometimes the model just gets distracted or goes off on one. Quite human like.

Also I wasn’t amazingly impressed with the prior art citations. The variability in passages cited, the irrelevance of some passages, and the lack of citation of some obvious features reduced my confidence that the models were actually finding the “best”, most representative disclosure. The “black box” nature of a large single prompt makes it difficult to work out why a model has indicated a particular mapping.

RAG, in the most basic form as some kind of vector comparison, provides improved control and explainability. You can see that the embeddings indicate “similarity” (whether this is true “semantic similarity” is an open question – but all the examples I have run show there is some form of “common sense” relevance in the rankings). So you can understand that one passage is cited because it has a high similarity. I find this helps reduce the variability and gives me more confidence in the results.

You can also get better focus from RAG approaches. If you can identify a subset of relevant passages first, it then becomes easier to ask the models to map the contents of those passages. The models are less likely to get distracted. This though comes at the cost of holistic consistency.

RAG would also allow you to use GPT4 rather than GPT4-Turbo, by reducing the context length. GPT4 is still a little better in my experience.

What might be behind the variability?

The variability in the mappings, and the features that are mapped, even in this relatively simple mechanic case, might hint at a deeper truth about patent work: maybe there is no “right” answer.

Don’t tell the engineers and scientists, but maybe law is a social technology, where what matters is: does someone else (e.g., a figure in authority) believe your arguments?

Of course, you need something that cannot be easily argued to be “incorrect”. But LLMs seem to be good enough that they don’t suggest wildly wrong or incorrect mappings. At worst, they believe something is not there and assert that confidently, whereas a human might say, “I’m not sure”.

But.

There may just be an inherent ambiguity in mapping a description of one thing to another thing. Especially, if the words are different, the product is different, the person writing it is different, the time is different, the breakfast is different. There might be several different ways of mapping something, with different correspondences having differing strengths and weaknesses, if differing areas. Why else would you need to pay clever people to argue for you?

I have seen this sometimes in trainees. If you come from a position of having completed a lot of past papers for the patent exams, but worked on few real-world cases, you are more likely to think there is a clearly “right” answer. The feature *is* disclosed, or the feature is *not* disclosed. Binary fact. Bosh.

However, do lots of real-world cases and you often think the exams are trying to trick you. “What, there is a clearly defined feature that is clearly different?” 80-90% of cases often have at least one feature that is borderline disclosed – it is there if you interpret all these things this way, but it isn’t there if you take this interpretation. Real-life is more like the UK P6 exam. You need to pick a side and commit to it, but have emergency plans B-H if plan A fails. Most of the time for a Rule 161 EPC communication, you recommend just arguing your side on the interpretation. The Examiner 90% of the time won’t budge, but that doesn’t say that what you say is wrong, or that a court or another jurisdiction will always agree with the Examiner.

This offers up the interesting possibility that LLMs might be better at patent exams than the exercise above…

Model Comparison

I was impressed at Claude 3 Opus. While I think GPT4-Turbo still has the edge, especially at half the price, Claude 3 Opus gave it a run for it’s money. There wasn’t a big difference in quality.

Claude 3 Opus also had some properties that stood out over GPT4-Turbo:

It seemed more reliable on repeated runs. There was less variability between runs.
It has nearly double the token context length. You could stick in all the prior art documents cited on a case.

Interestingly both Claude 3 and GPT4-Turbo tended to fall down in similar ways. They would both miss pertinent features, or sometimes get distracted in long prompts.

Based on these experiments, I’d definitely look at setting up my systems to modularly use LLMs, so I could evaluate both GPT4-Turbo and Claude 3.

Setting up billing and API access for Anthropic was also super easy, OpenAI-level. I have also tried to access Azure and Google models. They are horrendously and needlessly complicated. Life is too short.

Further Work

Vision

I didn’t look at the vision capabilities in this test. But both GPT4-Turbo and Claude 3 Opus offer vision capabilities (using a Vision Transformer to tokenise the image). One issue is that GPT4-Turbo doesn’t offer vision with long context – it’s still limited to a small context prompt (or it was last time I looked at the vision API). The vision API also has strong “alpha” vibes that I’d like to settle down.

But because you are all cool, here’s a sneak peak of GPT4-Turbo working just with the claim 1 text and Figure 1:

Claim Feature	Figure 1 (D1)	Reference Numeral in D1	Match (Yes/No)	Notes
Hair care appliance	Hair care appliance (likely a hair dryer)	–	Yes	The figure depicts a hair care appliance.
Body having an outer wall	Visible outer wall	1	Yes	The body of the appliance with an outer wall is clearly shown.
Duct extending within the outer wall	Duct present	4	Yes	There is a duct extending along the body within the outer wall.
Interior passage for receiving fluid flow	Space around the duct	–	Yes	There appears to be an interior passage for airflow around the duct.
Primary fluid outlet	Outlet for emitting fluid flow	7, 9, 13	Yes	The end of the appliance acts as the fluid outlet.
Outlet defined by the duct and an inner wall	Defined by duct and inner wall	13, 14	Yes	The primary fluid outlet seems to be defined by the duct and the inner wall.
At least one spacer between the inner wall and the duct	Presence of spacer(s)	?	No	It is unclear if spacers are present as they are not clearly depicted or labeled.

Pretty good!

A very similar analysis to the text, just from the image.

It’s definitely worth looking at integrating vision and text models. But how to do so is not obvious, especially how to efficient combine vision and long context input (there are some engineering challenges to getting the figures from a PDF involving finding the TIFFs or chopping pages into JPEGs that are boring and fiddly but essential).

Agent Personalities

We used fairly simple prompts in our example.

But we also commented on how often the law was a social language game.

Does your analysis of a claim differ if you are an examiner versus an attorney? Or if you are a judge versus an inventor? Or a patent manager versus a CEO?

It’s an open question. My first thought is: “yes, of course it does”. Which suggests that there may be mileage in performing our analysis from different perspectives and then integrating the results. With LLMs this is often as easy as stating in the user or system prompt – “YOU ARE A PATENT EXAMINER” – this nudges the context in a particular direction. It would be interesting to see whether that makes a material difference to the mapping output.

Whole File Wrapper Analysis

In our analysis with two prior art documents, we had 10,000 tokens. These were short prior art documents and we saw there was some degradation with the longer document. But we are still only 5-10% of the available prompt context.

It is technically possible to stick in all the citations from the search report (Xs, Ys, As) and go “ANALYSE!”. Whether you’d get anything useful or trustworthy is still an open question based on the present experiments. You could also get the text from the EPO prosecution ZIP or from the US File Wrapper.

I’d imagine this is where the commercial providers will go first as it’s the easiest to implement. The work is mainly in the infrastructure of getting the PDFs, extracting the text from the PDFs, then feeding into a prompt. A team of developers at a Document Management company could build this in a week or so (I can do it in that timespan and I’m a self-taught coder). It would cost though – on my calculations around £10-15 on the API per query, so 10x+ that on charges to customers. If your query is rubbish (which is often is for the first 10 or so attempts), you’ve spent £10-15 on nothing. This is less of a no-brainer than 15p.

Looking at the results here, and from reading accounts on the web, I’d say there is a large risk of confusion in a whole file wrapper prompt, or “all the prior arts”. What happens when you have a claim 1 at the start, then 10 other claim 1s?

Most long-context models are tested using a rather hacky “needle in a haystack” metric. This involves inserting some text (often incongruous, inserted at random; machine learning engineers and proper scientists or linguistics weep now) and seeing whether the query spots it and reports accordingly. GPT4-Turbo and Claude 3 Opus seem to pass this test. But finding something is an easier task than reasoning over large text portions (it just involves configuring the attention to find it over the whole input space, which is easy-ish; “reasoning” requires attention computations over multiple separated portions).

So I predict you’ll see a lot of expensive “solutions” from those that already manage data but these may be ineffective unless you are clever. They would maybe work for simple questions, like “where is a spacer between a duct and an inner wall possibly described?” but it would be difficult to trust the output without checking or know what exactly the black box was doing. I still feel RAG offers the better solution from an explanability perspective. Maybe there is a way to lever the strengths of both?

“Harder” Technology

Actually my experience is that there is not a big drop off with perceived human difficulty of subject matter.

My experiments for hardcore A/V coding, cryptography, gene editing all show a similar performance to the mechanical example above – not perfect, but also not completely wrong. This is surprising to us, because we are used to seeing a human degradation in performance. But it turns out words are words, train yourself to spin magic in them, and one area of words is just as easy as another area of words.

What is a patent? Asking Again in the Age of Machine Learning

Large Language Models (LLMs) and other Machine Learning (ML) approaches have made huge progress over the last 5-6 years. They are leading to existential questioning in professions that pride themselves on a mastery of language. This includes the field of patent law.

When new technologies arrive, they also allow us a different perspective. Let’s look.

What is a patent?

At its heart, a patent is a description of a thing or a process.

It is made up primarily of two portions:

claims – these define the scope of legal protection.
description and figures – these provide the detailed background that supports and explains the features of the claims.

Claims

These are a set of numbered paragraphs. Each claim is a single sentence. A claimset is typically arranged in a hierarchy:

independent claims
- These claims stand alone and do not refer to other claims.
- They represent the broadest scope of protection.
- They come in different types representing different types of protection. These relate to different infringing acts.
dependent claims
- These claims refer to one or more other claims.
- They ultimately depend on one of the other independent claims.
- They offer additional limitations that act as fallback positions – if an independent claim is found to lack novelty or an inventive step, a combination of that same independent claim and one or more dependent claims may be found to provide novelty and an inventive step.

Why claims?

An independent claim seeks to provide a specification of a thing or a process so that a legal authority can decide whether an act infringes upon the claim. This typically means that another thing or process is deemed to fall within the specification of the thing or process in the claim.

Patents arose from legal decrees on monopolies. They started to become a legal concept in the 15th and 16th centuries. At first, the legal authority was a monarch or guild. So you can think of them as an attempt 500-odd years ago to describe a thing or process for some form of human negotiation.

A key point is that claims are inherently linguistic. The specification of a thing or a process is provided in a written form, in whatever language is used by the patent jurisdiction in question. So we are using words to specify a thing or a process in a way that allows for comparison with other things or processes.

Normally we want the specification to be as broad as possible – to cover as many different things or processes as possible so as to maximise a monopoly. But there is a tension with the requirements that a claim be novel and inventive (non-obvious). There is a dialectic process (examination) that refines the language. I want a monopoly for “a thing” (“1. A thing.”) but there are pre-existing “things” that are a problem for novelty.

So claims are not only compared with other things and processes when determining infringement, there are also compared with things and processes that were somehow available to the public prior to the filing of a patent application containing the claims.

Description and Figures

In a patent application there is also a written description and normally one or more figures. These are “extras” that help understanding and building up a context for any comparison of the claims.

If we are examining claims for novelty and inventive step, we are often comparing them with the description and figures of existing patent publications. This is because claims are typically more abstract than the written description, and the written description contains a lot more information. We are using the principle that the specific anticipates the general.

Figures are traditionally line diagrams. They started as engineering drawings and since extended to more abstract diagrams, like flowcharts for processes and system diagrams for complicated information technology equipment.

How do computers “see” claims?

A Very Brief History of Patent Information

If we want to help ourselves compare claims, either for infringement or examination, it would be good to automate some of the process. Computers are a good tool for this job.

Patent applications used to be handwritten (as were all documents). If copies were to be made, these would also be handwritten.

Later, they were printed using mechanical printing presses. The process for this used to be the arrangement of the letters and characters in a frame to form pages of text, which were then inked and pressed onto paper. Illustrations were typically originally hand-drawn, and then reproduced using etchings or lithography.

As typewriters became common in the 20th century, patent specifications were typed from handwritten versions or as a patent attorney dictated. When I started in the profession in 2005, there were still “secretaries” that typed up letters and patent specifications.

Computers came rather late to the patent profession. It was only in the 1990s they started entering into the office and it was only in the 21st century that word processors finally replaced physical type and paper.

We still refer to “patent publications” and there is a well-trodden legal process for publication. This was because it used to take a lot of work to publish a patent specification. This seems strange in an age when anyone can publish anything in seconds at the click of a button.

From Physical to Digital

Computers are actually closer to their analogue cousins than we normally realise.

At a basic level, a text document of a set of patent claims comprises a sequence of character encodings. Each character encoding is a sequence of bits (values of 0 and 1). A character is selected from a set that includes lower case letters, upper case letters, and numbers. Normally there is a big dictionary of numbers associated with each character. You can think of a character as anything that is either printed or controls the printing. In the past, characters would be printed by selecting a particular block with a carving or engraving of the two-dimensional visual pattern that represents the character. If you imagine a box of blocks, where each block is numbered, that’s pretty much how character encoding works in a computer.

For example, the patent claim – “1. A thing.” is 049 046 032 065 032 116 104 105 110 103 046 in a decimal representation of the ASCII encoding. This can then be converted into its binary equivalent, e.g. 00110001 00101110 00100000 01000001 00100000 01110100 01101000 01101001 01101110 01100111 00101110. In an actual character sequence, there is typically no delimiting character (“space” is still just a character), so what you have is 0011000100101110001000000100000100100000011101000110100001101001011011100110011100101110. What bits relate to which character is determined based on fixed-length partitioning.

Another hangover from mechanical printing and typing is that many of the control and spacing characters are digital versions old mechanical commands. For example, “carriage return” doesn’t really make sense inside a computer, a computer doesn’t have a carriage. However, a typewriter has a carriage that pings forwards and backwards. Similarly, the “tab” character is a short cut for those on typewriters having to type tables. Any actual text thus contains not only the references to the letters used to form the words, but also the control characters that dictate the whitespace and file structure.

A sequence of character encodings is typically referred to as a “string” (from the mental image of beads on a string). This may be stored or transmitted. Word processors store character encodings in a more complex digital wrapper. Microsoft Word rather silently shifted a decade ago from a proprietary wrapper to a more open extended mark-up (XML) format (which is why you have all those different options for saving Office files). A modern Word file is actually a zip file of XML files.

Things get more confusing when we consider the digital replacement for physical prints – PDF files. PDF files are different beasts from word processing files. They are concerned with defining the layout of elements within a displayed document. While both word processing documents and PDF files store strings of text somewhere underneath the wrapping, the wrapping is quite different.

What does this all mean for my claims?

It means that much of the linguistic structure we perceive in a written patent claim exists in our heads rather than in the digital medium.

The digital medium just stores sequences of character encodings. A digital representation of a patent claim does not even contain a machine representation of “words”.

This still confuses many people. They assume that “words” and even sometimes the semantic meaning exist “somewhere” in the computer. They assume that the computer has a concept of “words” and so can compute with “words”. This was false…until a few years ago.

Comparing claims using computers

Traditional Patent Searching

Patent searching can be thought of as a way of comparing a patent claim with a body of prior publication documents. You can see the limitations of traditional computer representations of text when you consider patent searching.

Most digital patent searching, at least that developed prior to 2020ish, is based on key word matching. This works because it does not need the computer to understand language. All it consists of is character sequence matching.

For example, if you are looking for a “thing”, you type in “thing”. This gets converted into a binary sequence of bits. The computer then searches through portions of encoded text looking for a matching binary sequence of bits. It’s a simple seek exercise. It’s also slow and fragile – “entity” or “widget” can pretty much have the same meaning but will not be located.

Now there are some tricks to speed up keyword matching on large documents. You can do a simple form of tokenisation by splitting character sequences on whitespace characters (e.g., a defined list of character encodings that define spaces, full stops, or line returns). These represent words 80-90% of the time but there are lots of issues (compare splitting on ” ” and “.” for “A thing.” and “This is 3.3 ml.”). The resulting character sequences following the split can then be counted. This is called “indexing” the text. This then has the power of reducing the text to a “bag of words” – the “index”. It turns out that lots of words are repeated used (e.g., “it”, “the”, “a” etc.). The bag of words, represented as a set of unique character sequences, thus has much fewer entries that the complete text. You can also ignore words that don’t help (called “stopwords”, they are normally chosen to exclude “a”, “the”, “there” and other high-frequency words). The “index” can thus be much more quickly searched for character sequence matches. (This ignores most of the very clever real world optimisations for keyword searching in large databases but is roughly still how things work so stay with me.)

Now, key word searching is only a rough proxy for a claim comparison.

If you try to search the complete character sequence of the claim against all the patents every published, it is very likely you will not find a match. This is because the longer the sequence of characters, the more unique it is. You would only find a match in Borge’s library. The Google PageRank claim is around 600 characters. You would need to find a string with 600 characters arranged identically. And you would not match against semantically identical descriptions in prior publications that just used a different punctuation character encoding somewhere amongst those 600 characters (don’t get me started on hyphens).

Multiple term key word searching typically involves picking multiple key words from the claim we wish to compare and doing a big AND query, looking for all those words to have matches with a body of text. Even more complex approaches such as “within 5” typically just perform a normal character match then look for another match in a subset of character encodings either side of the character match.

How Patent Attorneys Compare Claims

Patent attorneys learn their skill through repeatedly working with patents over many years, typically decades. It’s a rather unique and niche skill. But often it’s one that cannot be easily explained or formalised.

That’s why it’s always a useful exercise to image explaining what you do to a lay person. Your gran or a five-year old.

When I was first training as a patent attorney, coming from a science and engineering background, I did think there was a “right” way of comparing patent claims and that it was just a matter of learning this and applying it. For the law, you quickly realise that this isn’t how things work. Training typically consisted of working with a skilled qualified attorney and watching how they did things. And then seeking to rationalise those things into a general scheme. It’s much more of a dark art. After working with many different attorneys, you realise there is lots of stylistic variation. You realise the courts often have an intuitive feel for what is right, and this is used to guide a rationalised logic process within the bounds of previous attempts. The rationalised logic is what you end up with (the court report), while the intuitive feeling often hides in plain sight.

Anyway, claim comparison is typically split into the following process:

Construe the claim
Split the claim into features
Match features
Look at the number of matched features

If all the features match in a way that is agreed by everyone then the claim covers an infringement or the claim is anticipated by prior art.

Construe the claim

“Construing” a claim is shorthand for interpreting the terms within the claim. Typically, it concentrates on areas of the claim that may be unclear, or are open to different interpretations. For example, a claim could have a typo or error, or a term might have multiple meanings that cover different groups of things.

Construing the claim is typically performed early on as it allows multiple parties to have a consistent interpretation of the text. It is thus needed before any matching is performed. It is often presented as an exercise that is “independent” of the later stages of the comparison. However, in practice, construction is performed with an eye on the comparison – if the infringement or prior art revolves around whether a particular feature is present (e.g., does it have a “wheel”?) then the terms that describe that feature have greater weight when construing (e.g., what is a “wheel”?).

Claim construction is something that is hard to translate to an automated analysis. It involves having parties to a disagreement agree on a set of premises or context. It thus naturally involves mental models within the minds of multiple groups of people, people that have a vested interest in an interpretation one way or another.

Where there is disagreement, the description and figures are typically used as an information source for resolving error and ambiguity. For example, if the description and figures clearly state that “tracked propulsion” is “not a wheel”, then it would be hard for a party to argue that “wheel” covers “tracked propulsion”. Similarly, if the claim refers to a “winjet” and the description consistently describes a “widget”, then it seems clear “winjet” is a typo and what was meant was “widget”.

Claim construction can also be seen as making the implicit, explicit. Certain terms in a claim may be deemed to have a minimum number of properties or attributes. These may be based on the “common general knowledge” as represented by sources such as textbooks or dictionaries. These can be taken as a “base line” that are then modified by any explicit specification in the claim or description and figures. Again, if the parties agree that both objects of comparison have an X, there is little reason to go into this level of detail. It is mainly terms about which the comparison turns that undergo this analysis. These terms are typically those where there is the greatest difference between the parties and the strongest arguments. One of the roles of the courts or the patent examination bodies is to shape the argument so that points of agreement can be quickly be admitted, and the differing points number a reasonable amount. (If there are lots of differences, and many of these, on the face of it, are supported, it is difficult to bring a case or find agreement within the authority; if there are no differences that are contested, the case is typically easy to bring to summary judgement.)

When construing the claim, prior decisions of the courts can also be brought to bear. If a higher court rules that using the phrase “X” has a particular interpretation, this can be applied in the present case.

Split the Claim into Features

What are claim “features”?

Here we can go back to our original split between “things” and “processes”.

“Things” are deemed to have a static instantiation (whether physical or digital). Things are deemed to be composed of other things: systems have different system components, physical mechanic devices have different parts, and chemical compositions have different molecular and/or atomic constituents.

“Processes” are a set of events that unfold in time, typically sequentially. They are often methods that involve a set of steps. Each step may be seen as a different action and/or configured state of things and matter.

When we are looking at claim “features”, we are looking to segment the text of the claim into sub-portions that we can consider individually. Psychologically, we are looking to “chunk” the content of the claim. We chunk because our working memories are limited. When comparing we need to hold one chunk in the working memory, and compare it with one or more other chunks. Our brains can hold a sequence of about three or four “chunks” in working memory at any one time, or hold two items for comparison. We decompose the claim into features as a way to work out if a match exists – we can say the whole matches if each of the parts match.

Now, we only need to break a claim into features because it is complex. If the claim was “1. A bicycle.”, we could likely hold the whole claim in our working memories and compare it with other entities. In this case, we might need to use the previous step of claim construction to determine what the minimum properties of a “bicycle” were. (Two wheels? Is a tricycle, a motorcycle, or a unicycle a “bicycle”?). Here we see that the definition of claim features can be a recursive process, where the depth of recursion into both explicit and implicit features depends on the level of disagreement between parties (and likelihood of collective agreement between different parties within an authority, such as between a primary examiner and senior examiner). Recursion can also be used to “zoom in” on a particular feature comparison, while then concluding on a match at a “zoomed out” level of the feature (e.g., this does match a bicycle because X is a first wheel and Y is a second wheel).

A Short Aside on Segmentation

Claim feature extraction is a form of semantic segmentation.

Segmentation in images made a huge leap in 2023 with the launch of Meta’s Segment Anything model. In images, segmentation is often an act of determining a context-dependent pixel boundary in two-dimensions.

For the sequence of characters that form a patent claim, we have a one-dimensional problem. We need to determine the “feature” breakpoints in the sequence of characters.

It turns out patent attorneys provide clues as to this semantic segmentation via the use of whitespace. Patent attorneys will often add whitespace such that the claim is partitioned into pseudo-features by way of the two-dimensional lay out.

In the example above we see that commas, semi-colons, and new lines break the patent claim into five natural “features”.

It turns out there are a number of problems with the reliability of automated segmentation based on whitespace:

The text is often transformed when it is loaded into different systems, meaning that original white space may be lost or omitted. Fairly often new lines are stripped out, or stripped out then manually replaced.
There are many different encodings of many different forms of whitespace – they are multiple versions of the new line character for example.
Real-world patent claims often have a multi-tier nested structure that requires more advanced recursive segmentation.

Things or Events as Features

Those familiar with patent law will realise that when someone refers to “claim features”, they are normally referring to portions of text within the claim that are indicated as separate sections by the author’s use of whitespace. Claim charts are tables that often have 5-10 rows, where each row is a feature that is a different portion of the claim text determined in this way. Claim charts normally are structured to fill up one page of A4, so we can easily get an idea of the feature matches.

However, we can ask a deeper question – what are those different whitespace separately portions of the claim actually representing?

Or put another way – what do we mean by semantic segmentation of the claim text?

Let’s have a look at the simple WIPO claim example above. Using new lines we can split that into the following features:

[a]n apparatus (, comprising:)
a plurality of printed pages;
a binding configured to hold the printed pages together;
a cover attached to the binding,
characterized in that, the cover is detachable from the binding.

Looking closely, we see that actually those text portions are centred on different things. The claim defines an “apparatus“, that forms the top line. This apparatus has a number of components: pages, a binding, and a cover. We see that the middle three segments are based around definitions of each of these components. The last section then defines a characteristic of the apparatus in terms of the cover and binding components.

So for a “thing” claim, we see that our semantic focus for segmentation is “sub-things”. “Things” are made of interconnected “sub-things” and this pattern repeats recursively. We can look at different “things” or “sub-things” in isolation of its connections to focus on it’s individual properties. Things at each level are defined by the interconnection and inter-configuration of sub-things at a lower level.

Now in English grammar, we have a term for “things”: nouns. Nouns and noun-phrases are the terms we use to classify the location of “things” in text. So when we semantically segment a claim, we are doing this based on the noun content of the claim.

Method claims are slightly different. We no longer have a subdivision by static structural “things”. Rather we have a partition by time, or more precisely different sequences of actions within time. Take another claim example from WIPO:

If you were to ask a patent attorney to split that claim into “features”, they would likely choose each step – i.e. each clause starting on a new line and ending with a semi-colon and new line:

[a] process for producing fried rice (, comprising the steps of:)
turning the heat source on;
cooking rice in water over the heat source for a predetermined period;
placing a predetermined amount of oil in a pan;
cooking other ingredients and seasoning in the pan over the heat source;
placing the cooked rice in the pan; (and)
stirring consistently the rice and the other ingredients for a predetermined length of time over the heat source.

These steps are different actions in time, where time runs sequentially across the steps.

Now we can see that method claims also share certain aspects of the “thing” claims. We have several “things” that are acted on in the method, including: “fried rice”, “heat source”, “rice”, “oil”, “pan”, “ingredients”, “seasoning”, “cooked rice”, and “length of time”. We can also see that some of those “things” are actually different states of the same thing – we start with “rice”, which then becomes “cooked rice”, which is output by the method as “fried rice”.

Even though a method consisting of: “turning”, “cooking”, “placing”, “cooking”, “placing”, and “stirring” would be a valid patent claim, it would likely lack novelty. For example, the quite different method of cooking a chicken dinner below would fall within that method:

turning a chicken breast in flour;
cooking a set of potatoes in water;
placing the chicken breast and cooked potatoes on baking trays;
cooking the chicken breast and potatoes in the oven;
placing the cooked chicken breast and potatoes on a plate; and
stirring gravy to pour over the plate.

So we see that it is the things that are involved in each step that define the (sub) features of the step.

Match Features

Once we have identified features in the claim the next step is comparing each of those features. For infringement, we are comparing with a possibly infringing thing or process. For examination, we are comparing with a prior publication.

Splitting a claim into features lessens the cognitive load of the comparison. It also allows agreement on uncontentious aspects, focusing effort on key points of disagreement. Much of the time, there is only really one feature that may or may not differ. Often one missing feature is all you need to avoid infringement and/or argue for an inventive step.

Now, you might say that matching is easy, just like spot the difference.

Going back to an image analogy, visual features may be segmented portions of a two-dimensional extent. In spot the difference we compare two images that are scaled to the same dimensions. We are then looking for some form of visual equivalence in the pixel patterns in different portions of the image.

Words are harder though. We are dealing with at least one level of abstraction from physical reality. We are looking for a socially agreed correspondence between two sets of words.

The facts of the case determine what features will be in contention and which may be more easily matched. Different features will be relevant for different comparisons. Inventive step considerations still involve a feature matching exercise, but they involve different feature matches in different portions of prior art.

What does it mean for a feature to match?

We have our claim feature, which is set out in a portion of the claim text (our segmented portion).

Our first challenge is to identify what we are comparing with the claim. These can sometimes be fuzzy-edged items that need to be human-defined. Sometimes they are harder-edged and more unanimously agreed upon as “things” to compare. For infringement, the comparison may be based on a written description of a defined product, or a documented procedure. For examination, it is often a prior-published patent application.

Our second challenge is to find something in the comparison item that is relevant to the particular feature. There may be multiple candidates for a match. At an early stage this might be a general component of a product or thing, or a particular component of a particular embodiment of a patent application as set out in one or more figures.

Once we have something to compare, and have identified a rough candidate correspondence, the detailed analysis of the match then depends on whether we are looking at infringement or examination for novelty.

For infringement, we have a “match” if the language of the claim feature can be said to describe a corresponding feature in the potentially infringing product or process. At this stage we can ignore the nuances of the infringement type (e.g., use vs sale), as this normally only follows if we have a clear infringing product or process. To be more precise, we have a “match” if a legal authority agrees that the language of the claim feature covers a corresponding feature in the potentially infringing product or process. So there is also a social aspect.

For the examination of novelty, we have a “match” if a portion of a written description can be said to describe all the aspects of the claim feature. As claim features are typically at a higher level of abstraction, this can also be thought of as: would an abstracted version of the written description produce a summary that is identical to the claim feature?

A match is not necessarily boolean; if there is a particular point of interpretation or ambiguity there may be numerous options to decide. A decision is made based on reason (or reasons), sometimes with an appeal to previous cases (case law) or analogy or even public policy. If you asked 100 people, you might get X deciding one way and 100-X deciding the other.

Look at the number of matched features

This is normally the easy part. If we have iterated through our “matching” for each identified claim feature, and the set of claim features exhaustively cover all of the claim text, then we simply total up the number of deemed “matches”.

If all the features match, we have a matching product or process for infringement, or our claim is anticipated by the prior art.

If any feature does not match, then we do not have infringement (ignoring for now legal “in-filling” possibilities) and our claim has novelty, with the non-matching features being the “novel” features of the claim.

Any non-matching features may then be subject to a further analysis on the grounds of inventive step. If the non-matching feature is clearly found in another document, and a skilled person would seek out that other document and combine teachings with no effort, then the non-matching feature is said to lack an inventive step.

Can we automate?

Given the above, we can ask the valid question: can we automate the process?

The answer used to be “no”. The best we could do was to compare strings, and we’ve seen above that any different in surface form of the string (including synonyms or differences in spelling or white space) would throw out an analysis of even single words.

Fuzzy matching

Before 2010, natural language processing (NLP) engineers tried tinkering with a number of approaches to match words. This normally fell within the area of “fuzzy matching”. An approach used since the 60s is calculating the Levenshtein distance, a measure of the minimum number of single-character edits that change one word into another. This could catch small typos but still needed a rough string match. It failed with synonyms and irregular verbs.

word2vec

In the early 2010s though, things began to change. Techniques such as word2vec were developed. This allowed researches to replace a string version of a word with a list of floating point numbers. These numbers represented latent properties of use of the string in a large corpus of documents. Or put another way, we could compare words using numbers.

Early word vector approaches offered the possibility of comparing words with the same meaning but different string patterns. In particular, we found that the vectors representing the words had some cool geometric properties – as we moved within the high-dimensional vector space we saw natural transitions of meaning. Words with similar meanings (i.e., that were used in similar ways in large corpora) had vectors that were nearby in vector space.

So words such as “server” and “computer” might be matched in a claim based on their word2vec vectors despite there being no string match. I remember playing with this using the gensim library.

Transformers

We didn’t know it at the time, but word vectors were the beginning not the end of NLP magic.

In the early days, word embeddings allowed us to create numerical representations of word tokens for input into more complex neural network architectures. At first you could generate embeddings for a dictionary of 100,000 words, giving you a matrix of vector-size x 100k and you could then select your inputs based on a classic whitespace tokenisation of the text.

Quickly, people realised that actually you didn’t need the word2vec as a separate process, but you could learn that matrix of embeddings as part of your neural architecture. Sequence to sequence models were built on top of recurrent neural network architectures. Then in 2017, Attention is All You Need came along, which turbo-charged the transformer revolution. Fairly quickly in 2018 we arrived at BERT, which was the encoder side of AIAYN and was built into many NLP pipelines as a magical classifying workhorse, and GPT, the foundation model that became the infamous ChatGPT. In 2023, we saw the public release of GPT4, which took language models from an interesting toy for making you sound like a pirate to possible production language computer. In 2024, we are still struggling to get anywhere near to the abilities of GPT4.

With large language models like BERT and GPT, you get embeddings of any text “for free” – it’s a first stage of the model. We can thus now embed longer strings of text and convert it into a vector representation. These vectors can then be compared using mathematics. STEM – 1 ; humanities – 0 (just don’t take a close look at society).

Settlers in a Brave New World

The power of word embeddings and large language models now open up whole new avenues of “legal word processing” that were previously unimaginable. We’ve touched on using retrieval augmented generation here and here. We can apply the same approaches to patent documents and claims.

We now have a form of computer that takes a text input and produces a text output. We don’t quite know how it works but it seems to pass the Turing Test, while reasoning in a slightly stunted and alien manner.

This then provides the opportunity to automate the process described above, to arrive at automated infringement and novelty opinions. At scale. While we sleep. For pennies.

I’m excited.

RAG for Legal Documents

I’ve been working for a while with Retrieval Augmented Generation (RAG) systems. This ranges from simple vectorisation of text to more complex libraries such as LangChain or LlamaIndex. This post explores some of the nuances of applying RAG to legal documents, with tips for powerful production systems.

I don’t particularly like the acronym “RAG”; I’m not keen on acronyms, jargon, or buzzwords. But I can’t control language, and I don’t have a better suggestion, so hey-ho.

Why RAG?

I am already seeing many people use large language models (LLMs) like ChatGPT to answer legal questions.

Normally the answers are wrong in some ways. Sometimes they are wrong in dangerous ways. However, the answers are not completely wrong; they are wrong in a “nearly there” or “halfway there” kind of way.

This matches what I have read in other fields and professions, such as medicine. The 80% of an answer is often there. 15% of the content is definitely wrong. 5% is dangerously wrong.

Often the low hanging fruit is found. But the law is applied in the wrong way based on the probabilities of Internet use, or important laws are missed.

RAG systems offer a way to improve the initial answers of LLMs by getting them to work with sources of information. We explored how this could work in this blog post. The present post develops that work with some more advanced techniques.

Naive Chunking

The main problem with naive RAG implementations is that it is independent of document structure. Early transformer architectures like BERT were limited to around 512 tokens, meaning documents were “chunked” in batches of around 400 words or less (a token is a word or word part). Normally, text was extracted naively – just dumping any string content from larger structures into a single string, then chunking that single string based on token counts. In practice this makes for poor retrieval, as semantically continuous sections are broken apart mid-section & with disregard for meaning.

Document as a Tree

Now, a more sophisticated view of a document is as a tree structure. A tree is a particular instance of a graph. The tree has parent and child nodes, separated by edges. There are well known methods for building and navigating trees.

Now most electronic documents are in a tree form already. The webpage you are reading this on is a tree in the form of a complex Document Object Model (DOM). XML and JSON data structures are often parsed into nested dictionaries that can be represented as trees. Word documents are stored as XML under the hood. PDFs – well…

However, most electronic documents are designed for machines to parse and extract data not semantic meaning. For example, often they are arranged like (and built from) complex databases. Only a subset of information is semantically useful to human beings, which is why we need to render things as pretty webpages that hide much of the complexity.

RAG is concerned with the semantic content of documents rather than the syntactic contents. In fact, sticking XML or JSON into a RAG system as text tends to throw it off, as matches concentrate in similarities with the encoded syntax rather than the encoded semantic content. RAG encoders just need the semantic content in a manner similar to a human viewer. Also, the raw electronic data structure, when represented as a string, is very verbose, so it costs a lot in time and resources to encode.

Legal Documents

Legal documents are often long and complex. That’s why people pay lawyers a lot of money. But lawyers are still human, and humans can’t make sense of large streams of dense text (at least without an entertaining narrative or decent characterisation). Hence, lawyers use a variety of tools to help humans parse the documents. These include “sections” (with an arbitrary level of nesting) and “tables of contents”. These are all forms of tree structure. Normally, we represent them linearly with indents to represent levels in the tree hierarchy.

But we can also visualise the same data structure as more of a tree-like shape:

As humans, we know how to use these tree structures to quickly arrive at the section we need, by iterating over the tree. Often we approximate something similar to a breadth-first search – we look at the first level of topics, determine if one of them is relevant to our query, then look at the next set of titles within the relevant topic, repeating until we arrive at a relevant portion of text.

The tree structure of legal documents is also used when citing sources. In legal argument, we cite either the absolute reference assigned to the leaf nodes (e.g., “Section 4, Patents Act 1977”) or the trail down the tree (“Guidelines for Examination, A-II-1.2.1”).

In a particular area of law, like patent law, we often have a small number of “documents” but a large number of text chunks. In the area of case law, we have a large number of documents, each with around 100-200 paragraphs, often split via headings or subheadings. In the area of patent law, we have a very large number of patent publications (250 million odd), each with between 20 and 500+ paragraphs, split roughly into a number of canonical headings, some with subsections and most semantically chunked by embodiment.

RAG on Trees

Based on the above we have an intuition that using the tree structure of legal documents can provide for more meaningful vector search and source citation. How might we begin to build a useful search system?

Now LlamaIndex does have a tree index but this seems separate from their vector index. We really want to combine the power of freeform vector search, with the guidance of the tree structure for relevance and easy citation.

1. Parsing the Document

This is often a challenge in itself. Each document type needs a bespoke solution. Let’s take the EPO Guidelines for Examination as an example. We have the option of a PDF download or a series of web-pages.

a) PDF

Off-the-shelf PDF parsers are poor at extracting the tree structure – you get text chunks that split up some semantic sections or merge other sections.

b) Web pages

Most web-page versions of materials tend to be located across a series of web-pages, with one web-page per leaf node of the tree. However, because this is 2024, the actual HTML for the webpage is a mess of Javascript and other front-end rendering functions, leaving us scratching our heads as to where the data is actually located.

What you see above…with what you get below

2. Building the Tree

If we write our own scripts per document source to handle data ingress (e.g., based on a PDF file path or a web URL), our next challenge is to parse the initial data structure into a useful semantic tree.

This often requires simplifying and validating the data structure we start with, placing everything into a common tree model.

LlamaIndex does have the data abstractions of Documents and Nodes. This is a good start. Documents here would be the high level data source (e.g., Guidelines for Examination, Case Law Book, Statute, Rules etc.) and the Nodes would represent the different levels of the tree. We thus would need to work out how the levels of the tree are represented in the initially received data structure representing the parsed data source and convert that into Nodes and their relationships.

With a tree, the relationships are the relatively straightforward parent-child relationship. The tree is represented as a Directed Acyclic Graph (DAG – no relation), where the direction normally indicates parent to child (as per the graphs above). LlamaIndex has a relationships property built into the Node data model so we could use that. Or we could build our own simple model.

To leverage the good work of the LlamaIndex team and speed up development, we might create the Documents and Nodes ourselves, but then use the inbuilt vectorisation of those nodes. We can then build custom retrieval pipelines. However, there is merit to building our own version, in that it is typically easier to see what is going on and we are not beholden to a fast-moving library.

My experience has been that plain old cosine similarity via the dot product works pretty much near identically to more complex methods, and using OpenAI’s API or SentenceTransformer for embeddings also has similar performance. What makes a big difference to performance is the logic of retrieval and how the prompts are generated, typically in an iterative manner.

In terms of the text data stored with each node in the tree, we can start with the text of the titles as presented.

3. Querying

If we have a tree data structure representing our legal document, with the tree representing the semantic sections of the document we can begin to have some fun with query strategies.

Unfortunately, it’s all a bit “wild west” at the moment in terms of querying strategies and algorithms. There are a lot of academic papers with some useful ideas and custom solutions but none of these are ready for a production implementation or have a stable library you can drop in. They will come in time, but I’m impatient!

a) Naive Start

A naive starting point is to flatten the tree and to perform a vector search over all the nodes. This would give us results that mix different layers of the tree.

One problem with this is that titles are short and they often do not contain all the information that is needed to determine whether the nodes below are relevant. This leads to noisy results at best and just noise at worst.

b) Breadth-First Vector Search

The next natural step is to combine a normal breadth-first search with the vector search. This would involve filtering nodes by level, performing a vector search on the filtered nodes, and then picking a level to explore based on those results.

This again would suffer from the problem of the un-informative title text as discussed above.

c) Breadth-First Vector Search with Summary Embedding

One suggestion to address uninformative parent node text is to build a summary for every parent node, based on the contents of the child nodes. This can be built iteratively from the leaf nodes up.

For example, we iterate over the leaf nodes, get a lowest level set of parent nodes, iterate over those parent nodes and use a cheap LLM (like GPT3.5-turbo or a local 7B model) to summarise the text in text form at each parent. We then repeat starting with those parent nodes, until we have processed the whole tree. A human-readable text summary could be encoded with the title to get the embedding and/or may be read by a human being.

A variation on the above that uses the same algorithm generates an embedding for a parent node based on the concatenated text content of the children nodes. This can then be searched in the breadth-first manner above and should be more relevant and accurate.

The summary encoding discussed here would only need to be run once after the tree has been built.

d) Breadth-First Vector Search with Query-Summary Embedding

A further possibility to increase the accuracy of the method above, is to create custom summaries for each query. This custom summary may be used together with, or instead of, the generic summary above. This is based on the intuition that in a large section only some bits may be relevant, so a generic summary might not capture all the ways the section is relevant.

One problem with this approach is it would be more expensive. However, as cheaper or local models tend to be good at summarising, and embeddings cost peanuts, the cost may not be prohibitive (e.g., 1-50p per query via an API or free but slower if we use local models).

This approach would implement a Heath-Robinson (Rube-Goldberg for our US cousins) version of attention, but in a manner that may be more “explainable”. A user is provided with feedback in the form of the custom summary that represents what portions are deemed relevant to place in the summary.

The custom summary may also be used in the actual generation prompt. Normally, RAG systems dump the text of the most relevant nodes in a prompt together with a question to get an answer. Custom summaries of sections prior to specific text from the section might improve question-answering by setting up the context for the probabilistic retrieval.

e) Chain-of-thought or Iterative Querying

Another approach that could be used modularly with the above approaches is an iterative refinement of the nodes.

For example, if we perform a breadth-first search and select a particular section based on a generic and/or custom summary, we could combine the child node text (with a view on LLM context limits) and apply some kind of scoring based on whether the LLM thought the combined text was relevant. Now my experience is that LLMs aren’t very good at consistent scoring, especially over successive requests, but the higher power models are not too bad at ranking sections in order of relevance or determining reasons why a bit of text may or may not be relevant. This could then be built into a scoring system for selection of relevant portions of text or lower nodes. However, it does get expensive quickly.

4. Linking Between Trees

With the example of the EPO Guidelines above, we also see the possibility of linking between trees. Some of these links we might be able to parse from the extracted data. For example, we might be able to extract the links to associated articles and rules as the hyperlinks are within the <div> with the text that forms the link node.

We would need to store some metadata with each node representing a parsed form of the article or rule (e.g., maybe a flattened list of the string portions of a particular article or rule node and an integer version of the main part and/or subsequent part).

In PDFs and other unstructured text, we can maybe setup a “simple” regex rule to detect citations of articles and rules:

Another possibility is to also extract cases cited by each node, storing the case as string, number, and hyperlink.

The Manual of Patent Practice has links to sections, cases, and paragraphs of cases within the text. These could be parsed by regex, HTML parsing, and/or LLM to generate metadata on links.

Vector Section with Legal Filtering

Once extracted, links between portions of statute, cases, and guidance could be used in the retrieval logic prior to prompt generation.

For example, a flattened search over all nodes in a set of guidance could be used to extract the linked articles across the top N results. This could then be combined with a further flattened search over the articles themselves. The articles could then be scored based on both searches. Top scoring articles could then be used to refine vector search results and/or included in an question-answering prompt.

Improving Access to the Law

In my experience with tests, the strength of vector search is its ability to brainlessly search without any pre-thought. The user does not need to work out what key words need to be typed, they can just dump a text description of their problem, poor spelling and all. This has huge power for increasing access to the law.

However, vector search is noisy, has weird quirks and biases, and out-of-the-box RAG is not “safe” enough to provide any coherent legal advice. But improvement seems to be within the realm of possibility. With some work.

I tried a few examples on D1 EQE exam questions. GPT4, the best performing model provides a half-correct answer (a score of 30-40% or 0% depending on how harsh the Examiner was feeling).

Here is an example of asking GPT4 what articles of the EPC might be relevant to answer a D1 question:

Unfortunately, GPT4 missed the main bit of the answer, which involved Article 123(2) EPC (added subject matter), as this required some next-level working from the initial facts.

When using a naive RAG implementation, I got an improved answer that did highlight the applicability of Article 123(2) EPC. The top 10 retrieved portions of the Guidelines and Case Law were vaguely relevant, but they needed more work on filtering. The poor PDF and web-page parsing did not help.

This offers a possibility that with a bit of tweaking and building, we could up our 0-40% GPT4 result to a scrapped pass of 51%. If GPT4 can pass the EQE, what does that mean for legal knowledge?

(In my mind possibly good things for industry and the public, possibly painful change for the patent profession.)

Future Work

The systems are not there yet. The out-of-the-box-in-a-hackathon-afternoon solutions are not good enough as of February 2024. But progress seems possible. We haven’t hit a technology “wall” yet with integrating the power of LLMs.

The changes will likely be as big as the Internet.

This is how I accessed the law when I started in the patent profession:

This is how we currently access the EPO Guidelines:

UK patent case law:

And UK patent legislation:

Even an implementation of the out-of-the-box approaches would be a big improvement over this.

But we haven’t yet really touched the possibilities of augmented law, when we start building systems that “reason” and process over large bodies of text. Exciting times!

Talking Legislation – Asking the Patents Act

We all are told that Large Language Models (LLMs) such as ChatGPT are prone to “hallucinations”. But did you know we can build systems that actively help to reduce or avoid this behaviour?

In this post, we’ll be looking at build a proof-of-concept legal Retrieval-Augmented Generation (RAG) system. In simple terms, it’s an LLM generative system that cites sources for its answers. We’ll look at applying it to some UK patent legislation.

(Caveat: I have again used GPT-4 to help with speeding up this blog post. The rubbish bits are its input.)

Scroll down to the bottom if you want to skip the implementation details and just look at the results.

If you just want to have a look at the code, you can find that here: https://github.com/Simibrum/talking_legislation

Introduction

The complex and often convoluted nature of legislation and legal texts makes them a challenging read for both laypeople and professionals alike. With the release of highly capable LLMs like GPT-4, more people have been using them to answer legal queries in a conversational manner. But there is a great risk attached – even capable LLMs are not immune to ‘hallucinations’ – spurious or inaccurate information.

What if we could build a system that not only converses with us but also cites its sources?

Enter Retrieval-Augmented Generation (RAG), a state-of-the-art technology that combines the best of both worlds: the text-generating capabilities of LLMs and the credibility of cited sources.

Challenges

Getting the Legislation

The first hurdle is obtaining the legislation in a format that’s both accurate and machine-readable.

Originally the official version of a particular piece of legislation was the version that was physically printed by a particular authority (such as the Queen or King’s printers). In the last 20 years, the law has mostly moved onto PDF versions of this printed legislation. While originally digital scans, most modern pieces of legislation are available as a digitally generated PDF.

PDF documents have problems though.

They are a nightmare to machine-read.
Older scanned legislation needs to be converted into text using Optical Character Recognition (OCR). This is slow and introduces errors.
Even if we have digital representations of the text within a PDF, these representations are structured for display rather than information extraction. This makes it exceedingly difficult to extract structured information that is properly ordered and labelled.

Building the RAG Architecture

Implementing a RAG system is no small feat; it involves complex machine learning models, a well-designed architecture, and considerable computational resources.

Building a Web Interface

The user experience is crucial. A web interface has to be intuitive while being capable of handling the often lengthy generative timespans that come with running complex models.

Solutions

Using XML from an Online Source

In the UK, we have the great resource: www.legislation.gov.uk.

Many lawyers use this to view up-to-date legislation. What many don’t know though is it has a hidden XML data layer that provides all the information that is rendered within the website. This is a perfect machine-readable source.

Custom XML Parser

Even though we have a good source of machine-readable information, it doesn’t mean we have the information in a useful format for our RAG system.

Most current RAG systems expect “documents” to be provided as chunks of text (“strings” – very 1984). For legislation, the text of each section makes a good “document”. The problem is that the XML does not provide a clean portion of text as you see it on-screen:

Rather, the text is split up across different XML tags with useful accompanying metadata:

To convert the XML into a useful Python data structure, we need to build a custom XML parser. This turns the retrieved XML into text objects along with their metadata, making it easier to reference and cite the legislative sources. As with any markup processing, the excellent Beautiful Soup library is our friend. The final solution requires some recursive parsing of the structure. This always makes my head hurt and requires several attempts to get it working.

Langchain for Embeddings and RAG Architecture

This mini project provided a great excuse to check out the Langchain library in Python. I’d seen many use this on Twitter to quickly spin up proof-of-concept solutions around LLMs.

At first I was skeptical. The power of langchain is it does a lot with a few lines of code, but this also means you are putting yourself in the hands of the coding gods (or community). Sometimes the abstractions are counter-productive and dangerous. However, in this case I wanted to get something up-and-running quickly for evaluation so I was happy to talk on the risks.

This is pretty bleeding edge in technology terms. I found a couple of excellent blog posts detailing how you can build a RAG system with langchain. Both are only from late August 2023!

The general outline of the system is as follows:

Configure a local data store as a cache for your generated embeddings.
Configure the model you want to use to generate the embeddings.
- OpenAI embeddings are good if you have the API setup and are okay with the few pence it costs to generate them. The benefit of OpenAI embeddings is you don’t need a GPU to run the embedding model (and so you can deploy into the cloud).
- HuggingFace embeddings that implement the sentence-transformer model are a free alternative that work just as well and are very quick on a GPU machine. They are a bit slow though for a CPU deployment.
Configure an LLM that you want to use to answer a supplied query. I used the OpenAI Chat model with GPT3.5 for this project.
Configure a vector store based on the embedding model and a set of “documents”. This also provides built-in similarity functions.
And finally, configure a Retrieval Question-and-Answer model with the initialised LLM and the vector store.

You then simply provide the Retrieval Question-and-Answer model with a query string, wait a few seconds, then receive an answer from the LLM with a set of “documents” as sources.

Web Interface

Now you can run the RAG system as a purely command-line application. But that’s a bit boring.

Instead, I now like to build web-apps for my user interfaces. This means you can easily launch later on the Internet and also take advantage of a whole range of open-source web technologies.

Many Python projects start with Flask to power a web interface. However, Flask is not great for asynchronous websites with lots of user interaction. LLM based systems have the added problem of processing times in the seconds thanks to remote API calls (e.g., to OpenAI) and/or computationally intensive neural-network forward passes.

If you need a responsive website that can cope with long asynchronous calls, the best framework for me these days is React on the frontend and FastAPI on the backend. I hadn’t used React for a while so the project was a good excuse to refresh my skills. Being more of a backend person, I found having GPT-4 on call was very helpful. (But even the best “AI” struggles with the complexity of Javascript frontends!)

I also like to use Bootstrap as a base for styling. It enables you to create great-looking user interface components with little effort.

Docker

If you have a frontend and a backend (and possibly a task queue), you need to enter the realm of Docker and Docker Compose. This helps with managing what is in effect a whole network of interacting computers. It also means you can deploy easily.

WebSockets

I asked ChatGPT for some suggestions on how to manage slow backend processes:

I’d built systems with both async functionality and task queues, so thought I might experiment with WebSockets for this proof-of-concept. As ChatGPT says:

Or a case of building a TCP-like system on-top of HTTP to overcome the disadvantages of the stateless benefits of HTTP! (I’m still scared by CORBA – useful: never.)

Anyway, the WebSockets implementation was a pretty simple setup. The React front end App sets up a WebSocket connection when the user enters a query:

And this is received by an asynchronous backend endpoint within the FastAPI implementation:

Results and Observations

Here are some examples of running queries against the proof-of-concept system. I think it works really well – especially as I’m only running the “less able” GPT3.5 model. However, there are a few failure cases and these are interesting to review.

Infringement

Here’s a question on infringement. The vector search selects the right section of the legislation and GPT3.5 does a fair job of summarising the long detail.

We can compare this with a vanilla query to GPT3.5-turbo:

And to a vanilla query using GPT4:

Inventors

Here’s an example question regarding the inventors:

Again, the vector search finds us the right section and GPT-3.5 summarises it well. You’ll see GPT3.5 also integrates pertinent details from several relevant sections. You can also click through on the cited section and be taken to the actual legislation.

Here’s vanilla GPT3.5:

Failure Case – Crown Use

Here’s an interesting failure case – we ask a question about Crown Use. Here, the vector search is biased to returning a shorter section (122) relating to the sale of forfeited items. We find that section 55 that relates to Crown Use does not even feature in the top 4 returned sections (but would possibly be number 5 given that section 56 is the fourth entry).

Interestingly, this is a case where vanilla GPT3.5 actually performs better:

WebSocket Example

If you are interested in the dynamics of the WebSockets (I know all you lawyers are), here’s the console log as we create a websocket connection and fire off a query:

And here’s the backend log:

Future Work

There are a few avenues for future improvement:

Experiment with the more expensive GPT4 model for question answering.
Extend the number of returned sources.
Add an intermediate review stage (possibly using the cheaper GPT3.5).
Add some “agent-like” behaviour – e.g. before returning an answer, use an LLM to consider whether the question is well-formed or requires further information/content from the user.
Add the Patent Rules in tandem.
Use a conventional LLM query in parallel to steer output review (e.g., an ensemble approach would maybe resolve the “Crown Use” issue above).
Add an HTML parser and implement on the European Patent Convention (EPC).

Summary

In summary, then:

Positives

It seems to work really well!
The proof-of-concept uses the “lesser” GPT3.5-turbo model but often has good results.
The cited sources add a layer of trust and verifiability.
Vector search is not perfect but is much, much better than conventional keyword search (I’m glad it’s *finally* becoming a thing).
It’s cool being able to build systems like this for yourself – you get a glimpse of the future before it arrives. I’ve worked with information retrieval systems for decades and LLMs have definitely unlocked a whole cornucopia of useful solutions.

Negatives

Despite citing sources, LLMs can still misinterpret them.
The number of returned sources is a parameter that can significantly influence the system’s output.
Current vector search algorithms tend to focus more on (fuzzy) keyword matching rather than the utility of the returned information, leaving room for further refinement.

Given I could create a capable system in a couple of days, I’m sure we’ll see this approach everywhere within a year or so. Just think what you could do with a team of engineers and developers!

(If anyone is interested in building out a system, please feel free to get in touch via LinkedIn, Twitter, or GitHub using the links above.)

Can Simulations be Patented in Europe? An Update

A Review of Oral Proceedings for G1/19

On 15 July 2020, the European Patent Office (the “EPO”) held Oral Proceedings for the pending G1/19 referral to the Enlarged Board of Appeal. Although no decision was reached by the Enlarged Board, they indicated their preliminary opinion on the referred questions and gave the Appellant and the EPO a chance to present arguments.

As a potential first, the Oral Proceedings were live-streamed due to the Covid-19 restrictions, which meant that those at home or in the office could attend. About 1600 had registered beforehand to view.

Question Under Consideration

The Enlarged Board is basically considering the question: can you patent (computer) simulations in Europe?

The questions are referred from T0489/14. This is a Board of Appeal case where the subject matter related to the simulated movement of pedestrians through a building. The initial referral was covered in this blogpost: G1/19 – Enlarged Board of Appeal to Consider the Matrix.

One of the issues thrown up by the appeal case is the status of T1227/05. The Board in T0489/14 were inclined to find that the pedestrian movement claim lacked inventive step, but felt this would conflict with T1227/05, which found that a claim to simulating noise when designing chips was patentable.

For reference for the discussion below, I will briefly repeat the referred questions, using the numbering convention of the Enlarged Board:

1. In the assessment of inventive step, can the computer-implemented simulation of a technical system or process solve a technical problem by producing a technical effect which goes beyond the simulation’s implementation on a computer, if the computer-implemented simulation is claimed as such?

2. a) If the answer to the first question is yes, what are the relevant criteria for assessing whether a computer-implemented simulation claimed as such solves a technical problem?

2. b) In particular, is it a sufficient condition that the simulation is based, at least in part, on technical principles underlying the simulated system or process?

3) What are the answers to the first and second questions if the computer-implemented simulation is claimed as part of a design process, in particular for verifying a design?

Quick Initial Answers

Below I set out some of the points that were raised during the Oral Proceedings. However, if you are looking for the quick outcomes I will indulge you.

Can you patent (computer) simulations in Europe? The answer so far appears to be “yes” – the discussion suggests that simulations are to be treated as per any other computer-implemented invention.

Referred Q1: Yes – basically as set out above.

Referred Q2a): Side-stepped by a likely finding of inadmissible.

Referred Q2b): No – something more than technical principles is required – likely a technical effect and a solution to a technical problem as per conventional computer-implemented inventions.

Referred Q3): The form of the claim does not matter.

We will need to wait for the final decision of the Enlarged Board to confirm these initial indications, and it is not impossible for their position to change after having considered the presentations in Oral Proceedings.

Amicus Curaie

Since the initial referral, 23 amicus curiae briefs have been filed. These can be read here.

Initial Enlarged Board of Appeal Opinion

On 22 June 2020, the Enlarged Board issued an initial communication that set out some of their thinking (see the first 22.06.2020 entry here). In particular, the communication set out a number of questions that the Enlarged Board felt had been raised by the referral.

Firstly, the board considered numerical simulations that have no direct link to physical reality, such as a change in or a measurement of a physical entity. They summarised two approaches as presented in the amicus curaie:

A technical system or process is simulated in a manner which sufficiently corresponds to the behaviour of the real system or process – in this case “virtual” or “potential” technical effects may be considered like “real” technical effects when applying COMVIK.
The numerical result of a simulation serves a technical purpose. This matches the decision in T1227/05, where the subject matter was patentable as it was directed to a tool for circuit design.

It was mentioned that technical considerations and/or the technical skills of the relevant skilled person could provide guidance as to what was patentable.

The Enlarged Board then set out a number of questions for discussion at oral proceedings:

Can COMVIK be used for computer simulation? [Possibly rhetorical.]
If we assume COMVIK can be applied:
- Can “potential” or “virtual” technical effects be treated as “real” technical effects?
- How do “mental acts” and “discoveries, scientific theories and mathematical methods”, as set out in Article 52(2)(a) and (c), interact with simulations?
- Does the technical purpose need to be in the claim? [Possibly rhetorical.]
Is there a difference between simulations based on “human behaviour or activities” and simulations based on “natural phenomena”?

We will look again at the answers to these at the end of this post.

Admissibility

The good news is that the Enlarged Board indicated (unofficially) that the referral was admissible, mostly.

The Enlarged Board appeared happy with the admissibility of questions 1), 2b) and 3). They did, however, have reservations about the admissibility of question 2a). Questions 1), 2b) and 3) can mostly be answered with simple “yes/no” responses, whereas question 2a) asks for a “list of criteria”. It seems the Enlarged Board are (maybe wisely) side-stepping those criteria.

The EPO was of the opinion that all the questions were admissible, as both of the two alternatives set out in Article 112 EPC are met: there is a question of the uniform application of law via the purported deviation of T1227/05 and the issue is of fundamental importance as it may affect the ever-growing fields of machine learning and artificial intelligence.

Deviation from T1227/05

The Enlarged Board also gave a hint as to their thinking on any potential “non-uniform” application of the law based on T1227/05. They noted that there may not be as much deviation as initially suggested. They believe that T1227/05 does not provide blanket support for the patentability of simulation methods. Within their discussion of “functional technical features” it is inferred there is support for both the finding that a claim to a simulation may have technical features and the finding that a claim to a simulation may lack technical features. The EPO’s representative also indicated that there were some initial versions of the claims in T1227/05 that were subsequently withdrawn that were found not to provide an inventive step.

Dynamic Patentability & COMVIK

Although now rather old-hat, it was nice to see confirmation that the EPO and practitioners are generally happy with the way patentability is assessed for computer-implemented inventions. The present approach, based on COMVIK, is stable and stands up well when applied to deep learning inventions. This field hardly existed as a field of technology a decade ago but now makes up a substantial part of many inventions in both traditional and non-traditional engineering areas. It is better to define “technical” dynamically via the case law as what is an “invention” changes with time.

Indeed, “simulation” appears to have touched a nerve with the EPO and the filers of the amicus curiae briefs – it’s a Trojan horse for wider questions raised by machine learning and modern numerical methods. Most want these forms of inventions and methods to be protectable.

With one eye on the future, I was also please that the EPO implied that a link to direct measurement or physical reality was possibly not needed. The EPO stressed that the trend is towards increasing digitalisation, and an increased blurring of the line between real and simulated. Problems may be avoided if the EPO approach sticks to the key issue of whether a technical problem is solved and whether there is a technical purpose.

COMVIK also confirms that the mere fact that a feature relates to a specifically excluded field does not mean it cannot provide a technical effect. It may provide a technical effect in the context of the whole invention if a technical problem solved.

“Technical Simulations”

Many discussions on patentable subject matter descend into a circular definition of the term “technical”. The EPO made some great points as to why this was needed (see the Dynamic Patentability section below) but also presented some possible routes out of the maze.

One definition from the EPO that I liked was that a “technical” simulation is a “useful” simulation. This was then deemed to require an accurate and realistic model of a technical system that provides useful design information to an engineer. Such a simulation would include “technical considerations” if it reflected (at least) in part technical attributes of the simulated system.

The EPO also suggested that one condition for a “technical” simulation is that it needs to directly provide technical data as opposed to more generic “data” that needs to be further analysed by a user to derive technical data. It provides an option to find that general pedestrian modelling lacks suitable technical features as it does not provide concrete parameters that are useable for building design. But it does raise the suggestion that a claim may be patentable if specific building parameters were provided as an output.

The Appellant raised a good counterpoint – that sometime negative determinations from a simulation are technically useful. For example, if the simulation says not to build a particular chip or building, surely these negative decisions are as important as active positive decisions?

One suggestion for sufficient conditions as per question 2 from the EPO was that the object of the simulation was limited to a technical system and that the output of the simulation provides technical information relating to that technical system. Cases where these conditions are met may be easier to deem sufficiently “technical”. If is found that it is not a sufficient condition that the simulation is based, at least in part, on technical principles underlying the simulated system or process, then another condition could be that the output relates to technical properties of the system that is simulated, e.g. parameters relating to the performance of the simulated system.

The problem then mainly arises for more generic claims to simulations of “technical systems” without any clear technical output relating to the system being modelled. It was suggested that this requires more consideration of “technical purpose” and that this “technical purpose” needed to be present in the claim.

“Technical Purpose”

The concept of “technical purpose” came up a lot in discussions, and formed a core part of the Appellant’s arguments.

The basic premise was that if a simulation method had a “technical purpose” it could be patentable. The Appellant offered the concept of “technical purpose” as a way out of the difficulty of defining “physical entities” within “physical realities”. They stressed that a simulation is a tool and that we should look at purpose of that tool.

I am not sure the Enlarged Board will be happy going too far down this path.

For example, how do you reconcile a “technical purpose” argument with later infringement? It seems silly to need to rely on a separate nebulous concept when deciding whether a claim infringes. It would thus seem necessary to include any “technical purpose” within the claim, ideally as clear limitations on the claim. In this case, can you not just look at the claim language rather than requiring an external concept. For example, if a claim is directed to “modelling a building design” and comprises a “model of a building”, you can talk about a “technical purpose” of “building design”.

Later the Appellant raised an example of weather simulation. This is generally deemed to be non-patentable as you cannot control the weather. However, if you have a blind control system that uses a weather simulation, this appears patentable. The Appellant tried to link this to the “technical purpose” arguments. However, it seems more to indicate that the field needs to be defined in the claim – the general weather simulation is not patentable but use of the same in a field of technology is patentable. This kind of argument basically shifts the focus: is the field of building design suitably technical? This is the million-dollar question all parties appeared to be ducking.

“Potential” and “Virtual” Effects

The EPO provided the best (and only) definition of “potential” and “virtual” effects, which arose in the communication from the Enlarged Board.

A “potential” technical effect was deemed to be an effect that occurs via use of the invention.

A “virtual” technical effect was deemed to be an effect that is achieved in a computer, where the effect corresponds to an effect that would be seen if the modelled process was happening in the real world.

This can get complicated because computer program product claims can be said to provide a potential technical effect – in themselves they do not provide the technical effect but when the computer program is executed the technical effect is provided. The EPO rightly says we need to park this interpretation and not get into repeated ground from earlier G decisions. The conclusion in general is that there needs to be a concrete technical effect, but this can occur as it occurs for computer program claims.

Another interpretation of “potential” technical effects is that these relate to the Appellant’s teleological effects, i.e. they are effects that arise when the simulation is used for its technical purpose. However, this also raises difficulties, so we will see if the Enlarged Board quietly drop this language, or at least stress that we are looking for a concrete technical effect within the claim as we would for a computer program claim.

With regard to “virtual” technical effects, the EPO was of the opinion that, generally, a virtual technical effect does not solve a technical problem of itself. However, the “virtual” technical effect may be further used to provide a concrete technical effect.

An example was provided that covered direct and indirect measurements. With indirect measurements we may take raw data and compute a quantity rather than explicitly measuring it. For example, the quantity may be an estimate of a physical variable that is not measured directly but that is computed from other measurements or data. In this case, the “virtual” technical effect may be deemed to relate to the “virtual” measurement. Another example of a “virtual” technical effect provided by the EPO was the use of “virtual sensors” or “digital twins”. In this case, you may have an indirect measurement in parallel with a direct measurement.

It seemed to be implied that a further step was needed that included the use of the indirect measurements to solve a concrete technical problem for patentability, but in other cases it seemed implied that the indirect measurement itself could be patentable. We will have to see what the Enlarged Board say about this.

Aircraft Wind Tunnel Simulations

Both the EPO and the Appellant provided an example of a virtual wind tunnel. The EPO discussed how simulations of wind tunnels may be used instead of real wind tunnels to estimate and test aeronautical properties of aircraft wings. It was indicated by the EPO that a new experiment design for the testing of aircraft wings would be patentable, subject to the other requirements of novelty and inventive step, regardless of whether it was a physical or virtual experiment. Both virtual and concrete experiment designs were deemed alternative solutions to the same technical problem – and both should be patentable. This was also echoed by the Appellant who indicated it was the technical effect or technical purpose, rather than the form of the method, that mattered.

“Natural Processes” vs “Human Activities”

Within the discussions, following the questions in the communication from the Enlarged Board, a distinction between “natural processes” and “human activities” was made. Unfortunately, we got no further than an indication that this distinction may be important.

The implied tone appeared to be that “human activities” may be more likely to fall within the excluded subject matter of Article 52(2) EPC and so not be able to contribute to an inventive step in themselves. The EPO provided an example of a simulation of human agents in an online auction (a nod to T0258/03 for the fans) – while it was implied that these simulations per se may not be patentable, it was also implied that a larger claim that includes these simulation may not be unpatentable, as long as the claim solved a technical problem or had a technical purpose.

For me, the discussions of “human activities” had echoes of the “cognitive effects” discussed in the case law – stuff that relies on conscious activity within the human brain is normally assumed not to provide an inventive step.

The EPO though raised the interesting counterexample of ray tracing and rendering. This is a detailed and necessarily accurate modelling of physical or natural phenomena but where the technical effect has a perceptual aspect – the aim is to provide a lifelike rendering of an object, even though that object does not exist. I was impressed that this was explicitly raised, as it shows the Enlarged Board need to be careful of relying on an explicit link to actual physical objects or systems, or effects that exclude human minds.

This divide between “natural processes” and “human activities” has the potential to throw up many traps for the unwary. For example, when considering “physical activity”, i.e. actions in the world, where do you draw the line between “natural processes” and “human activities”? This is a key question for the present facts, as pedestrians can be considered both a human undertaking an activity and as an object within three-dimensional space.

Mental Acts, Scientific Discoveries & Mathematical Methods

We are generally taught that the exclusions of Article 52(2) EPC are equal in kind, so it was interesting to see the EPO submit that some exclusions are more exclusive than others and that the practice for considering certain “non-inventions” may differ according to the type of “non-invention”.

For example, “schemes, rules and methods for playing games or doing business” are relatively easy to evaluate – if certain claims features are deemed to relate to these exclusions then they cannot provide an inventive step and you cannot obtain a patent for a business method by just performing it on a computer. However, “discoveries, scientific theories and mathematical methods” and “schemes, rules and methods for performing mental acts” may be harder to disentangle – most inventions begin as “mental acts” and use “discoveries, scientific theories and mathematical methods”. In these latter cases, it is more important to look at the technical considerations and the technical problem that is being solved.

Examples of “features” that may be found in simulation claims include: the algorithms used, the models used, the computer-implementation, what is being modelled, and the purpose of the simulation. In this list, the EPO were of the opinion that “models” were most similar to excluded “mental acts”; however, in a simulation method, the “model” often serves a purpose. If this purpose is technical then the claim may be patentable.

It was also noted that machine learning models avoid the need for models that incorporate (directly) technical principles – they are just trained – but these appear to be valid alternative solutions and so should not be excluded from patentability.

In certain cases, claims that appeared on the surface to be “mental acts”, i.e. that could be performed mentally, could involve technical considerations and may be replicating physical experiments & measurements. For example, they may form a new experiment design for building aircraft wings. The EPO thought that applying the “paper & pencil” test (i.e. it is not patentable if you can do it with pen and paper) was not conclusive and was trumped if a technical solution could be demonstrated.

This also nicely fits in with the reason why “scientific discoveries” are excluded – they lack a “technical purpose” or fail to provide a “technical solution”.

Practitioners will be interested to here that the EPO appeared to allow for more leeway for claims based on (but not exclusively to) “mental acts”, “mathematical methods”, “scientific theories” that concretely solve a technical problem, e.g. as compared to “business methods”.

Civil Engineering Bias?

Civil engineering is an interesting field as there is often a bias against it. For example, patent people much prefer chemistry or hard-core electronics and often feel uncomfortable with the fringes of “technology” where category definitions are “fuzzier”.

A subtext that runs through the referral, but that no-one is brave enough to tackle head on, is a question as to the “technical” nature of civil engineering; put bluntly, is civil engineering “technical” enough?

This is where you have the potential problem with T1227/05 – this case is in a comfortably “hard” engineering field – circuit design. Without some legal gymnastics, if your gut feeling is that the claim in T0489/14 is not patentable, you seem to be saying that “noise in circuits” is a suitable “physical reality” for “technical” character but “people in a building” is not.

Of course, the irony behind this queasiness for softer fields is that the maths behind “noise” or “people” modelling is often the same! For example, differential equations, finite methods and/or digital approximations may be used for both cases. So the only difference is the use case (circuit vs building).

Question 3 – Do You Need Any Special Claim Form?

This question was looked at in two ways: first, in the narrow manner of the question – what if the the claim is part of a (wider) design process, e.g. for verifying a design – and second, in a wider sense of whether you needed any steps before or after the “simulation” part of the claim to aid patentability.

In the second case, options that are familiar from the case law in the UK and US include a preceding step relating to an explicit “measurement” of a real-world environment and a following “control” or “material production” step that sets out an explicit use of the simulation. Although no firm decisions were made, the general tone appeared to be that neither of these steps needed to be provided, and that a claim to the core of the simulation may be considered on its own. Both the Enlarged Board and the EPO indicated they were happy with the current practice applied to computer-implemented methods. This appears good news for applicants – the “measurement” and “control” steps introduce complications for infringement, especially for server-based methods where different parties may perform different ones of these additional steps.

The EPO did raise an additional point regarding the third question – if the simulation claim as such does not meet the sufficient condition(s) required by the answer to question 2b), would embedding that claim in a wider claim (e.g. to visualisation to assist a user) fulfill the sufficient condition(s)?

Revisiting the Enlarged Board’s Questions

To finish we can look back at the questions asked by the Enlarged Board in their communication of 22 June 2020 and begin to see some answers forming.

Can COMVIK be used for computer simulation?
- Appears to be “yes”.
If we assume COMVIK can be applied:
- Can “potential” or “virtual” technical effects be treated as “real” technical effects?
  - “No” – something more appears to be needed.
- How do “mental acts” and “discoveries, scientific theories and mathematical methods”, as set out in Article 52(2)(a) and (c), interact with simulations?
  - They may be present and care should be taken not to dismiss the claim just because they are present.
  - A different approach to “business methods” may be necessary.
  - Again, what matters is not their presence but that it can be demonstrated that a technical problem is being solved using the simulation (and maybe the simulation provides a technical effect beyond the technical effect of the simulation per se).
- Does the technical purpose need to be in the claim?
  - Implied “Yes” – although not really discussed directly.
Is there a difference between simulations based on “human behaviour or activities” and simulations based on “natural phenomena”?
- Implied – “Yes” – the former requiring use within a claim to provide a technical effect (over and above the simulation per se); the latter possibly providing a technical effect more as-is – but requiring some form of ultimate technical aim, effect or purpose.

Again, we will see for sure how this translates into the decision of the Enlarged Board in due course. See you next time for that!

EPO Computer-Implemented Inventions – Spring Case Law Review

As we all bed into virus lock-down, here is a quick review of a few European Patent Office Board of Appeal cases that relate to computer-implemented inventions (so-called “software patents”).

T 2272/13 – Inventory Tracking

This first case (see link here) considered a claim related to the management of a set of distributed devices. These devices included a “mother device” that needed to track a number of “satellite devices” over a Wireless Local Area Network (WLAN). The claim involved sending a “reachability request” and alerting a user if one of the “satellite devices” did not respond. The invention was described in the context of portable inventory tracking, e.g. tracking personal belongings and locating lost devices.

At first instance, the Examining Division refused the case for relating to a “non-technical” inventory scheme. The Board of Appeal were highly critical of the Examining Division’s decision, in the end remitting the case back to first instance for search and examination. This was despite the specification of WLAN being a choice from a set of described protocols that included RFID, infrared and Bluetooth.

This case may be of use to applicants and appellants that are facing difficulties at first instance, especially where objections assert that any “technical” features of the claim are deemed to be “notorious” or common general knowledge. The Board cited previous cases T 690/06 and T 1411/08 and asserted that the term “notorious” had to be interpreted narrowly. To prove that a technical feature is “notorious”, it is necessary for the examining division to indicate that it is “so well known that its existence at the date of priority cannot be reasonably disputed” and that the “technical detail is not significant”. The Board were also critical of the Examining Division’s assertion that the claims related to “the automation of [excluded] processes”.

The case continues a trend within the Board of Appeals to clamp down on lazy refusals from the examining divisions; the preferred approach is for at least some prior art to be cited, and for evidence-based reasons to be provided.

T 1442/16 – ECG Monitoring

This second case (see link here) was directed to medical monitoring, in particular to the collection and display of electrocardiogram (ECG) data. The main request was broader, referring to “sensors”, whereas the auxiliary request was limited to “ECG electrodes” and “ECG data”. The more concrete “ECG” auxiliary requests were analysed in detail.

It was determined that the claims of the auxiliary requests differed from the closest prior art in that each axis of a set of multiaxis diagrams displayed ECG data from a respective ECG lead, and that the position and the angle of each of the axes corresponded to the location of the respective ECG lead on the patient. This allowed a three-dimensional heart model to be displayed together with the multiaxis diagrams, where each of the axes in the multiaxis diagrams extended from the centre of the heart model.

The case thus considered whether the differing features above could contribute to inventive step, or whether they related to the “non technical” presentation of information (e.g. as per Article 52(2)(d) EPC).

The appellants began by arguing that the arrangement of the axes reflected an underlying state of a technical system – the technical system being formed by the set of ECG sensors. The Board of Appeal disagreed with this – the arrangement of the data was found not to prompt the physician to interact with the ECG device or to contribute to the functioning of said device. The Board felt that the alleged technical effect relied on the user’s cognitive abilities, for example the physician’s knowledge of anatomy and the principles underlying ECG. As such the differing features set out above were deemed to be “non-technical” on the grounds that they related to the presentation of information. They were thus disregarded, and a lack of inventive step was found. A “polygonal shape” of certain auxiliary requests was also found to be “non-technical”. The appellant did manage to have some success with the last auxiliary request, where the addition of an alarm trigger moved the claim away from the presentation of information.

This case makes it clear that where sensor information is being displayed, to avoid features being dismissed as relating to the presentation of information, the underlying system being sensed needs to be a technical system; the human body is not considered to be a technical system. It appears not to have helped the appellant’s case that the arrangement of the data related to an approach that was well-known to physicians (the Cabrera system). Arguments for an inventive step will also be strengthened if the display of information facilitates interaction with the sensing device.

As an aside, the Board also indicated that just because additional features were introduced gradually, with language such as “in another embodiment”, this did not mean that the applicant has a “carte blanche to mix and combine features from different embodiments as they please”. It seems that the EPO will only accept a combination of features if an example is provided that explicitly has those features. This is something to beware of when drafting applications for Europe.

T 0247/15 – Targeted Online Advertising

The last case (see link here) concerned the selective delivery of advertisements to a plurality of online users.

In the Board’s view, “advertising” is, in general, considered a “non-technical” activity by the European Patent Office. Consequently, the content of advertisements or any effect the advertisements might have on a user’s behaviour or on the sales of a product (or a service) are not regarded as technical features or technical effects that, when present in a patent claim, can contribute to an inventive step.

In particular, the Board considered that the described effect of delivering advertisements more “efficiently” was not a technical effect, as it did not relate to a technical aim to be achieved or a technical problem to be solved by a skilled person. For example, “efficiency”, as described in the application, related to a more “efficient” scheduling of delivery of content or of “optimizing” the delivery of content. On closer inspection these were deemed to relate to the fulfilment of conditions for an advertisement campaign (i. e. assuring that content is delivered to users according to the agreement with the advertisers) and maximizing the quantity of delivered advertisements. The “efficiency” and “optimization” thus did not relate to a “technical” system, e.g. they did not consider the utilisation of technical resources of the computer network, for example. Instead, these related to contractual constraints of the advertising campaign. Selecting what type of advertisement will be delivered to a user based on their profile (e.g., a default or targeted advertisement), monitoring the delivery of advertisements and updating the delivery list accordingly, were all considered to relate to an underlying business model. The solution was thus deemed an administrative method that involved the scheduling of delivery of online advertising content, which in turn was based on an abstract mathematical model. It could not support an inventive step.

EPO Computer-Implemented Invention Round Up

A quick post that checks on the state of play for computer-implemented inventions (“software patents”) at the European Patent Office. It has a quick look at some minor updates to the Guidelines for Examination and then reviews a few recent Board of Appeal cases.

Guidelines for Examination

After the overhaul of 2018, there are relatively few updates to the Guidelines for Examination for the 1 November 2019 edition. I go through those that relate to computer-implemented inventions below. I recommend viewing the links to the sections with the “Show modifications” check box ticked.

Section G-II, 3.3.1 on “Artificial intelligence and machine learning” has been tweaked to indicate that terms such as “support vector machine”, “reasoning engine” or “neural network” do not by themselves imply a technical means. They must be considered in context (i.e. make sure you describe a “hard” engineering problem and context).

Section G-II, 3.3 on “Mathematical methods” has a few minor changes.

It is stressed that special attention needs to be paid to the clarity of terms in claims that relate to mathematical methods. If terms are deemed to have “no well-recognised meaning” this may make it difficult to demonstrate a technical character (and so care should be taken to provide detailed functional definitions within the detailed description).

It is also added that mathematical methods may produce a technical effect when applied to a field of technology and/or adapted to a specific technical implementation. In this case, the “computational efficiency” of the steps of the methods may be taken into account when assessing inventive step. This is echoed in a minor update to section G-II, 3.6 on “Programs for computers”.

As also discussed below, the EPO is hinting that it might be a good idea to provide some actual experimental evidence to back up claims of increased efficiency when dealing with more abstract software-style inventions.

T 1248/12 – Data Privacy

T 1248/12 considers the field of data privacy.

In this case, the Board distinguished the field of data privacy from the field of data security. It was implied that the field of data security could give rise to technical solutions that provide an inventive step under Article 56 EPC. However, the field of data privacy was felt to relate to administrative, rather than technical, endeavours. In particular, the Board held that de-identifying data, by removing individually identifiable information, and by aggregating data from a plurality of sources, was not technical. It was felt that the claims related to data processing with a legal or administrative aim, rather than a technical one.

It is noted that the specification of the patent application was relatively light on concrete technical details – this may have led the Board to a negative opinion. The generalizations to the field of data privacy are perhaps too heavy-handed; there appears to be room to argue that some data privacy systems do contain technical features. In the light of this case, those drafting applications directed towards a data privacy aim may wish to determine if the technical effects may be recast in neighbouring data security fields.

T 0817/09 – Scoring a Document

T 0817/09 related to a computer implemented method for scoring a document.

The scoring was related to history data and was generated by monitoring signatures of the document, where the signatures were provided in the form of “term vectors”. As per similar linguistic processing cases discussed before, the “term vector” was found not to be “an inherently technical object” and “semantic similarity” was deemed to be a non-technical linguistic concept. The Board considered that solutions developed by the “notional mathematician” or the “notional computer programmer” would generally not be technical, whereas solutions developed by a digital signal processing engineer could be technical.

On the facts, the claimed methods were not found to provide any resource savings that could be presented as a technical, rather than linguistic effect. This does, however, suggest that providing evidence of technical improvements, e.g. reduced server processing times and/or reduced memory usage, may help applications with algorithmic subject matter.

T 0697/17 – Database Management Systems

T 0697/17 considered the patentability of SQL extensions within a relational database.

At first instance, the Examining Division held that the claim in question “entirely described a purely abstract method”. The Board disagreed: they held that the claim related to a method performed in a relational database system, which was a known form of software system within the field of computer science and as such would involve a computer system. The claim was thus not an abstract method. The Board noted that describing a technical feature at a high level of abstraction does not necessarily take away the feature’s technical character.

In consideration of inventive step, the Board cited T 1924/17, and stated that features make a technical contribution if they result from technical considerations on how to (for instance) improve processing speed, reduce the amount of memory required, improve availability or scalability, or reduce network traffic, when compared with the prior art or once added to the other features of the invention, and contribute in combination with technical features to achieve such an effect. However, effects and the respective features are non-technical if the effects are achieved by non-technical modifications to the underlying non-technical method or scheme (for example, a change of the business model, or a “pure algorithmic scheme”, i.e. an algorithmic scheme not based on technical considerations). The Board made an interesting distinction between a “programmer as such” and a “technical programmer”, and stated it was difficult to distinguish abstract algorithmic aspects that were non-technical and arose from the “programmer as such” from “technical programming” aspects that arose from the “technical programmer”.

Returning to T 1924/17, the Board concluded that a database management system is not a computer program as such but rather a technical system. The data structures used for providing access to data and for optimising and processing queries were deemed functional data structures and were held to purposively control the operation of the database management system and of the computer system to perform those technical tasks. While a database system is used to store non-technical information and database design usually involves information-modelling aspects, which do not contribute to solving a technical problem, the implementation of a database management system involves technical considerations. In the end, the case, which had been pending for 13 years, was remitted back to the Examining Division with an informally rap over the knuckles. It provides a useful citation for those drafting and prosecuting applications relating to database management systems.

G1/19 – Enlarged Board of Appeal to Consider the Matrix

On 22 February 2019, a Board of Appeal of the European Patent Office referred a set of questions relating to computer simulations to the Enlarged Board of Appeal (the European patent version of the Supreme Court). This is being considered as pending referral G1/19. This is only the second time that the Enlarged Board of Appeal have considered questions relating to computer-related inventions. If the Enlarged Board of Appeal choose to answer the questions, the result could influence how machine learning and artificial intelligence inventions are examined in Europe.

The full interlocutory decision that led to the referral can be found here: G1/19.

All the People, So Many People

The original case on appeal related to modelling pedestrian crowd movement. Claim 1 of the main request considered a model of a pedestrian that included a “provisional path” and “an inconvenience function”, where movement was analysed around various obstacles. The models could be used to design buildings such as train stations or stadiums.

The Board of Appeal found that claim 1 avoided the exclusions of Articles 52(2) and (3) EPC as it related to a “computer-implemented method”. However, the Board considered that claim 1 was straightforward to implement on a computer “requiring only basic knowledge of data structures and algorithms”. The Board also deemed that the design of the method was not motivated by technical considerations concerning the internal functioning of the computer. The question of inventive step thus revolved around whether claim 1 provided further technical aspects that go beyond the mere functioning of a conventionally programmed computer.

Electrons are People too?

The Appellant argued that the claim provided a further technical effect in the form of “a more accurate simulation of crowd movement”. The Appellant argued that modelling a crowd was no different from modelling a set of electrons. The case on appeal thus considered what form of activities could be seen as a technical task producing a technical effect. The Board was not convinced that numerically calculating the trajectory of an object as determined by the laws of physics was always a technical task; for example, they believed that a technical effect requires, at a minimum a direct link with physical reality, such as a change in or a measurement of a physical entity.

Divergence Klaxon

The problem for the Board of Appeal was that the Appellant cited T 1227/05, which is discussed here. In this case, a numerical simulation of a noise-affected circuit was deemed to provide a technical effect as it related to “an adequately defined class of technical items”. However, the Board agreed that the arguments applied in T 1227/05 could be applied to the modelling of pedestrians, as the laws of physics apply to both people and electrons. The Board appeared minded to go against T 1227/05, on the grounds that the benefits were either in the brains of the engineers using the simulations or were the common benefits of using computers to implement testing methods.

Is Software 2.0 Patentable in Europe?

The Board appreciated that numerical development tools and computer simulations play an important role in the development of new products. Many of the points under discussion could also apply to the much larger field of machine learning, where the lines between measurement and simulation are often blurred. Indeed, the Board note this saying that there may be no ground to distinguish between simulating and using a model to predict the function of a system. The Board believed that a decision on the patentability of simulation methods needs to be made, and guidance on the interpretation of Articles 52(2) and (3) and 56 EPC would be useful.

We have Questions

The Board have thus referred three questions to the Enlarged Board of Appeal:

In the assessment of inventive step, can the computer-implemented simulation of a technical system or process solve a technical problem by producing a technical effect which goes beyond the simulation’s implementation on a computer, if the computer-implemented simulation is claimed as such?
If the answer to the first question is yes, what are the relevant criteria for assessing whether a computer-implemented simulation claimed as such solves a technical problem? In particular, is it a sufficient condition that the simulation is based, at least in part, on technical principles underlying the simulated system or process?
What are the answers to the first and second questions if the computer-implemented simulation is claimed as part of a design process, in particular for verifying a design?

The referral raises a number of interesting points for computer-related inventions. In the interlocutory decision the Board go back to VICOM (T 208/84) to argue that a direct link with physical reality seems necessary. However, they cited different cases to suggest that it was not clear whether a direct or “real-world” effect needed to be present to provide a technical effect. With simulations there is also a question of whether non-claimed features, such as a future use, could be taken into account when assessing inventive step.

The latter part of the decision provides an overview of a number of Board of Appeal cases in the area of simulation and also touches on the national case law of Germany and the United Kingdom, citing Logikverifikation (published in English in IIC 2002, 231), Halliburton v Smith International [2005] EWHC 1623 (Pat) and Halliburton v Comptroller-General of Patents [2011] EWHC 2508 (Pat).

My Two Cents

The Board of Appeal have done well to summarise the issues in the area of simulation and to highlight where further clarification would be useful. As is often the case, the actual claimed invention appears to be a straw person for consideration of broader policy questions.

The referral is timely. Cases relating to machine learning and “artificial intelligence” are increasing rapidly. Last time the Enlarged Board of Appeal had a chance to clarify the law for computer-implemented inventions, in G3/08, they dodged the bullet, arguing that the referral was inadmissible. The well-formed points by the Board of Appeal, and the general zeitgeist, mean that they may not be able to do this again.

Revised Guidelines for Computer-Implemented Inventions

Examination practice at the European Patent Office follows a set of Guidelines. These are published online and provide guidance for European Examiners and applicants. They are updated annually.

An updated set of Guidelines came into force on 1st November 2018. The recent updates introduce major amendments to sections that cover subject matter that is excluded from patentability in European. These sections include those directed to “mathematical methods”, “schemes, rules and methods for performing mental acts, playing games or doing business” (often shortened to “business methods”), and “programs for computers”. The updates are relevant to those filing applications related to “computer-implemented inventions” (often colloquially referred to as “software patents”).

Although the amendments do not significantly change current practice at the European Patent Office, they do expand the guidance on what may and may not be protected with a European patent. They represent a significant upgrade and demonstrate the maturity of the case law with regard to computer-implemented inventions.

This post will review and highlight the updates. The post may be useful for those seeking to patent machine learning and artificial intelligence inventions. The updates cover the following areas:

claims to distributed computing systems;
inventions that use mathematical methods;
AI and machine learning inventions;
inventions that cover simulations and models; and
inventions that relate to business methods, gaming, mental acts or computer programs.

Distributed Computing

Section F-IV, 3.9.3 has been added to the section relating to claims for computer-implemented inventions. It provides expanded guidance and an example relevant to processes operating in a distributed computing environment. These processes form a basis for many real-world implementations of computer-implemented inventions. For example, a smartphone accessing a cloud computing service would implement a process operating in a distributed computing environment.

The section sets out the current practice of the European Patent Office. Claims in a claim set may be directed to each entity of the distributed system and/or the system as a whole. Such a claim set may be argued to meet the requirement for multiple independent claims set by Rule 43(2)(a) EPC, i.e. the claims may be allowed despite having multiple independent claims in the same category because the subject-matter of the claims relates to a plurality of interrelated products. However, each individual claim will need to meet the requirements of novelty, inventive step and clarity.

For example, if a cloud computing service provides a new image classification function via an application programming interface that is accessed by a smartphone, a claim set may feature apparatus claims to both a server computing device (the cloud server) and a mobile computing device (the “accessing device” or smartphone). If the smartphone is simply a generic smartphone making a network request (e.g. an “HTTPS request to a REST API endpoint”), it will likely not be new when compared to known smartphones. An objection will be raised against the smartphone claim. However, if the smartphone implements some new low-level processing, e.g. some new feature extraction process that is specific to the new image classification function (like pre-extracting cat-like facial features), it may also be new and inventive in itself and be allowed.

The updated section draws our attention to the need for clarity in claims to distributed entities. It recommends that distributed method claims specify the entity that is performing each method step.

Claiming distributed processes in a challenge. In practice, one entity often implements most of the new and inventive process (e.g. the cloud server), while other devices are relatively “thin” and generic (e.g. the smartphone). However, claims to the accessing device are often of greater commercial value (e.g. they might allow a royalty for each smartphone that is sold). This often leads to the inclusion of claims to the mobile computing device in a claim set, but a high likelihood of an objection being raised by the European Examiner.

To attempt to overcome novelty and inventive step objections to “accessing device” claims, it is common to include an indirect or implicit reference to the functions of the server computing device. This can then lead to one or more clarity, novelty or inventive step objections. For example, the indirect features may trigger a clarity objection for not clearly specifying features of the “accessing device”. Alternatively, the indirect features may be ignored for novelty and/or inventive step, as they are deemed to present no inherent structural limitations for the “accessing device”.

When drafting claims to distributed systems, it is worth questioning the inventors to determine what functions may be implemented with low-level adaptations to the accessing device. If the invention can be embodied in an “app”, it is worth looking at the architecture of the app, and the sequence of low-level system calls it may be implementing. This may not be obvious to the inventors, as commercial and engineering demands often require as much functionality as possible to be embodied on the back-end in the cloud.

Mathematical Methods

The section on the examination of mathematical methods has been re-written and two sub-sections have been added. A first sub-section – 3.3.1 – now provides specific guidance for artificial intelligence and machine learning. A second sub-section – 3.3.2 – expands upon claims to simulation, design or modelling.

The updated guidance is now clearer on the importance of “technical means”, i.e. a concrete implementation in a field of technology, when an invention makes use of mathematical methods. This complements the recent changes to practice for “abstract inventions” in the United States.

I really like the updates to this section and the inclusion of helpful concrete examples. The section emphasises that a mathematical method or algorithm per se will not be enough to make a claim feature patentable, although many patentable inventions do have a mathematical or algorithmic basis.

Examples of the Fast Fourier Transform, geometric objects and graphs are provided: these features may contribute to the technical character of an invention if they contribute to producing a technical effect that serves a technical purpose. Put another way, these features need to be provided in a context that relates to an engineering problem encountered in the real-world, and the use of these features needs to result in a change in the real-world that helps solve that problem. This is further emphasised later in the guidance – the technical purpose of the mathematical features needs to be specific rather than generic, and the claim needs to be functionally limited to the technical purpose, either explicitly or implicitly.

What kind of applications are seen by the European Patent Office to be “technical”? My personal definition is: does the application relate to something in the real-world that requires knowledge that is taught in an undergraduate engineering degree? If the answer is “yes”, then the application is “technical”. If the answer is “no”, then the application may not be “technical”.

Section 3.3 now provides a useful list of purposes that are deemed “technical”. These include:

controlling a specific machine or technical process, e.g. an X-ray apparatus or steel cooling;
using measurements to control a machine or technical process, e.g. using a compaction machine to achieve a desired material density;
digital audio/visual processing, this can be relatively high-level – detecting people is a provided example (but a clear relation to captured data is recommended);
processing speech data, e.g. to generate text (but processing text per se may not be technical);
encoding data, e.g. for transmission or storage;
cryptography;
load balancing;
generating higher-level measurements by processing data from physiological sensors or other medical diagnosis;
analysing DNA samples to provide a genotype estimate; and
simulating “technical” things (this is described in more detail in new sub-section 3.3.2).

The section stresses that there must be a sufficient link between the technical purpose of the invention and the mathematical method steps, for example, by specifying how the input and the output of the sequence of mathematical steps relate to the technical purpose so that the mathematical method is causally linked to a technical effect. When drafting an application for Europe for an invention that features mathematical operations (e.g. equations and/or algorithmic designs), it is recommended to place such an explanation in the description – this can then be pointed to in examination if any objection is raised.

Similar to practice in the United Kingdom, the section ends by indicating that a feature may contribute to the technical character of an invention independently of any technical application, when the claim is directed to a specific technical implementation of a mathematical method, and the mathematical method is particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer. Using the Fast Fourier Transform example, it may be possible to obtain protection for a new digital implementation of the Fast Fourier Transform, if you were performing specific mathematical operations that were adapted to the available computing resources of the implementation, e.g. available memory registers, processing cores, etc.

When considering inventions involving mathematical methods, one useful approach is to make an initial determination:

Does the invention relate to a specific engineering application (e.g. what branch of “applied” maths is being considered)?
Or does the invention relate to a new technical implementation of a mathematical operation (e.g. in effect a new and beneficial way of performing mathematical operations or “computing” using a device – sometimes called “core” inventions)?

A positive answer in the first case, suggests a quick check against the provided examples and the case law to determine if the specific engineering application has in the past been deemed to be “technical” under European practice.

A positive answer in the second case suggests looking carefully at the constraints imposed by the electronic hardware of the implementation. You will need to describe how the mathematical method is adapted, e.g. as compared to a “text-book” application, to provide concrete implementational improvements.

Artificial Intelligence and Machine Learning

Sub-section 3.3.1 is relatively short and seeks to summarise existing case law that applies in this area. This anticipates a large rise in patent applications over the coming years.

Machine learning inventions have been patented at the European Patent Office almost since its inception in the late 1970s. The present sub-section reminds us that despite the recent resurgence in neural networks, algorithms for approaches such as “classification, clustering, regression and dimensionality reduction” including “genetic algorithms, support vector machines, k-means, kernel regression and discriminant analysis” have been around for a number of years.

The sub-section stresses that the algorithms and approaches themselves per se of an abstract mathematical nature. The guidance from section 3.3 therefore applies: the invention either needs to relate to a specific engineering application that uses the approaches (e.g. using k-means clustering to classify packets in a network for selective filtering) or a new technical implementation of the approach that is constrained by technical factors at least the underlying computation hardware.

The sub-section hints that “technical character” often requires a clear causal link to measured data that represents physical phenomena. For example, classification of digital data such as physiological measurements, images, videos, or audio data is seen to be a common “technical application”. However, classifying text data is regarded as a “linguistic” and “non-technical” application. Likewise, general classification of “data” or “records” without a link to a specific technical problem would likely be seen as “non-technical”. Reference is made to case T 1358/09.

The sub-section ends by indicating that if a classification method is seen to serve a technical purpose then the steps of generating the training set and training the classifier may also contribute to the technical character of the invention if they support the technical purpose. This provides useful advice for drafting claims for inventions in this area: it is recommended to consider independent claims to the generation of training data and architecture training, as well as claims to an inference step. These claims may also provide a distributed processing system as discussed in section 3.3, for example inference may be performed on a smartphone, whereas data cleaning and training may be performed on a remote server. Care should be taken to cover different infringing acts.

Simulation, design or modelling

Sub-section 3.3.2 draws out material that was present in section 3.3. Discussing this material in a separate sub-section clarifies the high-level overview now present in section 3.3.

A computer-implemented simulation of a specific technical system or process may be seen to provide a technical effect and lead to a granted European patent. However, objections will be raised to computer-implemented simulations of non-technical systems or processes, such as those with an aim in the fields of finance, marketing, administration, scheduling or logistics. Care should be taken; cases such as T 531/09 indicate that the presence of technical devices (X-ray scanners in that case) is not enough to provide technical character, the technical devices need to be specific devices and the simulation needs to perform a technical purpose.

In the field of computer-aided design, the determination of a technical parameter which is intrinsically linked to the functioning of a technical object, where the determination is based on technical considerations, is a technical purpose. For example, a method of determining a particular value for a parameter of a specific technical device, in a manner that improves production or use of the device may be seen as suitably “technical”. Care should be taken if the design involves decisions to be made by a human being – e.g. the selection of an approved value – this intervention may be seen to break a causal chain that connects the design method to a technical purpose. Such decisions also risk importing factors that are outside of a narrow determination based on “technical considerations”.

Finally, this new sub-section suggests that claims that produce “models” will often lead to objections on the grounds that the models are not technical features per se; instead, they are seen as “abstract” mathematical or mental features. This again complements current practice in the United States. It is emphasised that generation of a model may be considered to lack a technical effect, even if the modelled product, system or process is technical. It this case it is important that the claim clearly indicates how the model is used, or to be used, in a technical system or process to solve a technical problem.

Business Methods

The previous high-level summary in section 3.5 has now been deleted, with this material being moved into separate sub-sections related to each of “performing mental acts”, “playing games” and “doing business”. Each sub-section then contains new material relating to each sub-category.

Each sub-section begins with a useful definition of each exclusion. Although this is described in the context of the exclusion being applied to the whole claim (e.g. the exclusion applying “as such” or “per se”), this often will not occur in practice, e.g. in most cases the exclusions set out in Article 52(2)(c) EPC will be avoided by having the method performed by a computing device. However, the definitions are useful as they indicate which claim features may be ignored for inventive step on the grounds that they provide no technical effect.

Mental Acts

These are described as instructions to the human mind on how to conduct cognitive, conceptual or intellectual processes. The learning of a language is given as an example, which hints at how the European Patent Office legally support an objection to “linguistic” features (e.g. text processing) as being non-technical.

When drafting claims to computer-implemented inventions, especially method claims, care should be taken to avoid accidentally falling within the exclusion. For example, claims should be checked to ensure that the method steps therein cannot be performed entirely in the human mind; at least one step needs to be performed outside of the human mind. In practice, considering whether a method step can be performed in the human mind is useful when predicting whether inventive step objections may be issued during European examination; if the determination is positive, the method step can often be drafted or amended in a manner that avoids this interpretation, e.g. by referring to a specific technical apparatus. The sub-section indicates that a method would not be seen as performing mental acts if it requires the use of technical means or if it provides a physical entity as a resulting product.

The sub-section does not indicate that mental steps are necessarily ignored for an analysis of inventive step; however, it does emphasise that are mental steps must contribute to producing a technical effect that serves a technical process. A good example provided in the sub-section is that of affixing a driver to a Coriolis mass flowmeter: steps specifying the position of the driver may be performed mentally but by defining the position so as to maximise the performance of the flowmeter, a technical contribution is provided.

Games

Games are defined in sub-section 3.5.2 as a conceptual framework of conventions and conditions that govern player conduct and how a game evolves in response to decisions and actions by the players. Games are governed by game rules, that are by their nature abstract, mental entities that are only meaningful within a game context. Games may be simple – matching random numbers – or complex – video games with extensive virtual game worlds.

If a claim sets out technical means for implementing the rules of a game, it is not excluded as such and analysis moves onto inventive step. To provide an inventive step, a claim feature must make a technical contribution, i.e. provide some engineering benefit beyond a mere computer-implementation of the game rules. The benefit of a claim feature is to be assessed from the point of view of an engineer or game programmer, who may be given the games rules by a game designer as a “requirements specification”.

The sub-section indicates that in many situations the burden is on the applicant to show that a gaming invention provides a real engineering benefit. It notes that abstracting non-technical game elements, relying on a complexity of a solution or indicating cognitive content will not help the applicant.

It is interesting to compare the general negativity of this sub-section with cases such as T 928/03 and T 12/08 that presented a more liberal view of the technical nature of gaming inventions. It will be seen whether they represent a narrower approach than seen in the past.

Doing Business

Doing business is defined in sub-section 3.5.3 as including activities which are of financial, commercial, administrative or organisational nature. The latter two areas should be noted; they are often overlooked as they do not directly relate to making a profit but are still seen to be “non-technical”.

Some useful examples of “business method” features are provided. They include:

banking,
billing,
accounting,
marketing,
advertising,
licensing,
management of rights and contractual agreements,
legal activities,
personnel management,
workflows,
scheduling of tasks,
logistics,
organisational rules,
operational research,
planning, forecasting,
business optimisation, and
data science for the purpose of managerial decision making.

If an invention relates to any of these features, it should be assumed to relate to excluded subject matter unless there is strong evidence that a technical problem is being solved by a technical solution that involves technical considerations.

For practitioners, a disclosure document or inventor from industry will often present an invention in terms of a commercial benefit. For example, inventors often become familiar with internally promoting an invention on commercial grounds. Care should be taken to dig behind these grounds and return to the underlying engineering aspects of the idea. If no engineering aspects can be presented, the idea may not be suitable for a European patent application. Examiners and Boards of Appeal will also use an indication of a commercial benefit, or the presence of the above business features, as evidence of a lack of a technical contribution. For this reason, it is recommended to avoid discussing these when drafting the patent specification.

Programs for Computers

Section 3.6 has now been redrafted and sub-sections 3.6.1, 3.6.2, and 3.6.3 have been added to respectively cover “further technical effects”, “information modelling” and programming, and “data retrieval, formats and structures”.

Section 3.6 now begins by indicating that computer programs must produce a “further technical effect” to avoid exclusion on the grounds of being a computer program “as such”. A “further technical effect” is an effect that goes beyond the normal operation of a computer, e.g. the physical effects of executing a computer program. Controlling a technical process or the internal functioning of a computer or its interfaces are deemed to be valid “further technical effects”.

Although not explicitly indicated in section 3.6, it is relatively straightforward to demonstrate a “further technical effect” and avoid an objection to the whole claim under Articles 52(2)(c) and (3) EPC. For example, claims to a computer program may be said to provide a “further technical effect” if they include instructions to implement a technical method, e.g. if they indicate a dependency to an independent method claim that is deemed technical. In this manner, European patent applications often feature claims to a “computer program for implementing the method of claim X”.

Further technical effects that may be demonstrated by a computer program are set out in sub-section 3.6.1. These include:

controlling a technical system or process (e.g. a braking system of a car or an X-ray device);
data processing in any of the areas highlighted in section 3.3, e.g. audio/visual processing, encryption or compression;
improving the internal functioning of a computer running the program, e.g. programs that are adapted for a specific architecture or that provide benefits at the kernel or operating system level; and
providing low-level tools such as compilers, memory allocators, and builders.

This updated section and its sub-sections are more useful in indicating what kind of features may be deemed to provide a technical effect. For example, if a feature of a computer program is deemed to provide a “further technical effect” as set out in this section, the feature would be seen as “technical” and be counted in any evaluation of inventive step (e.g. for other independent system or method claims).

Information Modelling and Programming

Sub-section 3.6.2 now provides useful guidance when the invention relates to aspects of computer engineering or software in itself, e.g. as opposed to a computerised solution in another field of engineering. While software developers may assume that their solution is technical according to the normal use of that term, features may not actually be “technical” for the requirements of patentability.

Information modelling is defined here as relating to providing a formal description of a real-world system or process. It may be seen to relate to models built in graphical or textual modelling languages, such as the Unified Modelling Language (UML) or the Business Process Modelling Notation (BPMN). Information Modelling may result in data models or templates that represent an underlying process.

Programming is defined as relating to the way in which computer code is written. It can involve choosing certain options or conventions for performing a common functional operation, or defining and providing a programming language, including text-based or graphical systems.

This sub-section stresses that information modelling or programming features that improve the intellectual effort of a programmer or software developer will often be seen to lack technical character and so cannot contribute to an inventive step. Benefits such as re-usability, platform-independence, conciseness, easier code-management or convenience for documentation, are not regarded as technical effects. For a feature to provide a technical effect, it must provide an improvement from the viewpoint of the computer, as opposed to the programmer. For example, manipulating machine code to provide for greater memory efficiency is seen as providing a technical contribution.

Data retrieval, formats and structures

Computer-implemented data structures or data formats embodied on a medium or as an electromagnetic carrier wave may be claimed, as they do not fall within the exclusions of Article 52(2) EPC. This sub-section has been relocated from previous section 3.7.

This section emphasises that cognitive data, i.e. data that is only relevant to a human user, cannot normally contribute to an inventive step. However, functional data, i.e. data that controls a device processing the data and that reflects technical features of the device, can.

Some examples of functional data are provided. These include a picture encoding, an index structure for a relational database, or a header structure of an electronic message. It is emphasised that the actually data content of the picture, database record or electronic message is often seen to be cognitive content and so cannot contribute to an inventive step.

Flexible Working & Career Breaks: A Personal Perspective

In advance of IP Inclusive’s Women in IP Flexible Working & Career Breaks Event, this post sets out some of the things I learnt taking a career break (shared parent leave) and working part-time. I hope it may be useful for others considering “off piste” career options.

For context, I have stuck my route through this whole thing at the bottom. Although I come at it from the point of view of a patent attorney, many points apply to other roles within the intellectual property (IP) profession, including paralegals, searchers, examiners, “back-office” roles, and IP solicitors.

Another disclaimer: these are my own views and not necessarily those of my employers present or past. You may also disagree; I am open to (polite!) discussion in the comments below.

As everyone likes “listicles” (or so say the £300/hour marketing consultants), I have decomposed my experience into ten key points:

There is no “official” path
You are lucky
Switch your view
You have a choice, but you can’t have it all
Time = Money = Career
The Patriarchy is accidental
Two is better than one
There is life outside of London
Remote working works
Imperfect Balance

Intrigued? Let’s have a look.

There is no “official” path

gray pathway surrounded by green tress — Photo by Skitterphoto on Pexels.com

When I first entered the world of intellectual property there seemed to be a well-defined structure: start in a junior role, put in the hours, work your way up to a senior role and a larger salary, work for another 20 years, get a gold clock and hope you have a defined benefit pension scheme.

This world is changing. Maybe it didn’t really exist at all. Assumptions are being called into question. The opening up of the workplace to a wider variety of backgrounds, together with general societal trends, means that people are taking a different, more realistic, and in many ways healthier, approach to work and careers.

Work for long enough and you too may see the cracks in the traditional narrative. Staff leave and join. Things you assumed were part of a “life path”, like housing or good health may no longer be available. Over the water-cooler or a networking pint, you hear whispers of others “doing something different”. You see people get lucky and unlucky rolls of the dice. You begin to learn that there are other possibilities, and that the hardest part is often imagining them.

The Internet and social media is a great help with this. You can see people move around on LinkedIn, post about their experiences on blogs such as this, or comment on Twitter.

Start with honesty. If things don’t seem to be working, try to work out why and don’t assume that there is a set structure or path you need to follow. Reach out to others in your employment, or via social media, to see what other routes there may be. Don’t be afraid to chat things through informally with your employer, line manager or a member of human resources; in my experience any fear I had was misplaced and people genuinely want to find something that works for everyone.

You are lucky

green club flower — Photo by Anthony on Pexels.com

If you are working in the world of IP, you are lucky. This applies regardless of your role.

Don’t believe me? If you work in private practice, you can look up the accounts of patent and law firms for free at Companies House. You will see that these businesses do not operate with food-production or milk-farmer level margins. Technology companies also tend to be better placed in the economy than other sectors, such as retail. Yes there are worse years and better years, but look at annual profits or average partner remuneration (spoiler: private practice averages are somewhere between £200k and £500k). The patent profession is “effectively a legal cartel” (quoting Sir Colin Birss).

This means that there is more monetary slack in the system than there may be in other industries. The skills of all roles are in high demand. This put employees in a good negotiating position. Firms and companies need to retain trained staff, and most firms and companies are always looking for more staff. Use this to your advantage. You have the margin to be brave in your choices.

Switch your view

ball ball shaped blur daylight — Photo by Pixabay on Pexels.com

That being said, businesses need to be run, and they need to make a profit. I’ve been on both sides of the managerial chair. If you want to branch out beyond the normal 9-5 employment contracts, your options also need to make managerial and financial sense for those you are working for. In an ideal world, it would be nice to live an idle life on law firm partner pay. But we can’t all be Jacob Rees-Mogg.

A good place to start is to work out your cost to your employer or to those using your services. Think about how your employer affords your salary or pay. In private practice, income comes from invoicing clients. In industry, a department may have an annual budget, revenue targets and key performance indicators. Remember that you often cost more than the money you receive in your paycheck. There is tax, national insurance, employee benefits, office costs, training costs, pension costs, etc. etc.

Then think about how your activities fit into the value chain. Docketing or paralegal cost may be charged as a “service fee” or have a budgeted amount per case. How much of your effort is required per work type? How much do you need to be paid to cover your personal expenses? How many hours do you have available per day? What seems like a endless series of choices can be whittled down to a practical proposal.

For employers, “flexible working” and “career breaks” have often been viewed with distrust. It was thought that employees could not be trusted, that flexible workers were too “difficult” to manage, or that the bottom line would fall. None of this has really come to pass. Indeed, the more astute employers are realising that in a field where there is a large demand for skills, offering flexibility can be a great selling point. Work/life balance was recently voted the most important concern for attorneys, above salary. Turns out wealth isn’t everything, who would have guessed? Beyond recruitment, a flexible workforce can also benefit the bottom line, helping get the day jobs done and freeing up in-house attorney time. The legal market is stretching vertically and approaches traditionally applied by counsel in industry to manage outside counsel are now being applied by law firms to manage virtual teams.

You have a choice, but you can’t have it all

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In my experience, running one or more households and caring for one or more human beings is a full-time job. Trying to wish otherwise is unrealistic. The choice comes in the form of the person (or persons) doing this job. It could be:

one person full-time without pay (e.g. the traditional “houseperson” or grandparent);
two people part-time without pay (e.g. two freelancers or a “second job” + grandparent or sibling);
one person full-time with pay (e.g. a childminder or full-time carer); or
one person doing this part-time without pay and one person doing this part-time with pay (e.g. the also semi-traditional “second job” + “help”).

The person could be a parent, a child, a family member, or a friend.

For some the choice may not be much of a choice. Each person has a different set of circumstances. Some options may be easier than others. Many options may only be options if something is given-up or bartered. Having your cake and eating it is like a free-lunch, you are paying somewhere, it just may not be obvious at first sight. These may not be as intuitive as you think they are.

What you can’t try to wish out of existence is the caring role itself. I like to view carer roles like physicists view dark matter – just because you don’t see them doesn’t mean they are not there. It is a cliché (but true) that for every person with external responsibilities working 12-hour days there are others that we do not see working hard, and we may never directly see this effort. It seems tragic that it is only recently we are being to talk about how these roles are divided, and realising that a majority of the population may have an opinion on the matter that differs from our expectations.

Time = Money = Career

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Once you have set out the demands of your external responsibilities, your job responsibilities and your expenses, you can start to map the options available to all parties in a household.

Three levers you can vary are: time, money and career. These are interconnected. You can have more time, but this may come at a literal cost to your wallet and a figurative cost to your career (or at least a certain image of your career). You can have higher pay, but you may have less time.

You may have more control over these than you think. Most people think of money and career as a one-way street (for the patent attorneys: “a monotonically increasing function“). Looking at the hours many senior staff put in, as you rise in your role, your “free” time also diminishes. Many of us know senior partners in law firms or department managers that are checking their emails while on holiday or in the evenings. This email time is time that is not available for a role caring for other human beings, or doing household chores, or taking up a hobby, or volunteering in a local group. Time is a fixed quantity. Remember the cost is not always visible.

In negotiations for flexible working do not be afraid to trade salary for time. If you have fewer hours available per week, e.g. because you have to care for a child or relative, this will mean you can work fewer hours per week. However, make sure that you are not being paid less for the same work. Look at the value your work provides your employer – for a paralegal this may be charged out directly, for an IT support role this may be indirectly charged out as patent attorney or paralegal time (if the computers stopped working no one could charge anything). This may be harder for roles in industry than for private practice, but you may find a costing for IP in an annual review or a licensing income you can use to justify your value. You may feel a bit besmirched putting everything in monetary terms but remember this is not your value, it is just a representation of your value for dealing with an entity whose purpose is monetary (to make a profit). You can increase your chances of success by playing the game. You can also play around with the figures, and mock up different scenarios.

The Patriarchy is accidental

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In private practice, most law firms have a heavily male partnership. Roles in industry are better, and it is good to see Women in IP highlighting senior female figures in IP, but there is still a male skew (evidence: attendees at CIPA Congress 2018). There is also a female skew to paralegal roles. Some believe this is driven by unintelligent design; I’m more of the opinion this is a historical accident that is open to change.

Now I’m not going to take on what is a huge issue, and walk briskly into a minefield. But I can just about see how a heavy-male skew in senior patent attorney roles can arise. First there is a bias towards men working in science and engineering (one I’m trying to reverse with my parental nudging to my daughters). Second to enter partnership in private practice you need to work hard and have excellent billing figures. The time many enter partnership also overlaps with the time that many start a family (30-40s), or if you have older parents, manage their ageing. Even at the most progressive patent firms, partnership and a 3-day week is generally not an option. To be fair, it is difficult (impossible?) to provide full 24-hour service to demanding clients and provide full 24-hour service to demanding children. Cultural bias, breastfeeding, self-affirming networks, the need to physically recover after birth, and the need to pay the bills all conspire to nudge the pinball of life towards traditional male-female caregiver roles. Now just because something is, doesn’t mean it should be. A first step is awareness; a second step is change. I receive many a confused look from men when I ask them whether they are willing to give up their own career development for a time to allow their partner to rise up through the ranks. Normally this is excused on monetary terms: we couldn’t afford it. Forgive my scepticism, but in the patent world this can come from people earning six-figure salaries. The average UK full-time salary is below £30,000 (gross).

Once you see the random trail of the pinball you can take more control over its direction. As a business owner or employer you can make the playground equal to allow both men and women to take time off. You can change how work is performed to make it easier to distribute work in a piece-meal fashion, facilitating flexible working. Men can realise that they may need to adjust their perceived route through life, that there are other paths. We can all switch off our phones at 5:30pm.

Two is better than one

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In you live in a two-parent household, we found that having two people work part-time or flexible roles is better than the traditional breadwinner / houseperson split. In purely financial terms, in the UK, you can be 15-20% better off with this arrangement, based on tax rates and penalties regarding child benefit and childcare.

Having two people work part-time can also allow a career to be pursued to a lesser extent by both parties, rather than requiring the traditional sacrifice from one half of the household. This has benefits outside of pure employment. For example, the experiences of both parties are closer, which reduces resentment and facilitates understanding.

For the partner used to working long office hours, there are benefits to stepping off the pedal and taking on more of the unpaid household tasks. Dropping the kids off at school or childcare allows you to become involved in your local community, and move out of a workplace or professional bubble. It is very easy to get caught up in a world of work, but such a world is fairly brittle. If the unexpected or unwelcome hit, they often hit hard. Working flexibly or part-time allows you to build up social capital that can make you more resilient.

Bronnie Ware, an Australian palliative nurse, noted that one of the top regrets of dying male patients was “I wish I hadn’t worked as hard”.

There is life outside of London

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*A collective gasp is heard along Chancery Lane*

Live in or around London and London often seems like life. It is not. There are IP roles outside of London. Not all large businesses have their headquarters in London. There are private practice offices in most UK university towns.

Living outside of London could half your housing and living costs, with a smaller proportional drop in salary. You could move somewhere where you can walk or cycle to work. Work outside of London and you can avoid commutes that clock up as an hour each way with what seems like the entire population of Denmark. Yes, there is less culture, fewer venues, galleries and restaurants, but have three kids and you rather quickly end up going to bed at 9pm and a walk to the shops resembles a military exercise. You are also not confronted with raging inequality that takes mental effort to filter out and rationalise everyday.

Remote working works

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When I started in the profession we were only just beginning to receive instructions by email. This was seen as “something not quite right”. Post (or perhaps facsimile transmission) was “how things were done”. People shuffled between patent firms and the London post branch of the UK Patent Office with brown-wrapped parcels. Work literally piled up in front of you.

In over a decade much of this has changed. Working can often feel like the film Inception as I dive into virtual machines within virtual networks within virtual machines. You can file a patent application from anywhere in the world. You can receive all communications electronically. Online file and docketing systems manage files and due dates in the cloud. Everyone loves Microsoft Word (okay that one isn’t great).

One of the panel at the Women in IP event, and an ex-colleague, spent a year working as a patent attorney while circumventing the globe. I know of patent attorneys who work for UK firms that do not live in the UK.

As a slight caveat, I am relatively IT-savvy. But I was surprised at the ease with which I could work outside of the office on a laptop or computer. All you need is good WiFi. Do not be afraid.

Imperfect Balance

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Life often seems to me like a Necker Cube. Look at it one way and you are a total failure, miserably falling short of your true potential. Flip your frame of reference and you are a wild success, much better off than most of humanity, alive or dead. Both may be true. No one can know for sure.

You have to be realistic. I spend much more time with my kids that many dads. But this doesn’t mean we live in Von Trappian bliss. If anything I probably get cross at my kids more than the average bedtime or weekend dad. Much of my day can pass in domestic drudgery (washing, dishes, shopping, cooking, ferrying; then repeat). But I feel richer than a Partner in a London law firm.

Human beings have known for over two and a half millennia that truth and success lie in a middle way. But this is hard because our desires, inclinations and habits pull us to the poles. You need to exert effort to stay balanced. Like riding a bike. Knowing how to do this takes years and is a skill you acquire. It doesn’t arrive by magic.

Working flexibly, taking a career break or changing your career may need you to give up certain things. But you can gain other things that can provide a path of fulfilment that may not be visible in life’s melee.

My Route

Photo 20-09-2018, 17 03 15 — Note: Mess and the CIPA Journal

I started off my career on the well-trodden patent attorney path. In my last year of university I sent off my letters to most of the patent firms in the Inside Careers guide, and ended up being lucky enough to be accepted for a training place at Gill Jennings & Every (GJE). Things went pretty smoothly for the first few years: I had success on the QMW Diploma course and the European Qualifying Examinations, and was progressing nicely within the firm. I got married and we were looking at buying somewhere to live in Wood Green. Then life, as it does, threw a few curve balls.

First, the “Great Recession” hit. Suddenly work started to dry up, and recruitment froze. It didn’t feel like a huge event at the time, but in hindsight it was clear that a general feeling of uncertainty and chaos hung around. Second, a close friend tragically died. She was a high-flying trainee lawyer. Although officially unrelated, I couldn’t help feel that the all-night working sessions and London lawyer lifestyle were somehow implicated. Third, my first daughter was born. There were complications with the birth, which shall we say “didn’t go well”, and she ended up in Intensive Care for a couple of weeks with possible brain damage. Luckily she appears fine now, 8 or 9 years later, but at the time we didn’t know whether there were going to be learning difficulties or other problems growing up.

This led us to look to leave London and to move closer to my (relatively large) family that lived in central Somerset. Bristol and Bath both had patent attorney firms and I sent the feelers out amongst the recruitment consultants. I ended up at EIP in Bath. We lived between family in Somerset and a family friend’s flat in Bath until we were lucky enough to find a small terraced house in Bath (any spelling mistakes are due to the lead paint and asbestos tiles I removed – we’re still there now).

Relatively quickly I was back on the career track, although now one slightly askew from the London mainline. My second daughter came along and I was eyeing up partnership in the distance along the classic lines. We tried to juggle parenting as best we could but ended up falling back onto rather typical binary roles (me at work until 6-7pm many nights, the other half being the default for the kids). Just after the birth of my second daughter, my father-in-law died suddenly, which shook us up a bit. My partner had a relatively small family, which was now even smaller, whereas I was used to large families down in Somerset. We thus decided to have one more.

At this time we began to look again at our parenting roles. Neither I nor my partner were particularly enjoying the long stressful hours (which were actually fairly tame by London standards), and the standard career route for me was for this all to become worse (for at least 10 years working up through the lower rungs of partnership). My partner enjoys her work and wanted to do more of it, but this meant days away once or twice a month that I often had to take as annual leave. It just so happened that it was at this time that the shared parental leave scheme came into force. It looked perfect.

In the end I applied to take 7 months shared parental leave while my partner took 2 months. I also put in a flexible working request for my return, to go down to 3 days a week. Both were granted (there wasn’t much choice on the first). Work was really good about things but as the scheme was in its infancy I ended up helping with human resources on the form of the policy. The downside was that the pay was just statutory. This meant going down to around £550/month for 7 months. All non-essential bills were cut, all food shops were Lidl (and still are), and any bills that could be frozen were. (In hindsight I should have asked for some pay during this period, at least matching the maternity schemes and possibly going beyond, trading this for an agreement to work for a particular length of time on my return.)

In any case the shared parental leave worked fairly well. There was a lot less pressure on my partner than the previous two. I could do the night shifts and the sleep training (think waking up every hour throughout the night for several months) without collapsing at work the next day. There was no need to drag a baby to the school and nursery drop offs. The drop in income was mitigated slightly by the fact you can’t really go out or do anything with three kids.

Flexible working also worked, to a certain extent. It felt a little split-brain – three days a week life was back to pre-parental leave days, being in the office and dealing with the day-to-day; then two days a week, I was plunged into the world of chores, kid juggling, and dirty nappies. The cognitive dissonance is sometimes hard. I wrote a bit about it here. Flexible working allowed my partner to work longer or non-standard hours on the other two days.

As the sleeping began to improve I looked at moving up to working four days a week to get back onto a partnership track. However, after a few months we found this wasn’t really working. The kids each needed to be in different locations at varying times between 8:30am and 9:30am, and to be picked up at times that span from 12:30pm to 4:30pm. Working four days a week meant that the workload crept back up to a 8:30am to 6:30pm working day – with commuting time this meant I was out for much of the time the kids were awake. Parenting is a zero-sum game and so someone else needed to be there when I was not there. A nanny or childminder was not an option when we looked at our after tax salaries. My parents have nearly a decade before they retire and my partner’s mother lives three hours away. I started chatting to and reading about others in similar situations – both in real-life and online. Becoming a consultant for a consultancy firm seemed an option. I had two very useful conversations with ex-colleagues, they were a great help explaining how things could work. Also what was the partnership everyone was aiming at, if not being your own boss? I decided to bite the bullet and start my own consultancy business in December 2017. I now work preparing patent drafts and office actions for various clients on a job-by-job basis.

My current arrangement is the best I have. I have traded off income for time. I can now do school and nursery pick-ups and drop offs. My partner can spend days away without a PhD in logistics. The income-time trade-off isn’t as linear as it seems: e.g. being around more means I can spend an hour at 4pm cooking tea from simple ingredients so a meal for 5 costs £2.50 rather than £25, in effect saving £22.50 after-tax. This is found in many other areas. Another is that I now have no commute, so I don’t need to pay £7000 after tax per year just to work. I can also choose to work hard on billable jobs one week, then have less paid work the next week to “work” on enjoyable not directly billable tasks such as this blog post, or processing 292,000 G06 US patent specifications.

As a caveat. It is still early days. It could all go disastrously wrong. I hope it doesn’t.