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.

Growth Accelerator

I have recently come across the Growth Accelerator scheme. They are on Twitter as @GrowthAccel. It looks potentially useful for small to medium sized businesses in England.

It is a public/private business coaching service for high growth companies. It is mainly funded by the government via the Department of Business, Innovation and Skills. To be eligible a company has to be UK registered, have up to 250 employees and a turnover of under £40 million. The coaching can fit with existing programmes and aims to help achieve high growth (~20% year-on-year for 2-3 years – but flexible for each company). There is a cost which is dependent on size: up to 9 people – £600; 10-49 people – £1,500; and 50-249 – £3,000.

One of their offerings is help and advice on commercialising innovation. This can involve the preparation of an intellectual property (IP) Audit. This generally covers the IP owned by a company but can be tailored for particular situations. For example, one company may want it to be directed at a planned new product launch, whereas another may want a more general overview. The selling point for companies is the UK Intellectual Property Office pays. This is worth at least the cost of the scheme for a larger size business, notwithstanding the business support coaching, advice and guidance.

As the cost is not particularly onerous and the scheme is heavily subsidised, it might be useful for growing smaller companies or for projects in larger companies (e.g. new product launches). Larger companies such as Huddle and The Fabulous Bakin’ Boys are part of the scheme and so it should not be thought of as directed towards the smaller end of the business spectrum.

I recommend that anyone who is interested check out their website where you can apply online.

Sustainability, Innovation and Creativity at TEDxBristol

On 8 September 2011 I attended TEDxBristol, an independently organised TED conference showcasing local leaders in the fields of sustainability, innovation and creativity. The event was held at Bristol’s newly-opened Mshed in a waterfront area that is quickly becoming a creative hub for digital industries.

The theme for the event was the World Around Us. A particular strength of Bristol enterprise is the ability to work both at a global and local level.

Wendy Stephenson, a renewable energy engineer, described how The Converging World helped a small Somerset village, Chew Magna, build wind turbines in India to offset their carbon emissions.
Tony Bury, a philanthropist and serial entrepreneur, explained the difference a mentor can make. His charity, The Mowgli Foundation, matches mentors with entrepreneurs in South West UK, Jordan, Lebanon and Syria.

The Innovation session saw talks from, amongst others, Bloodhound SCC, the Nanoscience & Quantum Information (NSQI) Centre at the University of Bristol and inventor Tom Lawton.

Richard Noble of Bloodhound CC entertainingly explained how they found themselves inspiring the next generation of engineers as part of their efforts to obtain a Eurofighter jet engine. The Bloodhound Education Programme involves over 2,410 primary and secondary schools, 176 further education colleges and 33 universities. Their journey to build a 1000mph car is now gathering pace, with tests due to begin in just over a year. They are aptly based just behind the SS Great Britain in Bristol Docklands.
Professor Mervyn Miles presented some of the Centre’s current research including some mind-bending work on a holoassembler. This device use optical traps of focused near infra-red radiation, positioned in space via a dynamic hologram, to assemble microscopic, and even nanoscopic, structures. Researchers have combined this technology with a multi-touch interface to create a system that would not look out of place in a science fiction film.
Tom’s talk provided a fascinating insight into the ups and downs of a private inventor. Over the last 10 years Tom has worked on a 360 degree camera for capturing immersive images (a “BubbleScope”). Tom explained his journey from his initial inspriation while travelling to his current iPhone pre-production accessory.

The event also featured preformances that built upon another of the West Country’s strengths: an ability to combine technology and the arts to produce truly original creations.

nu desine, a young start-up from Bristol, showed off their AlphaSphere musical instrument. Their presentation also produced one of my favourite quotes from the event: “I don’t rap, I’m an electronic engineer”.
David Glowacki, a theoretical chemist at the University of Bristol, demonstrated his Danceroom Spectroscopy project. This fuses theoretical Feynmann-Hibbs molecular dynamics simulations with a 3D imaging camera to allow the motion of dancers to warp the external forcefields felt by the simulated particles. The result is projected onto a screen with collisions mapped onto a muscial output. View it here.
Tom Mitchell and Imogen Heap ended the day with a demonstration of Tom’s musical gloves. These gloves allow wearers to manipulate music using just hand gestures. The result is unbelievable; you can watch the performance here.

In all, the day was a resounding success. A big thank you to Karl Belizaire, the event orgnaiser and his team. Hopefully those that attended were inspired to create their own impact in Bristol and the wider World Around Us.

PS: I was very impressed with the designs and doodles from the event – Nat Al-Tahhan lead the design work; check out here blog and event doodles here.

How is your Investability?

On 29 March 2011 I braved the traffic on the Bristol bypass to attend Investability at HP Labs. Investability is a one-day event held by Bristol and Bath Enterprise Network (BEN) for entrepreneurs, investors and anyone else with an interest in the funding of early stage technology businesses.

The highlight of the event for me was a keynote speech by Mike Southon who presented on what entrepreneurs could learn from arguably the most successful entrepreneurs in recent times: the Beatles (accompanying slides: here; Mike was arguably cheating though by being a professional speaker and motivator). Takeaway points included:

The five “p”s of a successful elevator pitch: Pain, Premise, People, Proof and Purpose;
The golf-club test: can your investor explain your idea to his mates at the golf club and get them to chip in?
Get a foil: i.e. find a team or partner who complements your skills and abilities, such as Paul and John. To paraphrase Mike, if have the technology brains but the thought of social functions leaves you dry, find a “talky, shouty” person to promote and sale your product or service.

Among those representing public support institutions, the atmosphere was bittersweet. The Regional Development Agencies (RDAs) and their associated programs were on their way out, as part of the political changes brought in by the new coalition government, but the new Bristol and Bath Science or S-Park was slowly taking form in what was a field in Emerson’s Green, Bristol. Excitement for the S-Park is gaining momentum, helped by the news that the UK’s National Composites Centre will also be housed on the site.

In the wake of the shutdown of a number of regional support agencies, many had the question: who would support business and innovation in the UK? One answer came from Dr. James Clipson of the Technology Strategy Board (TSB). The TSB are a Swindon-based organisation who offer a number of options for research and development funding in the UK. They offer three different R&D grants for single organisations:

Proof of market grant;
Proof of concept grant; and
Development of prototype grant.

These are available to pre start-ups, start-ups, and small and medium-sized businesses from all sectors across the UK. Applications are assessed throughout the year, but it is recommended to get application in as soon as possible: the vim and vigour (as well as the finance) instilled by the TSB’s new lease of life under the present government may suffer fatigue as the year passes. Plus funding is now awarded nationally (previously it was distributed through the RDAs), so you are in effect competing nationally. However, I was surprised to learn that up to 75% of applicants for low-level early (e.g. feasibility) grants receive funding and the competitions come with generous legal terms (e.g. any IP rights reside in the company receiving funding). This makes the TSB definitely worth a look for established SMEs who are looking to fund tentative market research projects.

Sean Smith of the University of Bath’s Technology Transfer unit also explained how Britain’s universities were available to help SMEs with innovation. Sean had a background in industry and so spoke with some understanding of the gulfs that sometimes exist between commerce and academia. He explained though that efforts on both sides to realistically understand the aims and goals of those involved in research projects could facilitate fruitful relationships.

I was pleasantly surprised by the number of angel and early stage investors who attended the event. In a session of three talks on the investment process the audience was roughly one third angel investors, one third entrepreneurs and one third others (e.g. me). Gonzalo Trujillo and Nathan Guest both delivered interesting overviews of the investment process, respectively from a finance and legal perspective. Takeaway points included:

An investor needs to be right for a company as much as the company needs to be right for the investor. Often at a long-term relationship will arise and it is important for the financial success of the company that this works.
As Gonzalo joked, an investment is much like a marriage: there is a lot of initial flirting and anxiety to work to a consummation (of a deal), but do not let this distract you from the real challenge: living together afterwards.
Nathan remarked on the need for initial legal paperwork, but warned not to get too bogged down in pages of text. An early situation is fluid – enough needs to be in place to allow a deal to proceed but exact terms can evolve and be added throughout the relationship.
Nathan also reminded attendees to carefully check the ownership for IP rights: conditions vary from right to right and depend on a person’s role, it is best to engage a professional to ensure that all valuable rights reside with the legal entity you are investing in.

The event ended with a look ahead to Venturefest on November 3 (a big event in the new S-Park) and the nascent Local Enterprise Partnership (more information: here, follow them on Twitter: @WofEnglandLEP).

Big thanks to BEN, including Martin Coulthard and Becky Smithson, and sponsors, including HP Labs, for a productive and informative event.

BEN: Green Profits

Bristol and Bath Enterprise Network (BEN) is a network for the technology business community based around Bristol and Bath. They hold a series of events designed to foster profitable connections between individuals and companies in the region.

I was recently lucky enough to attend one of their events in Bristol: Green Profits.

Green Profits

The key question for this event was: can commercial and environmental success be aligned? To help answer this question two case studies were presented.

ModCell Logo from Website Linda Farrow talked about how her innovative architecture and design practice, White Design, allowed them to develop a new building module, Modcell, for the rapid construction of sustainable (and even carbon-negative) buildings. The Modcell is a brilliant idea: a prefabricated flat pack wooden frame is assembled and filled with compressed straw bales before being coated with lime render to form a wall panel. I was pleased to see that Modcell have a granted UK Patent GB2457891B of a broad scope and are moving forward with protection worldwide. This will help ensure that they can adequately capitalise, from a commercial perspective, on the (literally) years of design and testing that went into the product. Both White Design and Modcell are a fine example of the kind of technological and commercial thinking that can help address the seemingly intractable problems of climate change and dwindling resources. Also being a Somerset lad, I am always glad to see straw used in novel ways. Take a look at White Design’s website for examples of the kind of buildings where this technology may be used in the future. If you are familiar with Bristol and the South West you will probably recognise their structures.

Friska Logo from Friska Website Linda was followed by Griff Holland of Friska Food, a take-away/restaurant/sandwich shop on Victoria Road in Bristol. Griff offered a different perspective on sustainable business practices. Even though food is a rather low-tech industry, Griff had important lessons on how to make clients and customers feel good about themselves by incorporating sustainable (or “green”) thinking into the heart of the business. An illuminating example was how customers liked to sort their recycling; at first allowing this would seem somewhat counter-intuitive – surely customers do not want the extra burden? However, what Friska observed was that customers had become used to sorting recycling from their homes and felt good about making some contribution, however small, to offset their impact on the world. A similar example was provided with fridges – by having doors on the fridges much electricity was saved and customers were happy opening and closing the doors (as opposed to open fridges), especially if they were told their slight inconvenience was good for the environment. Friska had big plans for growth over the coming years; more outlets like them are only a good thing for a sustainable UK.

The case studies were followed by a presentation by Amy Robinson, Network Director of Low Carbon South West (LCSW) who explained the myriad of accreditations and organisations to support (and somewhat confuse) business in the South West. LCSW are a trade association with a mission to promote growth in environmental technologies and sustainable services in the South West of England. They are the recent offspring of what was Bristol Environment Technology Sector Initiative (BETS) and a Low Carbon group from Bath University. Amy explained how LCSW are happy to offer assistance to individuals and companies as to how to navigate this confusing landscape and gain the right advice and services from the right people.

A lesson that emerged from the presentations and resulting round-table discussions was the need for long-term thinking and investment coupled with effective client/customer communication. Laying the groundwork for a sustainable business unfortunately often involves higher initial investment (and possibly higher product cost). However, this investment may pay off several years down the line when you find the market shifts and your business turns from fringe player into market leader, with your competitors struggling to retrofit their own practices to keep up. In a way this reminded me of Clayton Christensen’s Innovator’s Dilemma, wherein the disruptive innovation may be sustainable practices. Effective communication then becomes important to attract and maintain early adopters in the market that are may be better placed to afford the initial green /cost trade-off. A given example was that of the premium hotel market – they were prepared to pay a little extra for sustainable printing as this paid off in terms of better marketing for the hotel.

The next BEN Event is a free allday event: Investability at HP Labs Bristol on 29 March 2011.

The Third Culture Sparkles in Bath

BathSpark is a new event that brings together the people of Bath who work in technology. Its stipulated aims are “to create opportunity, encourage diversity and spark new ventures”. It is described as “social networking (for real) with local digital people enjoying a drink, and talking about what they love”. It grew out of previous informal tech people meet ups under the organising eye of The Filter’s CEO, David Maher Roberts.

The inaugural meet-up was held on 23 February 2011 in the Market Bar (http://www.marketbath.com/) and was a roaring success. Around 100 people attended and there was a great range of people eager to chat about all things tech/creative-based, from start-up CEOs to coders to publishers to freelance types. It was good to see many dialogues between the two cultures. Some good contacts were made and information exchanged. I think many were surprised at the level of underground activity in the area. Many found it useful for locating talented Bath individuals for entrepreneurial projects. Comments from attendees can be found here: http://www.bathspark.co.uk/events/16327966/?eventId=16327966&action=detail#comments.

Eden ventures kindly sponsored the bar, Future Publishing provided a magazine subscription prize and RipeDigital (http://www.ripedigital.co.uk/) did an amazing job producing laminated name cards (complete with Twitter usernames). Even though the ale ran out the bottles and pizza kept flowing to fuel the conversation.

The next meet-up is scheduled for 23 March 2011 but the date is not set in stone yet. 57 attendees have already signed up.

Bath: Software City?

Bath. Well known for this kind of thing:

Not, however, so well known for this kind of thing:

While the professional, scientific and technical, and information and communications, industries make up 13% of Bath’s workforce, they contribute 27% of the total gross-value-added (GVA) contributions (see page 24 of BANES’s Economic Strategy publication). The future of this area was the subject of a talk at the Octagon, Bath as part of the Treasure & Transform exhibition. The Chimp was in attendance (any factual errors in the reportage are my own, for which I and my hastily iPhone-typed notes apologise).

The Panel

The talk was in the form of a panel discussion chaired by Simon Bond, Director of Bath Ventures Innovation Centre (University of Bath). The panel featured the cream of Bath’s software industry: Paul Kane, CEO of CommunityDNS, Richard Godfrey, Director and Founder of i-Principles and Shaun Davey, CEO of IPL.

What Makes Bath Good for Software?

First up was a discussion of the attributes that make Bath attractive for software and high-tech companies. This included:

Excellent local universities (e.g. Bath & Bath Spa) providing a stream of talented people.
Great quality of life, especially for families.
Unique concentration of creative skills, e.g. in publishing, design and the arts.
A culture of quality, of building things well.
A good (if not affordable) transport link into London.
A concentration of affluent people, e.g. for investment.

What Difficulties do Businesses in Bath Face?

Next, the talk moved on to the difficulties faced by software and high-tech companies in Bath. These included:

Transport problems; e.g. high cost of train travel, difficulty getting into the center of the city; unpredictabiltiy of traffic; congestion and pollution.
Lack of office space suitable for software firms. Many offices were converted houses which were not suitable for the kind of open plan offices needed to allow productive brainstorming and idea sharing.
Communications infrastructure: outside of Bath University’s JANET link high-bandwidth connections were rare (although we were told BT are looking to install fibre in Kingsmead, mainly for residential customers). Also poor wi-fi (and 3G) coverage.
Cost of housing (I wholeheartedly agree with this one!): this prices out talented young people, e.g. between 18 and 40; who find other areas more affordable.

How Can Bath Improve?

Having looked at the difficulties, ways in which Bath could improve its position were discussed:

Stop non-city-centre traffic from needing to pass through the city centre (this would need lobbying of the Highways Agency on a national level).
Lobby BT/Virgin to upgrade communications infrastructure. Innovative ideas such as laying fibre along the river/canal tow-path or in existing drains and/or providing wi-fi from the hills were put forward.
Develop world-class conference venues. International conferences were a great way for Bath to attract talented people and become a thought leader (this was thanks to Muir Macdonald, MD of BMT Defence Services).
Continue the recent co-operation between the public and private sectors; consultations were always welcomed.
Use SMEs in Bath for local services; there are many companies in Bath that outsource services to London-based firms when local equivalents exist.
Increase promotion of high-tech industires in Bath. High-tech industry has often been the poor relation to the tourism industry when it comes to press coverage and marketing, which needs to change.

My Two Cents

Comparisons were made with Old Street (so-called “Silicon Roundabout”) and Cambridge (so-called “Silicon Fen”). I have worked in (and with) software and high-tech comapnies in both these locations. Bath can learn certain lessons from these areas:

Bath needs more innovation centres. The current Innovation Centre is at capacity and the awkward location in the bow of the river in the city centre is not great for commuting. BANES has earmarked the Bath City Riverside (where I will soon live) as sites with potential for development. These industrial areas on the edge of the city (Twerton / Newbridge), with easy road access, would make ideal (and cheap) sites for incubation centres that would solve the office space problems discussed above. Cambridge have recently opened their Hauser Forum in a similar edge-of-city location, which complements sites such as St Johns Innovation Centre (where I have worked). These buidling provide more than office space, they enable like-minded talented young people to mingle and connect, building a network effect that results in real growth and GVA.
The panel discussed attracting highly-educated, young 18-30-somethings to Bath to work in the high-tech industries. It was amusing listening to the ideas put forward, because the Chimp falls exactly within that demographic. For the Chimp the greatest difficulty coming to Bath is a lack of affordable family housing; if you were employed in Bath on a good yet (business-wise) affordable salary you would not be able to afford any 3-bed property outside of the estates in the south-west of the city. There are more affordable properties outside of Bath, but this means you will need to drive into Bath, and thus face the traffic problems discussed above. Here real brave thought and leadership is required, not only from the public sector, but from the more conservative and outspoken Bath public who often put a blanket ban on talk of development.

Summary

I will leave you with a corruption of Paul Kane’s ad-libbed marketing soundbite:

High-tech businesses in Bath thrive.

That they continue to thrive will be down to a continuation of the brave public-private partnerships and investments exemplified by the Treasure and Transform exhibition and seminars.

Image thanks to electricinca / Flickr

Image: Idea go / FreeDigitalPhotos.net

Quick IP Related Stories from the Economist

There were a few IP-related stories in this week’s Economist:

Innovation in Asia: Trading Places – How China is about to overtake Japan in patent applications.

Financing Small Business: The Mother of Invention – Innovative new funding models step in when the banks retreat (see http://www.fundingcircle.com/).

Books and arts: Well, what a good idea! – The release of two interesting books on innovation.

Can You Patent Disruptive Technology?

I am currently, rather belatedly, reading The Inventor’s Dilemma by Clayton M. Christensen. The author introduces the concept of sustaining and disruptive innovations.

To paraphrase Clayton:

Sustaining innovations are those that are aligned with an organisation’s value network; e.g. the demands of customers. Disruptive innovations are those that have benefits outside of the value network; e.g. for which no demand or market can be easily located. Disruptive innovations are a step downward in terms of profit margins and valued properties. However, over time disruptive innovations can rapidly move upwards and supplant existing technologies. This can catch-out organisations that are otherwise profitable and well-managed. In fact, Clayton argues that it is good management itself that causes companies to ignore disruptive innovations; disruptive innovations are not pursued inside organisations as they do not sustain traditional growth.

Reading this I wondered how the theoretical framework fitted with patent law. In particular, I saw an overlap with concepts of inventive step:

In patent law, patents are said to belong to a technical field. The size of the field is dependent on the scope of the claims. A narrow field could be seen to be a value network, whereas a broad field could be seen to span several value networks. Using the examples in the book, 3.5″ disk drives could be a narrow field and disk drives per se a broad field.

A “surprising or unexpected effect” is fast becoming a prerequisite to demonstrate inventive step (even though under European patent law it has traditionally been a secondary indicator). Sustaining innovations are aligned with well-defined demands in a value network. It would be more difficult to prove the “surprising or unexpected effect” of a sustaining innovation in a narrow field because there would be motivation for a skilled person to look for innovations that satisfied known demands. On the other hand, it would be easier to show such an effect for disruptive innovations, in that these often go against known demands.

Under European practice, to demonstrate an inventive step you typically need to show that a technical problem is being solved. With a disruptive innovation, uses and markets are not defined at the point of innovation. A period of trial and error is required. Hence, there may not be a well-defined problem at the point of innovation and it may be more difficult to demonstrate the inventive step of a disruptive innovation to a European Examiner.

The British courts are often hesitant to use a problem-solution approach for inventive step. It thus may easier be patent disruptive innovations in the UK.

If organisations are able to predict the uses and advantages of disruptive innovations, patenting those innovations may provide a level of insurance against upwardly mobile competitors. Many disruptive innovations are developed in-house but not developed. If they are also patented in-house, then such patents may be used as commercial leverage to slow the assent of a start-up that develops the innovative technology. I expect such an approach would be unpalatable to those who dislike the patent system.

On the other hand, many start-ups who pursue disruptive innovations consist of ex-in-house engineers. If these entrepeneurial engineers can successfully obtain the rights to their innovations, they may be able to obtain the required levels of funding to allow a market to be found. Without the safety net these rights provide, they may not be able to take the time to find a suitable market.

Four short links

Nominees for European Inventor Award 2010 : http://www.epo.org/topics/news/2010/20100308.html [No UK nominees with Germany having a presence in all categories apart from “Non European countries” – does this simply reflect a skew in the entries received or a real difference in innovation? Discuss.]

Sam Glover of Lawyerist reviews the Fujitsu Scansnap S1300: http://lawyerist.com/fujitsu-scansnap-s1300-portable-document-scanner/ [compare with his recommended S1500 – http://lawyerist.com/review-scansnap-s1500-document-scanner/].

Amazon One Click Shopping Patent survives re-examination – http://www.patentlyo.com/patent/2010/03/amazon-one-click-patent-slides-through-reexamination.html .

“Top” 25 US Patent Firms 2010 according to Intellectual Property Today: http://iptoday.com/issues/2010/03/top-patent-firms.asp .