Emotional Perception AI Ltd v Comptroller-General of Patents, Designs and Trade Marks
| Jurisdiction | England & Wales |
| Judge | Sir Anthony Mann |
| Judgment Date | 21 November 2023 |
| Neutral Citation | [2023] EWHC 2948 (Ch) |
| Court | Chancery Division |
| Docket Number | Case No: CH-2022-000144 |
[2023] EWHC 2948 (Ch)
Sir Anthony Mann
Case No: CH-2022-000144
IN THE HIGH COURT OF JUSTICE
CHANCERY DIVISION
BUSINESS AND PROPERTY COURTS OF ENGLAND AND WALES
ON APPEAL FROM THE UK INTELLECTUAL PROPERTY OFFICE
Royal Courts of Justice, Rolls Building
Fetter Lane, London, EC4A 1NL
Mark Chacksfield KC and Henry Edwards (instructed by Hepworth Brown) for the Appellant
Anna Edwards-Stuart (instructed by The Comptroller-General of Patents, Designs and Trade Marks) for the Respondent
Hearing dates: 5 th and 6 th July 2023; further submissions received on 22 nd and 27 th September 2023
Approved Judgment
This judgment was handed down remotely at 10.00am on 21 st November 2023 by circulation to the parties or their representatives by e-mail and by release to the National Archives.
Introduction
The Patents Act 1977 section 1(2)(c) excludes from patent protection “a program for a computer … as such”. The courts have had to grapple from time to time with the difficulties of this concept in relation to what I can call traditional computers and software. This appeal raises new questions because it involves deciding whether the use of an aspect of Artificial Intelligence, namely an Artificial Neural Network (“ANN”), in the circumstances of this case, engages the exclusion. I am told that this issue has not yet arisen in any of the authorities. A hearing officer in the UK Intellectual Property Office, Dr Phil Thorpe, decided that it did and therefore refused grant of the proposed patent in question, in a decision dated 22nd June 2022 (BL/O/542/22) (“the Decision”). The applicant for the patent, Emotional Perception AI Ltd, appeals that decision. On this appeal Mr Mark Chacksfield led for the appellant; Ms Anna Edwards-Stuart appeared for the respondent, the Comptroller General of Patents.
It needs to be understood that this appeal concerns the exclusion only. It does not, for example, deal with any sufficiency points which might arise, or any other questions going to validity save for briefly referring to the “mathematical method” exclusion in the same sub-section. I also point out that in this judgment I use the expression “computer program” as a synonym for “a program for a computer”, because it is shorter, but at all times I have the actual statutory wording in mind.
The field of the patent and the claims
In this section I shall describe the invention. In the next section I describe what an ANN is and how it works. For the purposes of understanding this section it can be envisaged as a black box which is capable of being trained as how to process an input, learning by that training process, holding that learning within itself and then processing that input in a way derived from that training and learning.
The applied for patent is said to provide an improved system for providing media file recommendations to an end user, including sending a file and message in accordance with the recommendation. A typical field of use is music websites, where a user may be interested in receiving music similar to another track of which he/she knows or already has. Existing websites are capable of offering similar pieces in, say, the same category (rock, heavy metal, folk, classical and so on) but the categorisation tends to be limited to types of music. The categorisation is derived from human tagging, the playlists of others and the like, which tend ultimately to derive from a human being's classification. The advantage of the proposed patent is said to be that it is able to offer suggestions of similar music in terms of human perception and emotion irrespective of the genre of music and the apparently similar tastes of other humans, and to arrive at such suggestions by passing music through a trained ANN which does the categorisation in that respect.
The two principle claims of the patent are set out in the Appendix to this judgment.
Claim 1 is a product by process claim, and claim 4 is the process. It was common ground that for these purposes there was no material difference between them and that I can work from either claim for the purposes of this judgment so far as necessary. The Hearing Officer worked from Claim 4.
Some simplified explanation is required at this stage of the way in which the claim works. Since there was no dispute about that I can reduce it to more everyday terms in the following manner without resort to the wording of the rest of the application. The invention is said to be applicable to various media, including music, images and text, but for the purposes of explanation the parties have considered music as a typical, perhaps the most likely, usage example. I shall do the same. The explanation which follows is generalised and not complete, but it is sufficient to provide the context for the issues in the case.
A useful starting point is the training of the ANN and to assume for the moment that the ANN itself is a hardware system (as opposed to a software emulation). A pair of music files is taken, each of which is accompanied by a natural language description of some sort of the type of music in its file in terms of how that music is perceived by a human. At its simplest the music might be described as happy, or sad, or relaxing, though the descriptions will be more complicated and wordy than that. The descriptions are in word form (hence the use of the word “semantic” and its derivatives which are used to describe this sort of feature of the music) and are to be analysed by an ANN via natural language processing software. An ANN is given instructions which enable it to assimilate the characterisation of the tracks and produce a vector or co-ordinates in a notional space (the semantic space) based on the type of music for each of the items in the pair. The similarity or difference between the semantic types of music is reflected by the distance between those two vectors (co-ordinates) in the semantic space. Two tracks of music which are semantically similar will have co-ordinates closer together; the farther apart they are in similarity the farther apart their vectors (co-ordinates) will be.
At the same time the same two tracks are analysed in another ANN (via parameters set by a human) for what are described as its physical properties — tone, timbre, speed, loudness and a lot of other characteristics set by the human (I am deliberately avoiding calling him/her a programmer for the moment). That analysis produces vectors (coordinates) in a notional “property space” (or “property embedding space” in claim 1), again with the differences or similarities in the music thus assessed reflected in the proximity of the co-ordinates. This is the ANN which will be the final operative ANN in the system.
The next step is a significant “trick” in the invention. The second ANN is trained to make the distances between pairs of the property co-ordinates converge or diverge in alignment with the distancing between them in the semantic space. Thus if the property space co-ordinates are farther apart than those in the semantic space, they are moved closer together, and conversely if the distancing is too close together in the property space to reflect semantic dissimilarity. This training is achieved by a process called back-propagation in which the “error” in the property space is corrected in order to make the results coincide with the training objectives. The back-propagation is done via an algorithm provided by a human and the correction is achieved by the ANN's adjusting its own internal workings in such ways as adjusting weighting and bias in its nodes and levels of assessment. It learns from the experience without being told how to do it by a human being.
This training process is repeated many times with many pairs of tracks and the ANN learns, by repetitive correction, how to produce property vectors whose relative distances reflect semantic similarity or dissimilarity. This goes on until it is assessed that the ANN is getting it right, at which point it is “frozen” and ready to perform its intended function. It can now provide a single vector in property space for any given track of music which will have a degree of semantic similarity to other files (tracks) which is reflected in their relative property vector proximity. Similar semantic styles will be reflected in the property vectors being closer together; dissimilar styles will be reflected in the vectors being farther apart. The ANN has learned how to discern semantic (dis)similarity from physical properties. It has not done so because any human (programmer) has told it how to do it. It has done it by producing results, being provided with information reflecting its degree of error, adjusting its own internal assessment parameters, reprocessing the files to reduce the error and repeating this process until it gets it sufficiently right sufficiently often.
The ANN is now ready to take any given track of music provided or proposed by a remote user, determine its physical properties and attribute a property or physical vector to it. It can then relate that vector to the vectors of files in an overall database from which it is to make recommendations, and can ascertain music which is semantically similar by looking for tracks with proximate physical vectors and make a recommendation of a similar track from those nearby vectors. It does this by sending a message and a file to the remote user.
The advantage of this over other systems for providing recommendations of similar music to users is described in the Decision in the following terms, which are not disputed on this appeal:
“49. At this point it is helpful to turn to the main piece of prior art identified by the examiner on the basis of the...
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