In recent years there has been a resurgence of interest in machine learning and so-called “artificial intelligence” systems. Much of this resurgence is based on advances in so-called “deep learning”, neural networks with multiple layers of connections. For example, convolutional neural networks now provide state-of-the-art performance in many image recognition tasks and recurrent neural networks have been used to increase the accuracy of many commercial machine translation systems. Machine learning may be considered a subdiscipline of “artificial intelligence” that deals with algorithms that are trained to perform tasks such as classification based on collections of data. This recent resurgence has meant that more companies wish to protect innovations in this field. This quickly brings them into the realm of computer-implemented inventions, and the nuances of protection at the European Patent Office.
“Computer-implemented invention” is the European Patent Office term for a software invention. Claims that specify machine learning and artificial intelligence systems are almost certainly to be considered “computer-implemented inventions”. The innovation in such systems occurs in the design of the algorithms and/or software architectures. Claims for new hardware to implement machine learning and artificial intelligence systems, such as new graphical processing unit configurations, would not be classed as computer-implemented inventions and would be considered in the same manner as conventional computer devices.
What Do We Have To Go On?
As key advances in the field have only been seen since 2010, there are few Board of Appeal cases that explicitly consider these inventions. It is likely we will see many Board of Appeal decisions in this field, but it is unlikely these will filter through the system much before 2020. However, applications in the field are being filed and examined. The following review is based on knowledge of these applications, evaluated in the context of existing Board of Appeal cases.
A first issue regarding machine learning and artificial intelligence systems is that many of the underlying techniques are public knowledge, given the rapid turn-over of publications and repositories of electronic pre-prints such as arXiv. Hence, many applicants may face novelty and inventive step objections if the invention involves the application of known techniques to new domains or problems. For patent attorneys who are drafting new applications, it is recommended to perform a pre-filing search of such publication sources and ensure that the inventors provide a full appraisal of what is public knowledge.
Domain of Invention
A second issue is the domain of the invention. This may be seen as the context of the invention as presented in the claims and patent description.
Inventions that apply machine learning approaches to fields in engineering are generally considered more positively by the European Patent Office. These fields will typically either operate on low-level data that represents physical properties or have some form of actuation or change in the physical world. For example, the following domains are less likely to have features excluded from an inventive step evaluation for being “non-technical”: navigating a robot within a three-dimensional space; dynamic adaptive change of a Field Programmable Gate Array; audio signal analysis in speech processing; and controlling a power supply to a data centre.
On the other hand, inventions that apply machine learning approaches within a business or “enterprise” domain are likely to be analysed more closely. These inventions have a greater chance of claim features being excluded for being “non-technical”. These domains typically have an aim of increasing profit. The more this aim is explicit in the patent application, the more likely a “non-technical” objection will be raised. For example, the following inventions are more likely to have features excluded from an inventive step evaluation for being “non-technical”: intelligent organisation of playlists in a music streaming service; adaptive electronic trading of securities; automated provision of electronic information in a company hierarchy; and automated negotiation of online advertising auctions.
Exclusions from Patentability
A third issue that arises is that individual features of the claims fall within the exclusions of Article 52(2) EPC. In the field of machine learning and artificial intelligence systems, there is an increased risk of claim features being considered to fall into one of the following categories: mathematical methods; schemes, rules and methods for performing mental acts or doing business; and presentations of information. These will briefly be considered in turn below.
The field of machine learning is closely linked to the field of statistics. Indeed many machine learning algorithms are an application of statistical methods. Academic researchers in the field are trained to describe their contributions mathematically, and this is required for publication in an academic journal. However, the practice of the European Patent Office, as directed by the Boards of Appeal, typically regards statistical methods as mathematical methods. In their pure, unapplied form they are considered “non-technical”.
Schemes, Rules and Methods for Performing Mental Acts
A claim feature is likely to be considered part of schemes, rules and methods for performing mental acts when the scope of the feature is too broad or abstract. For example, if a claimed method step also covers a human being performing the step manually, it is likely that the scope is too broad.
Schemes, Rules and Methods for Doing Business
Claim features are likely to be considered schemes, rules and methods for doing business when the information processing relates to a business aim or goal. This is especially the case where the information processing is dependent on the content of the data being processed, and that content does not relate to a low-level recording or capture of a physical phenomenon.
For example, processing of a digital sound recording to clean the recording of noise would be considered “technical”; processing row entries in a database of information technology assets to remove duplicates for licensing purposes would likely be considered “non-technical”.
Presentation of Information
Objections that features relate to the presentation of information may occur when the innovation relates to user experience (UX) or user interface (UI) features.
For example, a machine learning algorithm that adaptively arranges icons on a smartphone according to use may receive objections on the grounds that features relate to mathematical methods (the algorithm) and presentation of information (the arrangement of icons on the graphical user interface). As per Guideline G-II, 3.7.1, grant is unlikely if information is simply displayed to a user and any improvement occurs in the mind of the user. However, it is possible to argue for a technical effect if the output provides information on an internal state of operation of a device (at the operating system level or below, e.g. battery level, processing unit utility etc.) or if the output improves a sequence of interactions with a user (e.g. provides a new way of operating a device). Again, a technical problem needs to be demonstrated and the machine learning algorithm needs to be a tool to solve this problem.
Subfields of ML and AI
In certain subfields of machine learning and artificial intelligence, there is a tendency for Boards of Appeal and Examining Divisions to consider inventions more or less “technical”. This is often for a combination of factors, including field of operation of appellants, the history of research and traditional applications, and the background and public policy preferences of staff of the European Patent Office.
For example, machine learning and artificial intelligence systems in the field of image, video and audio processing are more likely to be found to have “technical” features that can contribute to an inventive step under Article 56 EPC. A convolutional neural network architecture applied to image processing is more likely to be considered a “technical” contribution that the same architecture applied to text processing. Similarly, it may be argued that machine learning and artificial intelligence systems in the field of medicine and biochemistry have “technical” characteristics, e.g. if they operate on data originating from mass spectrometry or medical imaging.
However, advances in search, classification and natural language processing are more likely to be found to have “non-technical” features that cannot contribute to an inventive step under Article 56 EPC. These areas of machine learning and artificial intelligence systems are often felt to be “technical” by the engineers and developers building such systems. However, it is a nuance of European case law that these areas are often deemed to have claim features that fall into an excluded “business”, “mathematical” or “administrative” category.
A recent example may be found in case T 1358/09. The claim in this case comprised “text documents, which are digitally represented in a computer, by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document”. The Board agreed with the appellant that the steps in the claim were different to those applied by a human being performing classification. However, the Board concluded that the algorithm underlying the method the claim did not “go beyond a particular mathematical formulation of the task of classifying documents”. They were of the opinion that the skilled person would have been given the (“non-technical”) text classification algorithm and simply be tasked with implementing it on a computer.
What Should We Not Do?
Managers and executives of commercial enterprises are often habituated into selling innovations to a non-technical audience. This means that invention disclosures often describe the invention at an abstract “marketing” level. When an invention is described in a patent application at this level, inventive step objections are likely.
The fact that mathematical formulae may comprise excluded “non-technical” features is difficult for inventors and practitioners to grasp. Often equations at an academic-publication level are included in patent specifications in an attempt to add technical character. This often backfires. While such equations may be deemed “technical” according to a standard definition of the term, they are often not deemed “technical” according to the definition applied by European case law.
In general, objections are more likely in this area when the scope of the claim is broad and attempts to cover applications of a particular algorithm in all industries. Applicants should be advised that trying to cover everything will likely lead to refusal.
What Should We Do?
Chances of grant may be increased by ensuring an examiner or Board of Appeal member can clearly see the practical application of the algorithm to a specific field or low-level technical area.
Patent attorneys drafting patent applications for machine learning and artificial intelligence systems should carefully consider the framing and description of the invention in the patent specification. In-depth discussions with the engineers and developers that are implementing the systems often enable innovations to be described more precisely. Given this precision, innovations may be framed as a “technical” or engineering innovation, i.e. a technical solution to a technical problem. This increases the chance of a positive opinion from the European Patent Office.
Often features of an invention will have both a business advantage and a “technical” advantage. For example, a machine learning system that learns how to dynamically route data over a network may help an online merchant more successfully route traffic to their website; however, this improved method may involve manipulation of data packets within a router that also improves network security. A patent specification describing the latter advantage will have a greater chance of grant than the former, regardless of the actual provenance of the invention. A practitioner may work with an inventor to ensure that initial business advantages are distilled to their proximate “technical” advantages and effects. For cases where data does not relate to a low-level recording or capture of a physical phenomenon, it is recommended to ensure that any described technical effect applies regardless of the content of the data.
When considering exclusion for “mental acts”, a risk of a “non-technical” objection may be reduced by ensuring that your method steps exclude a manual implementation. Note that this exclusion does not necessarily prevent other objections being raised (see T 1358/09 above).
When drafting patent applications, it is also important to describe the implementation of any mathematical method. In this manner, pseudo-code is often more useful than equations. It is also important to clearly define how attributes of the physical world are represented within the computer. Good questions to ask include: “What data structures and function routines are used to implement the elements of any equation?”, “How is data initially recorded, e.g. are documents a scanned image such as a bitmap or a markup file using a Unicode encoding?”, “What programming languages and libraries are being used?”, or “What application programming interfaces are important?”.
Practitioners do need to be concerned with including overly limiting definitions within the claims; however, a positive opinion is more likely when specific implementation examples are described in the patent specification, followed by possible generalisations, than when specific implementation examples are omitted and the description only presents a generalised form of the invention along with more detailed mathematical equations.
To be successful in search, classification and natural language processing, one approach is to determine whether features relating to a non-obvious technical implementation may be claimed. This approach often goes hand in hand with a knowledge of academic publications in the field. While such publications may disclose a version of an algorithm being used, they often gloss over the technical implementation (unless the underlying source code is released on GitHub). For example, is there any feature of the data, ignoring its content, which makes implementation of a given equation problematic? If inventors have managed to reduce the dimensionality of a neural network using clever string pre-processing or quantisation then there may be an argument that the resultant solution is implementable on mobile and embedded devices. Reducing a size of a model from 3 GB to 300 KB by intelligent selection of pipeline stages may enable you to argue for a technical effect.
Do Not Believe The Hype?
Despite the hype, machine learning and artificial intelligence systems are just another form of software solution. As such, all the general guidance and case law on computer-implemented inventions continues to apply. A benefit of the longer timescales of patent prosecution is that you ride out the waves of Gartner’s hype cycle. In fact, I still sometimes prosecute cases from the end of the dotcom boom…