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In September 2022, the UK Intellectual Property Office (IPO) published guidelines setting out the practice within the IPO for the examination of patent applications for inventions relating to Artificial Intelligence (AI).
This guidance follows the government’s response to a call for views on AI and intellectual property, which committed the IPO to publish these enhanced guidelines for the examination of patent applications for AI inventions. Our article reporting on the response to the call for views can be found here.
AI guidance is provided in two parts. A first document found here sets out the legal framework for examination of AI inventions and how this will be applied by the IPO. A second document found here helpfully provides a number of example AI inventions along with a simplified assessment of each invention to provide an opinion as to whether each invention is patentable.
The documents combine to provide useful guidance with practical examples as to how AI inventions are to be examined by the IPO.
A summary of the published guidance is provided below.
What is an AI invention?
For the purposes of the guidance the IPO has categorised AI inventions generally into two main categories:
Other aspects of AI are considered in addition to the above since AI inventions require training. AI models or algorithms may be trained using specific training datasets, for example. More information on training and datasets is provided below.
Exclusions to patentability
UK patent law excludes from patent protection inventions relating solely to a mathematical method, a computer program and/or a business method.
AI inventions typically rely on mathematical methods and computer programs in some way. Despite these exclusions from patentability, AI inventions may constitute patentable subject matter if a task or process performed by an AI invention makes a technical contribution to what is already known.
When does an AI invention make a technical contribution?
The guidelines broadly consider that an AI invention may make a technical contribution, and therefore be allowable, if:
(1) a task performed by a program represents something specific and external to the computer and does not fall within one of the exclusions from patentability. In this context, and more specifically, an AI invention makes a contribution that is technical in nature if its instructions:
(2) a program that solves a technical problem relates to the running of computers generally to make the computer work better. In this context, a program can make a technical contribution if its instructions:
(3) a program’s instructions define:
Patentable AI example scenarios considered to make a technical contribution
| AI Category | Scenario | Scenario title | Contribution | Reasons for allowability |
| Applied AI | 1 | ANPR system for recognising a vehicle registration number | Performing image processing operations using neural networks for recognition of vehicle registration plates | AI invention performing external technical processes and solving an external technical problem |
| Applied AI | 2 | Monitoring a gas supply system for faults | AI system monitors operation of a gas supply system for fault conditions | AI invention performing external technical processes and solving an external technical problem |
| Applied AI | 3 | Analysing and classifying movement from motion sensor data | A neural network processes a motion vector to classify real-world sensor data as a determined movement of a computing device | AI invention performing external technical processes |
| Applied AI | 4 | Detecting cavitation in a pumping system | Use of physical data to train a classifier system (neural network with back propagation) to detect cavitation in a pump system | AI invention performing external technical processes |
| Applied AI | 5 | Controlling a fuel injector in a combustion engine | Maintaining correct fuel-to-air ratio in an engine using neural networks to output a control signal to change a fuel injection amount | AI invention performing external processes and solving an external problem |
| Applied AI | 6 | Measuring percentage of blood leaving a heart | Improved measurement of percentage of blood ejected from the heart by providing a set of images of the heart to a trained neural network | AI invention performing external technical processes |
| Applied AI | 10 | Cache management using a neural network | Improving how the memory hierarchy in a computer works by using a neural network to select an optimum removal algorithm for removal of data from the cache | AI invention making the computer work better and solving technical problem in the computer |
| Applied AI | 11 | Continuous user authentication | Detection of malicious intrusion by repeatedly monitoring the characteristic usage of the computer system by a user based on behaviour scores calculated by machine learning model | AI invention making the computer work better and solving a technical problem lying within the computer |
| Applied AI | 12 | Virtual keyboard with predictive text entry | Improved virtual keyboard where a recurrent neural network predicts and ranks words most likely to be entered next and allows for user selection of predicted words | AI invention making the computer work better and solving a technical problem relating to a computer |
| Core AI | 16 | Processing a neural network on a heterogeneous computing platform | Operating a neural network by sharing processing load of neural network layer and controlling clock frequency to alter timing of processing completion | AI invention that includes a process of operating a computer in a new way in a technical sense |
| Core AI | 17 | Special purpose processing unit for machine learning computations | Improving the computational efficiency of existing processing units and operating computer in a new way using an address index to control an array of processing elements | AI invention making the computer work in a new way and solving a technical problem relating to a computer |
| Core AI | 18 | A multiprocessor topology adapted for machine learning | Using a new topology and data exchange method that optimises the performance of the machine learning task on a distributed computer system | AI invention making the computer work in a new way and solving a technical problem relating to a computer (distributed system) |
Non-patentable AI example scenarios
Scenarios 7 to 9 provided as part of the guidance are examples of “applied” AI inventions that are considered not to make a technical contribution and therefore are not patent eligible. Invention examples in these scenarios concern automated financial instrument trading, analysis of patient health records, and identification of junk e-mail using a trained classifier. These are considered excluded from patent protection because they relate to a business method or a computer program that merely processes information or data without revealing a technical contribution.
Scenarios 13 to 15 are examples of “core” AI inventions considered not to make a technical contribution. These scenarios concern optimisation of a neural network, avoiding unnecessary processing using a neural network, and active training of a neural network. These are considered excluded from patent protection because they relate solely to a computer program that does not solve a technical problem or merely processes information or data without revealing a technical contribution.
Training AIs and datasets
For the purposes of the guidelines, methods of training and other machine learning methods may also be categorised as “applied” AI or “core” AI, and they should be examined in the same way as described above.
Inventions relating to training AIs are compared to calibration since technical devices or functions may require calibration before they can be used accurately for their intended technical purpose.
Computer-implemented methods of calibration are patentable if they make a technical contribution. And by analogy, a method of training an AI model or algorithm for a specific technical purpose may also be considered patentable.
A dataset may be considered patentable by virtue of it being an integral feature of a patentable training method/algorithm or in the method of generating or improving the dataset, where the method makes a technical contribution. The dataset itself (dataset characterised by its content or delivery) is unlikely to be patentable.
Scenarios 4, 6, 11 and 18 provide examples of patentable inventions to training AI models or algorithms. An example of a training method not revealing a technical contribution (i.e. not patentable) is set out in scenario 15.
Sufficiency
The guidelines reinforce the importance of describing the AI invention, including the training dataset, in sufficient technical detail so that the invention can be performed by someone skilled in the art without undue burden. This has been set out in UK case law, Eli Lilly v Human Genome Sciences [2008] RPC 2, and the IPO considers these principles to be consistent with a recent decision of the European Patent Office’s (EPO) Board of Appeal, T 0161/18.
In reason 2.2 of decision T 0161/18 the Board stated that:
the application does not disclose which input data are suitable for training the artificial neural network according to the invention, or at least one dataset suitable for solving the present technical problem. The training of the artificial neural network can therefore not be reworked by the person skilled in the art and the person skilled in the art therefore cannot carry out the invention.
It should be noted that, in T0161/18, the training of the neural network was a feature of the claim and required to differentiate from the prior art. The requirement for a sufficient disclosure of the claimed invention is a fundamental requirement of patent law that applies equally to AI-related inventions.
Conclusion
These guidelines provide a useful overview of the IPO’s practice on assessing AI inventions by combining legal guidance with a number of scenarios of AI inventions to demonstrate the possibility of patent protection for AI innovation in the UK.
We look forward to applying the guidance while we can continue to assist our clients in obtaining valuable protection for their AI inventions.
Category: News | Author: Lloyd Palmer, Nikesh Patel | Published: | Read more
