Examining patent applications relating to artificial intelligence (AI) inventions: The Scenarios – GOV.UK

Introduction

1.This document contains a set of scenarios concerning inventions that involve artificial intelligence (AI) or machine learning (ML). It is designed to accompany the guidelines for examining patent applications relating to AI inventions. The guidelines are primarily concerned with the patentability of AI inventions in respect of the excluded matter provisions set out in section 1(2) of the Patents Act 1977.

2.The IPO considers that patents are available for AI inventions in all fields of technology. The scenarios in this document are intended to reflect and illustrate the wide range of diverse technical fields where AI inventions may be found.

3.Each scenario has a very brief description of how its AI invention works and an illustrative example of a patent claim. Each scenario includes a simplified assessment setting out our opinion on how each AI invention would likely be assessed in respect of section 1(2).

4.For the avoidance of doubt, we emphasise that this document is not a source of law. Our opinions on the patentability of the scenarios shall not be binding for any purpose under the Patents Act 1977.

5.The scenarios have been designed to focus on the question of excluded matter only. We have assumed that the claimed inventions are novel and non-obvious. We have also assumed that each scenario is sufficiently disclosed.

6.The assessments of excluded matter we give follow a simplified application of the four-step Aerotel approach. We have omitted detailed consideration of steps 1 and 2 of the Aerotel approach. Specifically: a. At step 1, we have simply assumed each claim is sufficiently clear and that no issues of construction arise. b. At step 2, we have simplified the assessment by stating what we consider is the actual contribution c. At steps 3 & 4, we have simplified the analysis by focussing in the main on the program for a computer exclusion of section 1(2). Unless otherwise indicated, our non-binding opinions are limited to this exclusion.

7.Any comments or questions arising from these scenarios should be addressed to:

Phil ThorpeIntellectual Property OfficeConcept HouseCardiff RoadNewportSouth WalesNP10 8QQ

(Telephone 01633 813745)Email: Phil Thorpe

Nigel HanleyIntellectual Property OfficeConcept HouseCardiff RoadNewportSouth WalesNP10 8QQ

Telephone 01633 814746Email: Nigel Hanley

Background This invention concerns a parking management system located in a car parking facility equipped with camera surveillance.

Images from the systems cameras are processed by a first neural network which is trained to detect a vehicle approaching an entrance of the facility. When the first neural network detects an approaching vehicle in an image, the image is passed to a second neural network to implement an Automatic Number Plate Recognition System (ANPR) system. The second neural network is trained to identify a specific number plate region in the image. A recognition module receives the identified number plate region and applies an optical character recognition algorithm to determine the registration number of the vehicle.

A number plate recognition system comprising:

an image capturing device positioned at an entrance to a parking facility;

a computing device for receiving images from the image capturing device and comprising:

a first neural network configured to detect a vehicle in a captured image;

a second neural network configured to receive an indication of the vehicle from the first neural network, detect the presence of a number plate in the image, and determine a region of interest in which the number plate is located; and

a recognition module configured to receive the region of interest and apply an optical character recognition process to the region of interest to determine characters of a registration number of the vehicle.

The contribution

Beyond a conventional surveillance system having a camera and a computer, the contribution made by the invention is:

a number plate recognition system using a first neural network to detect a vehicle in a captured image; a second neural network to detect the presence of a number plate in the image and to determine a region of interest in which the number plate is located, and a module to apply an optical character recognition process on the region of interest to determine the characters of the vehicles registration number.

The contribution is a number plate recognition system which is not excluded by section 1(2). Although the number plate recognition system is computer implemented, it is more than a program for a computer as such because it is carrying out a technical process external to a computer. The number plate recognition system includes a combination of two neural networks and a recognition module that specifically perform image processing operations which are technical in nature (see Vicom). The system has a technical effect in the sense of signpost (i). It self-evidently solves a technical problem relating to the recognition of vehicle registration plates, so signpost (v) would also point to allowability.

The claimed invention is not excluded.

Gas supply systems are complex systems that are monitored by multiple sensors located in the supply system and in its operating environment. Typically, data from sensors can be combined and analysed by an operator to provide the operator with an overview of the operational state, both of the individual components within the system and of the system as a whole. This may assist the operator in identifying faults within the system and options for reconfiguring the system.

However, it is acknowledged that this approach requires specialised skills on the part of the operator and is prone to error, especially when data from large numbers of interconnected sensors must be considered. In particular, understanding the interdependency of changes made to the system is challenging.

This problem has been recognised by the inventor, who has developed an artificial intelligence system to receive and categorise sensor data relating to a gas supply system, identify faults, and recommend system configuration changes to resolve the faults. In making recommendations the AI system will analyse the effect a configuration change may have on the system. The system may implement a recommended configurational change to the system using an automatic operational controller.

A computer-implemented method of managing the operating state of a gas supply system using sensors within the gas supply system and in its operating environment, and characterised in that the method comprises an AI system:

receiving and analysing data from the sensors;

identifying fault conditions within the gas supply system based on the analysis;

and

reporting the fault conditions and generating a recommended solution to an automated operational controller of the gas supply system.

The contribution

The contribution is managing the state of a gas supply system using an AI system that identifies fault conditions in the gas supply system, based on sensor data relating to the operation of the gas supply system, and reports the fault conditions and recommended solutions to an automated operational controller.

The contribution does not fall solely within the computer program exclusion. The contribution made by the invention is a solution to a technical problem lying external to the computer on which the AI system runs, namely the monitoring of the operation of an external technical system (a gas supply system) for fault conditions. This is a technical contribution. Signposts (i) and (v) point to allowability.

The invention defined in the claim is not excluded under section 1(2).

Analysing the motion of an object can be used to identify an activity. In some known examples, such as sporting events, analysing motion of an object can be useful for coaching. Alternatively, in gesture-based systems, a determined gesture can be used as a control mechanism or to issue an alarm. In one known example, a smoking cessation system generates an alarm in a wrist-worn device to deter the user from smoking.

Typically, these known systems operate by comparing real-time data to statistical models to determine motion, and they are heavily reliant on the accuracy of their statistical model. Consequently, systems that rely on the statistical models can be inaccurate.

The inventor has proposed a system that uses motion vectors derived from acceleration, velocity, and orientation in X, Y and Z directions as an input to a neural network to classify the motion. The system functions by receiving motion data in real time from a device such as a sports watch, or other motion sensors. The neural network processes the motion vector using a classification library to classify the motion to a particular movement.

A computer-implemented device for analysing motion comprising:

a controller having a data interface, a neural network, and a movement classification library;

sensors including a gyroscope, a magnetometer, and an accelerometer, wherein data from each sensor each is output to the controller via the data interface;

characterised in that the controller is operable to:

determine a motion vector from the received data; and

provide the determined motion vector to the neural network, wherein the neural network is configured to classify the motion vector as one of a particular movement in the classification library.

The contribution

The contribution is a device that determines a motion vector from data captured by its sensors (gyroscope, magnetometer, accelerometer) and uses a neural network and classification library to classify the motion vector as a movement from the library.

The contribution is not solely a program for a computer since its task is to perform a process of classifying measured sensor data describing the physical motion of a computing device. This process is a technical process lying outside the computing device and is carried out by technical means. It concerns the classification of real-world sensor data as a determined movement. Signpost (i) would point to patentability.

The invention defined in the claim is not excluded under section 1(2).

Cavitation in a pumping system is the formation of vapour bubbles in the inlet flow region of the pump, which can cause accelerated wear, and mechanical damage to pump seals, bearings and other pump components, mechanical couplings, gear trains, and motor components.

A pump system has a measuring apparatus adapted to measure pump flow and pressure data associated with the pump system. A classifier system detects pump cavitation according to the flow and pressure data. The classifier system comprises a neural network which is trained using back propagation. The measuring devices comprise sensors (1,2) for measuring input pressure and output pressure associated with an inlet (3) and an outlet (4), respectively, of the pump system. The flow through the pump is also measured.

1.A method of training a neural network classifier system to detect cavitation in a pump system, the method including the steps of:

correlating each of a plurality of measured pump flow and pressure data pairs with one of a plurality of class values, thereby producing a training data set, wherein each of the plurality class values is indicative of an extent of cavitation within the pumping system and at least one of the plurality of class values is indicative of no cavitation in the pump system; and

training the neural network classifier system using the training data set and back propagation.

2.A method for detecting cavitation in a pump system comprising:

measuring pump flow and pressure data;

detecting pump cavitation according to said flow and pressure data;

wherein the detection step includes providing said flow and pressure data as inputs to a classifier system using a trained neural network, wherein the neural network provides a signal indicative of the existence and extent of cavitation in the pump system, and updating said signal (6) during operation of said pump system.

The contribution

Beginning with claim 1, the contribution it makes is a computer-implemented method of training (i.e. setting up) a neural network classifier so it can detect cavitation in a pump system, where the method uses back propagation with a set of training data comprising measurements of pump flow and pressure from the pump system that are each correlated with a value indicating a corresponding extent of pump cavitation in the system.

Although the contribution relies on a computer program, it is more than a computer program as such. The contribution relates to a process of using physical data to train a classifier for a specific technical purpose, namely the detection of cavitation in a pump system. This is technical in nature. There is a technical contribution in the sense of signpost (i).

Claim 2 also reveals a technical contribution since it relates to the use of a trained classifier for the specific technical purpose of detecting cavitation in the pump system. A technical process lying outside the computer in the sense of signpost (i) is performed.

The invention defined in claims 1 and 2 is not excluded under section 1(2).

Many cars are fitted with catalytic converters to reduce the amounts of gases such as NOx and CO in their exhaust fumes. A problem for such converters is that their operational efficiency changes with the ratio of fuel-to-air in the combustion chambers of the engine. The fuel-to-air ratio must therefore be controlled to be maintained at a fixed value to maintain the efficient operation of the catalytic converter.

It is known to control the amount of fuel injected into an engines combustion chamber using feed forward control in relation to throttle position and additional feedback control in relation to an oxygen sensor (or air/fuel sensor) provided in the exhaust. Although this works well, it can be difficult to control the fuel-to-air ratio correctly when the engine is accelerating or decelerating.

The inventor has developed an injector control system which uses a trained neural network to determine an amount by which a given fuel injection amount should be adjusted during acceleration/deceleration to maintain a correct fuel-to-air ratio and thus maintain catalytic converteor efficiency. The neural network receives data inputs relating to the operational state of the engine, such as engine speed (RPM), intake air pressure, throttle position, fuel injection amount, air intake temperature, engine coolant temperature, and data from an exhaust gas sensor. The neural network outputs a signal indicating a change to the fuel injection amount for controlling the engine.

A computer-implemented neural network for adjusting the amount of fuel injected into a cylinder of a combustion engine, the neural network comprising:

an input layer having:

an input for receiving the RPM of the engine;

an input for receiving intake air pressure of the engine;

an input to receive current throttle position;

an input to receive the present injected fuel amount;

an input to receive air intake temperature;

an input to receive water cooling temperature;

an input to receive exhaust gas sensor data;

at least one hidden layer, wherein the hidden layer is connected to the input layer;

an output layer connected to the at least one hidden layer; and wherein

the output layer has an output indicating an amount by which the fuel injection should be changed.

The contribution

The contribution is a neural network that outputs a control signal relating to an amount by which fuel injection should be changed based on inputs relating to the operational state of the engine, as defined in the claim.

The contribution is a solution to a technical problem lying outside a computer, i.e. maintaining correct fuel-to-air ratio in an engine, so it is more than a program for a computer as such. The neural network takes as its inputs data representing the operating state of the engine and outputs a control signal indicating the amount by which a fuel injection amount should change. The control signal is suitable for controlling a technical process that exists outside of the computer on which the neural network runs. This is a technical contribution. Signposts (i) and (v) apply.

The invention defined in the claim is not excluded under section 1(2).

It is useful to measure the percentage of blood leaving each ventricle of a heart when determining the health of the heart. This measurement can be estimated by a skilled operator of an ultrasound imaging system by imaging a heart and marking out and measuring the boundaries of the ventricles of the heart at either extreme of a heartbeat. However, the accuracy of the operators estimate depends upon the operators skill and judgement.

The inventor has devised a method in which a trained neural network is used to provide a measurement of the percentage of blood ejected by a heart by analysing a series of images of the heart over a heartbeat. The neural network is trained using a supervised learning approach.

A computer-implemented method for determining a percentage of blood ejected from a given heart during a heartbeat, the method comprising:

training a neural network with heart imaging data sets, each set comprising imaging data of a ventricle over time and associated blood ejection percentages, the sets being associated with different hearts;

and using the trained neural network to:

receive a set of imaging data of a ventricle of the given heart;

output a percentage of blood ejection for the given heart.

The contribution

The contribution is a method of estimating a percentage of blood ejected from a heart by training a neural network with heart imaging data which has been labelled with blood ejection percentage, and then obtaining an estimate for the percentage of blood ejected from a given heart over a heartbeat by providing a set of images of that heart (over its heartbeat) to the trained neural network.

The contribution is more than a program for a computer as such because it relates to an improved measurement of the percentage of blood ejected from a heart during a heartbeat. This is a technical measurement of a physical system. This improved measurement is an example of a technical effect upon a process lying outside the computer that implements the invention, following signpost (i). This is a technical contribution.

The invention is not excluded under section 1(2).

Traders on a trading exchange monitor the performance of various stocks and tradeable instruments to try to identify opportunities to make a beneficial trade. It requires specialist knowledge, understanding, and experience to recognise and identify patterns and trends in the market. This means traders will often specialise in a narrow range of instruments e.g. energy shares, financial derivatives, or commodities.

The inventor has recognised that this can result in beneficial trades being overlooked. A trader may either miss a trading opportunity for instruments held as part of their position or a chance to a reduce a loss or increase a profit from a transaction. To assist the trader, the inventor has developed an AI that can identify patterns and correlations between share and instrument prices, identify trades based on recent performance and timing differences, and predict future behaviours. One advantage the AI offers is the opportunity to see connections that would be otherwise opaque and not obvious.

The AI is coupled to an automatic brokerage platform to allow it to execute trades according to profit/loss limits provided by the trader.

A computer-implemented financial instrument trading system comprising an exchange market, a broker terminal, an AI assistant, and an automated brokerage system, characterised in that the AI assistant is configured to:

receive current and historical price data for tradeable financial instruments from the exchanges;

cross reference combinations of financial instruments to identify correlated groups of instruments;

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Examining patent applications relating to artificial intelligence (AI) inventions: The Scenarios - GOV.UK