British Telecommunications Patente

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A computer-implemented method of selecting an overlay graphic placement location for display of an overlay graphic on a display device over an image sequence originating from a camera, the method comprising: obtaining overlay graphic data; obtaining object data indicating proximity of any objects in the image sequence to the camera at the time the image sequence was recorded; initiating a process to determine a placement score, based on the overlay graphic data and the object data, for each of a plurality of candidate graphic locations, the placement scores being determined in dependence on proximity data derived from the object data; and selecting the overlay graphic placement location in dependence on the placement scores.

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Some examples of a method and apparatus are described, including: for each of a plurality of time instants, receiving operation data, wherein the operation data comprises a current configuration of an operation and an output of the operation, where the operation is computer-implemented functionality for managing a communications network or for managing a service deployed in a communications network; determining a performance label associated with the output of the operation; generating behaviour data comprising the operation data and the performance label; and storing the behaviour data in a behaviour repository. The method comprises simulating the operation using the stored behaviour data; and triggering change to the configuration of the operation in response to output of the simulation.

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A computer implemented method, comprising: acquiring a plurality of images of a first scene captured from different viewing positions; training a 3D implicit neural representation model for the scene using the acquired images; wherein the training comprises rendering an image from the 3D model of the scene, the image being rendered as if captured from a first viewing position, comparing the rendered image with one of the acquired images taken from the first viewing position, and adjusting one or more parameters of the model in dependence on a result of the comparison; and the training further comprises rendering a 2D spatial image bearing 3D information, the 2D spatial image being rendered as if captured from a second viewing position, and adjusting one or more parameters of the model in dependence on the 2D spatial image.

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A method (400) of operating a telecommunications system (100), the method comprising the steps of: providing a Communication Node (120), CN, configured to: store an embedded Subscriber Identification Module (eSIM) profile for accessing a Cellular Telecommunications Network (130), CTN; and communicate with a User Equipment (110), UE, over a wireless local connection (180-1); communicating, from the CN, to the UE, over the wireless local connection, the eSIM profile (420); receiving, at the CTN, from the UE using the eSIM profile, alan: request for a service plan; identifier of the communicated eSIM profile; and unique identifier associated with UE, other than the identifier of the eSIM profile (430); determining, at the CTN, the UE to be (440): eligible to receive the service plan in response to identifying, as stored in the CTN, an absence of an association between the unique identifier with prior provision of the requested service plan by the CTN; and ineligible to receive the service plan in response to identifying, as stored in the CTN, an association between the unique identifier and prior provision of the requested service plan; and in response to determining that the UE is: eligible, the CTN permitting the identified eSIM profile to access to the requested service plan (460); and not eligible, the CTN preventing the identified eSIM profile from accessing to the requested service plan (450). Further methods and systems are disclosed.

The present disclosure relates to named-entity resolution and recognition, such as may be used to pre-process data for input to automated cybersecurity and telecommunications network management systems. Methods described herein relate to a data structure comprising: a hash table, referred to herein as an 'embedding hash table', consisting of one or more buckets, wherein: for each bucket of the embedding hash table, a corresponding key is a vector embedding of an entity name; and each bucket of the embedding hash table stores an entity type label associated with the corresponding entity name; and a data substructure storing all of the keys to the embedding hash table.

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A heat pump system and fluid distribution system are provided. The heat pump system comprises a heat exchanger installed in proximity to a portion of a fluid distribution system such that it affects the temperature of the fluid with that portion of the fluid distribution system. The fluid distribution system delivers fluid from one or more sources to one or more end users via one or more pipes. The fluid distribution system comprises a temperature controller configured to: receive measurements of the temperature of the fluid at one or more locations within the fluid distribution system; analyse the measurements to determine whether remedial action needs to be taken to control the temperature of the fluid within the fluid distribution system; and transmit a control signal to the heat pump system in response to determining that remedial action needs to be taken. The heat pump system further comprises a controller configured to: receive the control signal; and control the operation of the heat pump system in accordance with the control signal so as to control an amount of heat transferred between a refrigerant fluid within the heat exchanger and the fluid distribution system.

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Some examples of for training a neural network, wherein the neural network comprises a plurality of interconnected neurons, comprise receiving input data at a first neural network unit of the neural network and processing the input data using the first neural network unit to produce an output, a neural network unit comprising one or more of the neurons. Assign a first frequency to the first neural network unit and assign a second frequency associated to the second neural network unit. A difference value between the first frequency and the second frequency is determined and is used as an attention mechanism by, scaling the output of the first neural network unit in relation to the determined difference.