The present invention relates to a computer implemented system for animating a virtual object or digital entity. It has particular relevance to animation using biologically based models, or behavioural models particularly neurobehavioural models. There is provided a plurality of modules having a computational element and a graphical element. The modules are arranged in a required structure and have at least one variable and being associated with at least one connector. The connectors link variables between modules across the structure, and the modules together provide a neurobehavioural model. There is also provided a method of controlling a digital entity in response to an external stimulus.

The present invention relates to a computer implemented system for animating a virtual object or digital entity. It has particular relevance to animation using biologically based models, or behavioural models particularly neurobehavioural models. There is provided a plurality of modules having a computational element and a graphical element. The modules are arranged in a required structure and have at least one variable and being associated with at least one connector. The connectors link variables between modules across the structure, and the modules together provide a neurobehavioural model. There is also provided a method of controlling a digital entity in response to an external stimulus.

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

A cognitive learning device includes inputs with each including an input path having a transistor device having a storage capacity. A circuit is responsive to the inputs and selects an input set in accordance with a current task, wherein the input set selected modifies a characteristic of the transistor device of one or more corresponding input paths to bias the input set for selection for subsequent accesses.

The description relates to artificial intelligence assisted wearables, such as backpacks. An example backpack may include sensors, such as a microphone and a camera. The backpack may receive a contextual voice command from a user. The contextual voice command may include a non-explicit reference to an object in an environment. The backpack may use the sensors to sense the environment, use an artificial intelligence engine to identify the object in the environment, and use a digital assistant to perform a contextual task in response to the contextual voice command. The contextual task may relate to the object in the environment. The backpack may output a response to the contextual voice command to the user.

The description relates to artificial intelligence assisted wearables, such as backpacks. An example backpack may include sensors, such as a microphone and a camera. The backpack may receive a contextual voice command from a user. The contextual voice command may include a non-explicit reference to an object in an environment. The backpack may use the sensors to sense the environment, use an artificial intelligence engine to identify the object in the environment, and use a digital assistant to perform a contextual task in response to the contextual voice command. The contextual task may relate to the object in the environment. The backpack may output a response to the contextual voice command to the user.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus, and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain/cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus, and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain/cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

An autonomous chat bot monitors actions of users on a messaging platform and generates self-initiated chat sessions with the user to gauge users' interest and intent with respect to a target subject matter and the conversations of the chat sessions. Based on the gauged interest and intent, profiles or preferences are generated for the users independent of or relevant to the target subject matter. In an embodiment, customer contact information for the users are provided by the autonomous chat bot to a Customer Relationship Management (CRM) system for further engaging the customer with respect to the target subject matter or other subject matters determined to be relevant from the profiles or preferences.