Abstract: A method and system to generate vectors that represent linearly progressing entities like time are disclosed. Traditional methods of vectorisation account for semantic or associative similarity of the entities. Thus, vectors conveying semantic information do not convey structural relations between such entities. The method allows for the representation of such structural information, for example the months in a year. The vectors generated by the invention encode this relation between the months such that one can interpret the sequence of the months, the difference between then and their cyclic nature. The method works in a manner similar to a genetic code, where subsequent “child” vectors are generated by related “parents”, thus encoding the similarity and the distance of the sequential entities. An object of the inventions to allow algorithms in machine learning to easily learn over temporal entities its natural text.

Abstract: A method and system are disclosed for a novel architecture in which competing suggestions, possibly generated by competing systems, are selected by a Cognitive Unit (CU). The CU observes the user context and learns which contextual circumstances affect the user's cognitive behaviour. Majority of the traditional models trained over multiple users fail to represent the individual because (1) they ignore personal bias toward certain decisions and (2) they don't have complete visibility of all options available to users (i.e. from competitive systems). The invention is ideally suited to interact with several other products as more and more modern products are using AIs to drive the user experience. That shifts traditional HCI towards a novel form of interaction that we call human-AI interaction (HAII). When applied to user experience, predictive models make decisions on users' behalf attempting to minimise user interaction while guiding them toward the completion of predefined funnels.

Abstract: Embodiments of the present invention are directed to computer-implemented methods and systems for automatically generating a description of a task or expectation. The method comprises receiving, in the form of an electronic communication, a natural language sentence that expresses a call or commitment to action; generating, using a machine learning model, a description of a task or expectation for a user based on the natural language sentence; and storing the description of the task or expectation in a non-transitory computer-readable memory.

Abstract: Embodiments of the present invention are directed to computer-implemented methods and systems for representing human or machine-controllable sources, including, but not limited to, cloud services and smart devices, with a Semantic Interface Definition Language (SIDL) so that such resources can be listened to and eventually remote-controlled by discovering and performing Situationally Suitable Actions (SSA). Embodiments of the present invention are further directed to a novel user experience that allows sources to augment each other, removing the need to switch back and forth from different user interfaces (UIs) and overall facilitating or automating the execution of chains of commands.

Abstract: Embodiments of the present invention are directed to a computer-implemented machine-learning method and system for automatically creating and updating tasks by reading signals from external data sources and understanding what users are doing. Embodiments of the present invention are directed to a computer-implemented machine-learning method and system for automatically completing tasks by reading signals from external sources and understanding when an existing task has been executed. Tasks created are representable and explainable in a human readable format that can be shown to users and used to automatically fill productivity applications including but not limited to task managers, to-do lists, project management, time trackers, and daily planners. Tasks created are representable in a way that can be interpreted by a machine such as a computer system or an artificial intelligence so that external systems can be delegated or connected to the system.

Abstract: A computer-implemented machine-learning method and system for searching for resources by predicting an intention and pushing resources directly to users based on the predicted intention. The method includes receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; and generating a sorted list of resources matching the weighted list of expressions. The system includes computer instructions for an intention inference engine and an intelligent ranking engine.