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: 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: 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.

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.