Microsoft Technology Licensing, LLC

Deepinder S Gill

Vipindeep Vangala

Govind Saoji

Ananthatejas Raghavan

Snehdip Karandikar

Nithin Kumar Mara

Rajasekhar Vinnakota

Rashi Anand

Ravi Gupta

Ankur Hayatnagarkar

Nitin Pande

Sravanth Venkata Madhu Kurumaddali

Srinivasa Varadhan Thirumalai-Anandanpillai

Systems and methods for contextual memory capture and recall are provided. The contextual memory capture and recall systems and methods help a user create, store, and recall memory information associated with an identified activity. The contextual memory capture and recall systems and methods are capable of identifying user activities where a memory inquiry may be desirable, creating a memory inquiry with a recommended memory action based on the activity, providing the memory inquiry to the user, and automatically linking an accepted memory action, along with any received memory information for the memory action, with the identified activity.

Systems and methods for contextual memory capture and recall

A graph data structure for an artificial intelligence knowledge base includes a plurality of user-centric facts associated with a user. Each user-centric fact has an application-agnostic data format and includes a subject graph node, an object graph node, and an edge connecting the subject graph node to the object graph node. The graph data structure is designed to accommodate facts from different application-specific data providers using the same application-agnostic data format even when the different application-specific data providers use different native data formats.

User-centric artificial intelligence knowledge base

Systems and methods for creating and/or using an artificial intelligence memory system that models human memory are provided. The AI memory system creates and/or uses a user centric memory graph. The user centric memory graph implicitly links memory elements of a user utilizing relationships created in space, time, and cognitive dimensions similar to how the human brain stores and recalls different memory elements. The user centric memory graph is used by searching and/or constraining the user centric memory graph based on a determined user context and/or a user query.

Systems and methods for building and utilizing artificial intelligence that models human memory

Systems and methods for intelligent memory sharing and contextual retrieval across multiple devices and multiple applications are provided. The systems and methods do not just show a user what he or she has stored across his or her different devices but intelligently suggests relevant topics and/or information based on what is contained in a shared working memory compiled from the temporary memories on all of the user devices.

Systems and methods for an intelligent distributed working memory

Intelligent capture, storage, and retrieval of information for task completion are provided. A user can explicitly request to capture information, or user information is implicitly captured from one or more data streams. Semantic understanding of captured information is provided, wherein offline processes are run that analyze captured information to bucket them into a set of pre-defined high value scenarios. Whenever a capture falls into such scenarios, additional structured information about the capture is extracted. Further, aspects provide for storing and indexing information in a data repository. Users are enabled to explicitly retrieve the stored information using a natural language interface, or stored information can be proactively provided to the user based on context to enable task completion.

Intelligent capture, storage, and retrieval of information for task completion

In non-limiting examples of the present disclosure, systems, methods and devices for providing contextualized SMS data from one or more SMS messages to a client are presented. SMS data from SMS messages may be filtered and relevant information from the filtered SMS data may be extracted. The extracted information from the SMS data may be categorized into one or more contextual categories in a tiered contextual content hierarchy. The categorization of the SMS data may include analyzing world knowledge related to the extracted information and associating searchable context metadata with the categorized information. An indication to provide feedback related to the SMS data may be received and feedback related to the received SMS data may then be provided to the client.

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Hyderabad Telangana, India

Deepinder S Gill