Microsoft Technology Licensing, LLC

Other

Vipul Agarwal

Imed Zitouni

Omar Zia Khan

Paul Anthony Crook

Marius Alexandru Marin

Ruhi Sarikaya

Jean-Philippe Robichaud

Alexandre Rochette

Soumya Batra

Mohammad Makarechian

Karthik Tangirala

Rahul Kumar Jha

Nikhil Holenarsipur Ramesh

Hisami Suzuki

Shankaranand Arunachalam

Anshul Dube

Vasiliy Radostev

Andrew Burke Ryan

Brian Alan Day

Billy Goethetaro Norcilien

Virat Satyam Mishra

Kimberly Gail Del Rosario Cabamalan

Jeff Todd Kizner

Connor David Copus

Ankita Mishra

Seyed Mohammad Kiaei

A computer-implemented technique is described herein for expediting a user's interaction with a digital assistant. In one implementation, the technique involves receiving a system prompt generated by a digital assistant in response to an input command provided by a user via an input device. The technique then generates a predicted response based on linguistic content of the system prompt, together with contextual features pertaining to a circumstance in which the system prompt was issued. The predicted response corresponds to a prediction of how the user will respond to the system prompt. The technique then selects one or more dialogue actions from a plurality of dialogue actions, based on a confidence value associated with the predicted response. The technique expedites the user's interaction with the digital assistant by reducing the number of system prompts that the user is asked to respond to.

Expediting interaction with a digital assistant by predicting user responses

A batching system accesses user interaction data to identify relationships between users and between users and resources. The relationships are weighted and users are grouped for migration based upon the weighted relationships. The groups are displayed for administrator interaction and are provided to a migration system for migration of the users between two tenants. This enhances migration efficiency and accuracy and makes migration much less disruptive to the end users.

Extracting user interactions to generate batches for migration between tenants

Systems and methods are disclosed for task identification and tracking using shared conversational context. In one implementation, a first communication from a first user is received within a communication session. The first communication is processed to identify a first content element within the first communication. A second communication is received within the communication session. The second communication is processed to identify a second content element within the second communication. The first content element is associated with the second content element. Based on an association between the first content element and the second content element, a task is identified. An action is initiated with respect to the task.

Task identification from electronic user communications using a shared conversational context

Conversational understanding systems allow users to conversationally interface with a computing device. In examples, a query may be received that includes a request for execution of a task. A data exchange task definition may be accessed. The data exchange task definition assists a conversational understanding system in managing task state tracking for information needed for task execution. Using the data exchange task definition, a per-turn policy for interacting with the user computing device is generated based on the state of a dialogue with a computing device and an evaluation of a process flow chart provided by a task owner resource. The task owner resource may be independent from the conversational understanding system. A response to the query may be generated and output based on the per-turn policy. In examples, the per-turn policy is used to generate one or more responses during a dialogue with a user via a computing device.

Policy authoring for task state tracking during dialogue

Described herein is a conversation engine that can be used in a system such as a personal digital assistant or search engine that combines a dynamic knowledge graph built during execution of a request and one or more static knowledge graphs holding long term knowledge. The conversation engine comprises a state tracker that holds the dynamic knowledge graph representing the current state of the conversation, a policy engine that selects entities in the dynamic knowledge graph and executes actions provided by those entities to move the state of the conversation toward completion, and a knowledge graph search engine to search the static knowledge graph(s). The conversation is completed by building the dynamic knowledge graph over multiple rounds and chaining together operations that build toward completion of the conversation. Completion of the conversation results in completion of a request by a user.

Knowledge graph for conversational semantic search

Aspects herein provide third party application authors with a user interface authoring platform that automates and simplifies a task definition process while also providing the ability to leverage pre-existing language understanding models and canonicalization and resolution modules that are provided by the operating system on which the CU system resides or as provided by other third parties. In particular, the present disclosure provides a method and system for authoring a task using a user interface authoring platform.

Natural language task completion platform authoring for third party experiences

Systems and methods are disclosed for personalized communications using semantic memory. In one implementation, a first communication is received from a user and processed to identify a first content element within the communication. The first content element is associated with a second content element within a content repository. A second communication that includes the first content element is received from the user. Based on an association between the first content element and the second content element within the content repository, a third communication that includes the second content element is generated and provided to the user in response to the second communication.

Personalized communications using semantic memory

Assigning tasks and monitoring task performance based on context extracted from a shared contextual graph

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Bellevue, WA, United States of America

Vipul Agarwal