Microsoft Technology Licensing, LLC

Christian Liensberger

Moe Khosravy

Roger Soulen Mall

Rene Jacco Bouw

Lukasz Gwozdz

Nikrouz Ghotbi

Ziv Kasperski

Marcus A Ash

René Jacco Bouw

Stéphane Nyombayire

Gregory David Swedberg

Jonathan E Hamaker

Varsha Mahadevan

Shankar Lakshmi Regunathan

Kunal Mukerjee

Michael D Plumpe

Venkatesh Krishnan

Adam Donald Wilson

Elisa Marie Flasko

Matthew Roche

Santosh Balasubramanian

Henrik Frystyk Nielsen

Erik Meijer

Hen Fitoussi

Patrick John Baumgartner

Chairy Chiu Ying Cheung

Anthony Nino Bice

Harris Syed

Keith Coleman Herold

Christina Storm

Mark Wade Heninger

Erez Altus

Piotr Milosz Puszkiewicz

Maxim Uritsky

Ori Kashi

Paul S Bloom

Murat Soyupak

James Eldon Tillinghast

Pauline Chao

Qunshu Zhang

Techniques are described herein that are capable of performing intent-based scheduling via a digital personal assistant. For instance, an intent of user(s) to perform an action (a.k.a. activity) may be used to schedule time (e.g., on a calendar of at least one of the user(s)) in which the action is to be performed. Examples of performing an action include but are not limited to having a meeting, working on a project, participating in a social event, exercising, and reading.

Intent-based scheduling via digital personal assistant

Intent-based calendar updating via digital personal assistant

Techniques are described herein that are capable of providing extensibility with regard to a context-aware digital personal assistant. For instance, a description of a capability of a target (e.g., a software target) may be received. Examples of a target include but are not limited to an application (e.g., a software application), a service, a bot, and a website. In a first example, a digital personal assistant may be caused to perform operation(s) based at least in part on a context of a user. In a second example, whether the digital personal assistant performs operation(s) that are configured to be triggered by first aspect(s) of the context of the user is controlled based at least in part on second aspect(s) of the context of the user. The operation(s) include notifying the user of the capability and/or implementing the capability on behalf of the user.

Extensibility for context-aware digital personal assistant

Various aspects for scaling an availability of information are disclosed. In one aspect, a response performance associated with responding to data consumption requests is monitored. A characterization of the response performance is ascertained, and a scaling of resources is facilitated based on the characterization. In another aspect, a data consumption status indicative of data consumed is ascertained. Here, a scalability interface is provided, which displays aspects of the status, and receives an input from a content provider. An allocation of resources is then modified in response to the input. In yet another aspect, a response performance associated with responding to data consumption requests is monitored. An application programming interface (API) call is generated based on a characterization of the response performance, and transmitted to a content provider. An API response is then received from the content provider indicating whether a scaling of resources for hosting the data was performed.

Availability management for reference data services

Organizations may comprise a variety of resources that are assigned to various organizational units, and allocated for various projects or tasks. A user who wishes to understand the organizational structure of the resources may examine information sources, such as resource manifests and resource access logs, and interpret such information with the user's knowledge of the organization, but may fail to achieve a comprehensive, accurate, and up-to-date understanding of the organization. Instead, interactions among users and resources may be monitored to infer organizational structure; assignment of resources to organizational units; relationships among resources; and usages arising within such resource relationships. Based on such inferences, an interactive visual map may be automatically generated that depicts the arrangement of the organization and the interrelationships of the resources. A user may interact with the visualized resources, such as changing the time range or level of detail, or selecting resources to view usage information.

Inference-based visual map of organizational structure and resource usage

Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Facilitating interaction among digital personal assistants

Obtaining a single unified result for a query over a plurality of data sources. A query specifies an identity of a type of data that the query is providing, and an identity of a format and type of data the query is looking for as a specific answer to the query. The query is mapped to a query pattern from among a plurality of available query patterns. A set of two or more different data sources are queried using one or more queries that correspond to the query pattern. At least two results of querying the particular set of two more different data sources are received. A single unified result is derived from at least one of the results. The single unified result has the format and type of data query specified that the query was looking for as a specific answer to the query.

Scenario based insights into structure data

Context-aware digital personal assistant supporting multiple accounts

Methods, systems, and computer-readable media are disclosed to generate an interface for a web service and to query the web service. A method includes generating a user interface associated with a web service. The user interface includes one or more input parameter options associated with the web service. The user interface is generated based on a service specification and one or more series specifications associated with the service specification. User input is received via the user interface, where the user input includes a selection of at least one of the one or more input parameter options. A query of the web service is generated based on the user input, and the query reflects at least one selected input parameter option.

Web service interface and querying

The present invention extends to methods, systems, and computer program products for forming data responsive to a query. Responding to a query can include selecting data views on top of data sources. A data catalog stores data elements from different data sources and stores links between data elements from the different data sources. A view can be selected for a query based on one or more of: classification of data included in the data catalog or prior usage of data included in the data catalog. A selected data view is populated with data elements from the data catalog using the links to determine that data elements spanning multiple data sources satisfy the query. The data catalog can be used to identify relationships between data elements from different data sources that would not otherwise be identified.

Forming data responsive to a query

Techniques and systems are disclosed for context-dependent speech recognition. The techniques and systems described enable accurate recognition of speech by accessing sub-libraries associated with the context of the speech to be recognized. These techniques translate audible input into audio data at a smart device and determine context for the speech, such as location-based, temporal-based, recipient-based, and application based context. The smart device then accesses a context-dependent library to compare the audio data with phrase-associated translation data in one or more sub-libraries of the context-dependent library to determine a match. In this way, the techniques allow access to a large quantity of phrases while reducing incorrect matching of the audio data to translation data caused by organizing the phrases into context-dependent sub-libraries.

Automatic learning of language models

Growing community of inventors

Bellevue, WA, United States of America

Christian Liensberger