Microsoft Technology Licensing, LLC

Patrick John Baumgartner

Pedram Faghihi Rezaei

Amir Netz

Peter Eberhardy

LuMing Wang

Christopher Alan Hays

Mark Xiaohong Yang

Wei Huang

Drew Voegele

Joseph Milan

John Hancock

Xiaohong Mark Yang

Jason W Burns

Zhenyu Tang

Adam Donald Wilson

Scott Heimendinger

Alisson A S Sol

Amer Shahnawaz

Zaiwei Du

Malik Hussain

Jin Wang

Matthew J Longley

Cristian Petculescu

Bongshin Lee

Christian Liensberger

Eran Megiddo

Shahar Prish

Greg R Smith

Amy Nicole Forstrom

Diego M Oppenheimer

Varsha Mahadevan

Alisson Sol

Sachin Patney

Jason Malcolm

Jingxiao Wu

Allison Tran

Aaron Meyers

Greg Smith

Matt J Longley

File exploration is facilitated by enabling zoom with respect to a thumbnail as a function of an identified point of interest. More particularly, a scaled thumbnail of the same size as a thumbnail can be presented as a function of an identified point of interest. Furthermore, navigation, among other things, is enabled to allow panning with respect to a scaled thumbnail, for instance.

Thumbnail zoom

The modifying of a natural language interpretation model for interpreting natural language queries. The system discovers modifications that one or more queriers made to one or more original query results of one or more natural language queries to generate one or more modified query results. The system then uses the discoveries to identify one or more changes to a natural language interpretation model that would result (given the same natural language queries) in one or more query results that more accurately reflect the one or more modified query results. The system the causes the natural language interpretation model to be modified with at least one of the one or more identified changes. Accordingly, over time, the natural language interpretation model may learn from observations of its own performance.

Observation-based query interpretation model modification

Presenting data from different data providers in a correlated fashion. A first query is performed on a first data set controlled by a first entity to capture a first set of data results. Then a second query is performed on a second data set controlled by a second entity to capture a second set of data results. A relationship ontology that correlates data stored in different data stores controlled by different entities is then consulted to identify one or more relationships between data in the selected results set and the second data set.

Cross-model filtering

File exploration is facilitated by enabling zoom with respect to a thumbnail based on an identified point of interest. More particularly, a scaled thumbnail of the same size as a thumbnail can be generated and presented based on an identified point of interest. In one instance, a scaled thumbnail can be generated automatically by repositioning an image based on a point of interest and applying a scaling factor to the image behind a fixed clipping mask of the same size as the thumbnail. Furthermore, navigation, among other things, is enabled to allow panning with respect to a scaled thumbnail.

Smart defaults are provided for data visualization by creating a default layout of rows, columns, filters, and comparable elements that improve a user's experience in finding relevant answers within the data. Usage history of the ways that users look at data in various data sources, user specific information, and inferred relationships between a current user and similar users are used to determine elements relevant to visualization of data for a particular user such that the visualization process may be automatically started, and a relevance model is formed/adjusted based on these factors. Queries may also be executed in a preemptive fashion based on the relevance model and results provided to a requesting user more rapidly enhancing user experience with networked data visualization.

Smart defaults for data visualizations

A mechanism for responding to a natural language query. Linguistic parsing is applied to the natural language query to generate a set of language element(s). One or more of those language elements are mapped to parameters and/or data elements of a parameterized report, which is selected for use in responding to the query based on the mapping. The selection of the parameterized report and/or mapping to the parameterized report may use the schema of the underlying dataset that the parameterized report uses in order to perform more precise mapping and selection. The selected parameterized report is then used to construct structured data that is responsive to the query. For instance, the structured data could include visualizations. In this manner, already existing parameterized reports that were previously used to generated structured data in the form of reports, may again be used in order to respond to natural language queries.

Query response using mapping to parameterized report

Techniques are described herein that are capable of presenting a synthesized alert using a digital personal assistant. A synthesized alert is an alert that is synthesized from multiple subsets of information. Examples of a subset of information include but are not limited to at least one notification (e.g., enterprise alert), at least one calendar entry, at least one document (e.g., word processing document, spreadsheet, Web page, image, audio file, video file, etc.), raw data, processed data, at least one output of an executable, and any combination thereof.

Presenting a synthesized alert using a digital personal assistant

Mechanisms to help a computing system respond to a request for information within a data model. After determining that there is insufficient information within the data model to respond to the request, the computing system identifies one or more additional data sources that are external to the data model and that contain information suitable to respond to the request. The computing system then automatically supplements the data model with at least one of such additional data sources. The computing system then responds to the request using the supplemented data model. In some embodiments, the supplementation is performed in advance of the request by analyzing the characteristics of the data model and/or by anticipating possible future requests. Thus, a data model grows automatically in response to particular usage of that data model to satisfy requests.

Automated supplementation of data model

Presenting data from different data providers in a correlated fashion. The method includes performing a first query on a first data set controlled by a first entity to capture a first set of data results. The method further includes performing a second query on a second data set controlled by a second entity to capture a second set of data results. The method includes receiving a selection of one or more results from the first data set. The method further includes using the one or more selected results, consulting a relationship ontology that correlates data stored in different data stores controlled by different entities, to identify one or more relationships between data in the selected results set and the second data set.

The description relates to an interactive digital display. One example includes a display device configured to receive user input and recognize commands relative to data visualizations. The system also includes a graphical user interface configured to be presented on the display device that allows users to interact with the data visualizations via the user commands.

Interactive digital displays

The techniques described herein determine configurations for data visualizations based on characteristics interpreted from input data. Input data including a plurality of images may be obtained. For instance, the input data may include one or more files containing images associated with an entity. The techniques disclosed herein may determine a characteristic, such as a primary color, based on the input data. The techniques disclosed herein may determine an individual entity or subject to be associated with the characteristic. Techniques disclosed herein also involve the generation of output data defining a visualization based on the characteristic. A rendering of the output data provides an indication of the individual entity or subject. In some configurations, a rendering of the output data provides a graphical association between data in a dataset and the individual entity or subject.

Intelligent configuration of data visualizations

Data processing architecture where submitters can write data and/or metadata changes to a destination through an asynchronous interface. The architecture includes a ticketing system that issues a ticket (a unique identifier) to the submitter in response to receiving a changelist from the submitter. When the changes are successfully completed at the destination, the cached destination data can be re-downloaded to the submitter, and the changes cached in the submitter, discarded. Absent this capability, a user has to manually manage the cached changes and cached data. The architecture can be distributed in that multiple submitters can submit changelists. The destination provides data integrity by managing submissions to assist in ensuring data integrity across multiple submissions. Automatic changelist cleanup is provided on the submitter so that changelists do not impact performance, memory, and storage.

Maintaining client data integrity in a distributed environment using asynchronous data submission

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Pullman, WA, United States of America

Patrick John Baumgartner