Context-based search and relevancy generation

Patent metadata

Field	Value
Publication number	US-10169432-B2
Application number	US-201815925287-A
Country	US
Kind code	B2
Filing date	Mar 19, 2018
Priority date	Nov 6, 2014
Publication date	Jan 1, 2019
Grant date	Jan 1, 2019

Abstract

A computing device receives a trigger to surface relevant content. The device also obtains a variety of different types of cross-source contextual information. Items of content are identified and relevancy weights are obtained based on the contextual information. A relevancy is calculated, based on the relevancy weights, for each item of content. The items of content are surfaced.

First claim

What is claimed is:

1. A computing system comprising:
at least one processor; and
memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to:
based on a plurality of sensor inputs, identify a plurality of different items of context from a plurality of different sources of context;
search a plurality of different content sources to identify a set of documents based on a trigger input;
generate a relevancy metric corresponding to each document in the identified set of documents, based on the plurality of different items of context; and
generate a user interface display that selectively displays the documents in the set of documents based on the corresponding relevancy metric.

2. The computing system of claim 1 wherein the instructions configure the computing system to:
determine whether a user is using the computing system in a work context or a non-work context; and
generate the relevancy metric for the documents based on the detected work context or non-work context.

3. The computing system of claim 2 wherein the instructions configure the computing system to:
detect a user location; and
generate the relevancy metric for the documents based on the detected user location.

4. The computing system of claim 2 wherein the instructions configure the computing system to:
identify temporal document usage patterns corresponding to the documents, each temporal document usage pattern being indicative of a pattern of usage of a corresponding document, by the user, over time; and
generate the relevancy metric for the documents based on the identified temporal document usage patterns corresponding to each document.

5. The computing system of claim 2 wherein the instructions configure the computing system to:
detect user interactions corresponding to each of the documents; and
generate the relevancy metric for the documents based on the detected user interactions.

6. The computing system of claim 5 wherein the instructions configure the computing system to:
detect recency of user interactions; and
generate the relevancy metric based on the detected recency.

7. The computing system of claim 5 wherein the instructions configure the computing system to:
detect interaction type; and
generate the relevancy metric based on the detected interaction type.

8. The computing system of claim 5 wherein the instructions configure the computing system to:
detect frequency of interaction; and
generate the relevancy metric based on the detected frequency of interaction.

9. The computing system of claim 2 wherein the instructions configure the computing system to:
access a calendar system;
identify user interactions with the documents relative to calendar items of the user; and
generate the relevancy metric for the documents based on the identified user interactions with the documents relative to the calendar items.

10. The computing system of claim 9 wherein the instructions configure the computing system to:
identify whether the user is interacting with the documents in temporal proximity to meetings or deadlines.

11. The computing system of claim 2 wherein the instructions configure the computing system to:
detect other personnel that are related to the documents, other than the user; and
generate the relevancy metric for the documents based on the detected other personnel.

12. The computing system of claim 11 wherein the instructions configure the computing system to:
detect a proximity of the user to the other personnel; and
generate the relevancy metric based on the detected proximity to the other personnel.

13. The computing system of claim 2 wherein the instructions configure the computing system to:
detect a type of user device being used by the user; and
generate the relevancy metric for the documents based on the detected type of user device.

14. The computing system of claim 13 wherein the instructions configure the computing system to:
detect open applications on the user device; and
generate the relevancy metric for the documents based on the detected open applications.

15. The computing system of claim 2 wherein the instructions configure the computing system to:
detect biometric data corresponding to the user; and
generate the relevancy metric for the documents based on the biometric data.

16. The computing system of claim 2 wherein the instructions configure the computing system to:
generate a confidence score associated with each of the documents; and
engage a dialog engine to obtain additional context information from the user when one or more confidence scores fall below a threshold value.

17. The computing system of claim 2 wherein the instructions configure the computing system to:
generate the relevancy metric for the documents based on weighted relevancy criteria; and
vary weights on the weighted relevancy criteria based on the items of context.

18. A computer implemented method, comprising:
identifying a plurality of different items of context, from a plurality of different sources of context;
receiving a trigger input;
in response to the trigger input, searching a plurality of different content sources, to identify a set of documents, based on the trigger input;
detecting a user location;
generating a relevancy measure corresponding to each document in the identified set of documents, based on the user location and the plurality of different items of context; and
generating a user interface that selectively renders the documents, in the set of documents, to a user based on the corresponding relevancy score.

19. The computer implemented method of claim 18 wherein detecting a plurality of different sensor inputs comprises:
detecting a usage pattern sensor input that identifies temporal document usage patterns corresponding to the documents, each temporal document usage pattern being indicative of a pattern of usage of a corresponding document, by the user, over time, wherein generating a relevancy metric includes generating the relevancy metric for the documents based on the identified temporal document usage patterns corresponding to each document.

20. A computing system, comprising:
at least one processor; and
memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to provide:
a context identification system configured to identify a plurality of different items of context, from a plurality of different sources of context, including whether a user is using the computing system in a work context or a non-work context;
a document interaction detector configured to detect user interactions corresponding to each of the documents;
a cross-source search component configured to search a plurality of different content sources, to identify a set of documents, based on a trigger input;
a relevancy generator configured to generate a relevancy metric corresponding to each document in the identified set of documents, based on the plurality of different items of context and the detected user interactions; and
a user interface component configured to generate a user interface display that selectively displays the documents in the set of documents based on the corresponding relevancy metric.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Vogel Matthew
• Seto Julie C
• Stepanich Darron
• Kikin-Gil Erez

Classifications

• H04L61/609

  Electricity · mapped topic

• G06F17/3053

  Physics · mapped topic

• G06F3/0484

  for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range · CPC title

• G06F17/30554 Primary

  Physics · mapped topic

• G06F17/30029

  Physics · mapped topic