CLIENT INTENT IN AN INTEGRATED SEARCH ENVIRONMENT

• US 20170039269A1
• Filed: 03/26/2014
• Published: 02/09/2017
• Est. Priority Date: 03/26/2014
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Huang, Xiao, Cai, Yichao, Crook, Aidan, Raff, Paul, Ma, Rui, Yan, An
• Application Number:
  US15/303,951
• Publication Number:
  US 20170039269A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

Architecture that operates in combination with an integrated search framework to derive user intent associated with a search query, and then based on the derived intent, choose the search method: a local search on the current local device from which the search is initiated, a non-local search of data sources other than the local device, or both the local search and the non-local search. The query context can be derived to more effectively assess the query intent. The architecture employs predictive models trained with candidate features that enable the prediction of a singular intent (or degree of intent) in the integrated search environment. The models can be trained using historical and real-time features. A classifier is trained using the features. The user intent is then derived based on the classifier output and the search is performed accordingly.

Claims

1. A system, comprising:

a search component configured to receive a query as part of an integrated search process and to receive query context of the query;

a classification component configured to generate a classification value for the query based on the candidate features of predictive models;

an intent component configured to identify a degree of intent based on the classification value, wherein the search process is directed by the search component based on the degree of intent, to at least one of a local search or a non-local search; and

at least one microprocessor configured to execute computer-executable instructions in a memory associated with the search component, the classification component, and the intent component.

2. The system of claim 1, wherein the search process is performed as both the local search and the non-local search to obtain overall results, and the overall results are adjusted to show only relevant results related to the degree of intent.

3. The system of claim 1, wherein the intent component computes the degree of intent as relates to non-local content, local content, a local file, and a local application.

4. The system of claim 1, further comprising a features component configured to obtain the candidate features related to the query and query context.

5. The system of claim 1, further comprising a suggestion component that operates separately or in cooperation with the classification component to suggest local content or web content relevant to the query context.

6. A method, comprising acts of:

receiving a query as part of a search process that can perform a local search and a non-local search;

deriving context of the query;

assessing features associated with the context;

computing a classification value of the query based on the features;

identifying a degree of intent based on the classification value;

directing the search process to at least one of the local search or the non-local search based on the degree of intent; and

configuring a microprocessor to execute instructions in a memory associated with the acts of receiving, deriving, assessing, computing, identifying, and directing.

7. The method of claim 6, further comprising adjusting the search results based on the degree of intent.

8. The method of claim 6, further comprising obtaining the features from predictive models to identify the degree of intent.

9. The method of claim 6, further comprising applying an out-of-context feature to determine when a query is most frequently associated with the local search.

10. The method of claim 6, further comprising overriding a programmed search function and choosing a different search process based on the degree of intent.