MULTI-DOMAIN SEARCH ON A COMPUTING DEVICE

• US 20150347594A1
• Filed: 09/30/2014
• Published: 12/03/2015
• Est. Priority Date: 05/30/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Hornkvist, John M., Kapoor, Gaurav
• Granted Patent: US 10,642,845 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Systems and methods are disclosed for improving search results returned to a user from one or more domains, utilizing query features learned locally on the user'"'"'s device. One or more domains can inform a computing device of one or more features related to a search query upon which to the computing device can apply local learning. A local search system can include a local database, a local search history and feedback history database, and a local learning system to identify features about query terms. The features can be learned from the user'"'"'s interaction with both local search results and remote search results, without sending the user interaction information or other user identification information to a remote search engine. A locally learned feature can be used to extend a query, bias a query term, or filter query results.

Claims

1. A computer-implemented method, comprising:

learning, on a computing device, a feature related to a search query, wherein the feature is learned, at least in part, using information generated on, and private to the computing device;

transmitting, to a remote search engine, a search query and an indication of the feature; and

receiving, by the computing device, search results responsive to the search query and the indication of the feature.

2. The method of claim 1, wherein the indication of the feature comprises at least one of:

a bias toward the feature or a feature vector.

3. The method of claim 1, wherein information obtained from the computing device comprises at least one of a search query performed on the computing device of information on the computing device or feedback of interaction by a user of the computing device with results returned from a search query performed on the computing device of information stored on the computing device, wherein the feedback of interaction by the user of the computing device is not transmitted to the remote search engine.

4. The method of claim 3, wherein learning comprises a statistical analysis of the information obtained from the computing device, wherein statistical analysis comprises one of:

linear regression, Bayes classification, or Naive Bayes classification.

5. The method of claim 1, further comprising:

receiving, from a remote search engine, a feature related to a search query for the computing device to learn.

6. The method of claim 5, further comprising:

learning, on the computing device, the feature received from the remote search engine, wherein the feature received from the remote search engine is learned, at least in part, using information generated on, and private to the computing device that is not transmitted to the remote search engine.

7. The method of claim 1, wherein learning the feature comprises disambiguating a query term related to the search query in accordance with the information obtained from the computing device.

8. A non-transitory computer-readable medium programmed with executable instructions that, when executed by a processing system, perform a method, comprising:

learning, on a computing device, a feature related to a search query, wherein the feature is learned, at least in part, using information generated on, and private to, the computing device;

transmitting, to a remote search engine, a search query and an indication of the feature; and

receiving, by the computing device, search results responsive to the search query and the indication of the feature.

9. The medium of claim 8, wherein the indication of the feature comprises at least one of:

a bias toward the feature or a feature vector.

10. The medium of claim 8, wherein information obtained on the computing device comprises at least one of:

a search query performed on the computing device of information on the computing device or feedback of interaction by a user of the computing device with results returned from a search query performed on the computing device of information stored on the computing device, wherein the feedback of interaction by the user of the computing device is not transmitted to the remote search engine.

11. The medium of claim 10, wherein learning comprises a statistical analysis of the information obtained from the computing device, wherein statistical analysis comprises one of:

linear regression, Bayes classification, or Naive Bayes classification.

12. The medium of claim 8, further comprising:

receiving, from a remote search engine, a feature related to a search query for the computing device to learn.

13. The medium of claim 12, further comprising:

learning, on the computing device, the feature received from the remote search engine, wherein the feature received from the remote search engine is learned, at least in part, using information generated on, and private to, the computing device that is not transmitted to the remote search engine.

14. The medium of claim 8, wherein learning the feature comprises disambiguating a query term related to the search query in accordance with the information obtained from the computing device.

15. A system comprising:

a processing system programmed with executable instructions that, when executed by the processing system, perform a method, comprising;

learning, on the system, a feature related to a search query, wherein the feature is learned, at least in part, using information generated on, and private to, the system;

transmitting, to a remote search engine, a search query and an indication of the feature; and

receiving, by the system, search results responsive to the search query and the indication of the feature.

16. The system of claim 15, wherein the indication of the feature comprises at least one of:

a bias toward the feature or a feature vector.

17. The system of claim 15, wherein information obtained on the system comprises at least one of:

a search query performed on the system of information on the system or feedback of interaction by a user of the system with results returned from a search query performed on the system of information stored on the system, wherein the feedback of interaction by the user of the system is not transmitted to the remote search engine.

18. The system of claim 17, wherein learning comprises a statistical analysis of the information obtained from the system, wherein statistical analysis comprises one of:

linear regression, Bayes classification, or Naive Bayes classification.

19. The system of claim 15, further comprising:

receiving, from a remote search engine, a feature related to a search query for the system to learn.

20. The system of claim 19, further comprising:

learning, on the system, the feature received from the remote search engine, wherein the feature received from the remote search engine is learned, at least in part, using information generated on, and private to, the system that is not transmitted to the remote search engine.

21. The system of claim 15, wherein learning the feature comprises disambiguating a query term related to the search query in accordance with the information obtained from the system.