METHOD AND APPARATUS FOR QUICKLY EVALUATING ENTITIES

• US 20130024465A1
• Filed: 07/19/2012
• Published: 01/24/2013
• Est. Priority Date: 07/20/2011
• Status: Active Grant

• Current Assignee: OpenTable, Inc. (Booking Holdings, Inc., fka Priceline Group Inc.)
• Original Assignee: NESS Computing Incorporated (Booking Holdings, Inc., fka Priceline Group Inc.)
• Inventors: Schiff, Jeremy Ryan, Chatterji, Sourav, Reese, Corey Layne, Schlansker, Steven Charles, Wu, Leejay, Twohey, Paul Kenneth
• Granted Patent: US 9,195,769 B2
• Time in Patent Office: Days
• US Class Current: 707/749
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]

Abstract

Embodiments of the invention relate to methods and systems for evaluating entities for a target user, the method comprising obtaining, at a server computer, entity data from a plurality of data sources. The entity data is then stored in an entity database. The method further comprises merging the entity data from the plurality of data sources, mapping the entity data to a corresponding entity, and differentiating the entity. Then a relevance is determined associated with the entity data and data source. The method further comprises generating a set of entity evaluations to the target user using the relevance, determining a set of one or more entities relevant to the primary user based on the entity data, user data, and the relevance, with an initial order of relevance, and displaying, on a user device, the set of relevant entities to the target user in the order of relevance.

Claims

1. A computer-implemented method for evaluation of an entity for a target user based at least on stored data about the target user, wherein the entity is an entity of a set of entities, and evaluation is related to the entity, the method comprising:

receiving, at a server computer, entity data relating to an entity in the set of entities, wherein the entity data includes a relevance and a weight to the entity data;

generating, by the server computer, a set of entity evaluations to the target user based on the weight to the entity data and the relevance;

determining, at the server computer, a set of evaluations for one or more entities relevant to the primary user based on the entity data, user data, and relevance;

determining initial order of relevance of the set of relevant entities based on the relevance and weight of the set of entity evaluations; and

communicating to the target user, on a user device, the initial order of relevance of the set of relevant entities to the target user, wherein the target user provides a response to entities in the set of relevant entities via the user device.

2. The method of claim 1, further comprising:

obtaining, at the server computer, feedback data from a plurality of users, wherein the feedback data is associated to the target user in the plurality of users;

obtaining, at the server computer, location data from an external data source in one or more data sources associated with the target user, wherein the location data is associated with an entity in a plurality of entities;

storing the location data at the server computer, wherein location data associated with the plurality of entities is stored in the entity database;

determining an entity that is associated with the feedback data from the target user;

determining, by the server computer, if the entity associated with the feedback data from the target user corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the feedback data to the corresponding entity associated with the location data, wherein if the entity associated with the feedback data from the target user does not correspond with an entity associated with location data in the entity database, the entity database is updated to include the entity associated with the feedback data from the target user;

updating the entity database to include the feedback data from the target user associated the entity, with the location data associated with the corresponding entity;

generating an updated set of relevant entity evaluations to the target user based on the updated entity database;

determining an updated order of relevance of the updated set of relevant entity evaluations based on the feedback data; and

communicating to the target user, on the user device, the updated order of relevance of the updated set of relevant entity evaluations to the target user.

3. The method of claim 1, further comprising:

obtaining, at the server computer, personal data from the one or more of data sources, wherein the personal data is associated with the target user in the plurality of users;

storing the personal data at the server computer, wherein personal data associated with the plurality of users is stored in a user database;

merging, by the server computer, the personal data from the plurality of data sources;

mapping, by the server computer, the personal data from the plurality of data sources to the target user;

updating the user database with the personal data and stored data associated with the target user;

determining, at the server computer, whether an entity is associated with the personal data from the target user;

determining, at the server computer, whether the entity associated with the personal data corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the personal data from the target user to the corresponding entity associated with the location data;

determining, at the server computer, inferred location data based on the mapping;

updating the entity database to include the inferred location data associated with the corresponding entity;

creating a personal profile for the target user based on the personal data, wherein the personal profile is stored in a user database;

analyzing, at the server computer, the personal data and stored data associated with the target user to determine a user-specific relevance;

updating the personal profile of the target user with the user-specific relevance;

generating an updated set of relevant entity evaluations to the target user based on the updated personal profile and the user-specific relevance;

determining an updated order of relevance of the updated set of relevant entities evaluations based on the updated personal profile of the target user; and

communicating to the target user, on the user device, the updated order of relevance of the updated set of relevant entity evaluations to the target user.

4. The method of claim 1, further comprising:

obtaining, at a server computer, data from a plurality of data sources about entities in the set of entities;

merging, using the server computer, the entity data from the plurality of data sources;

storing the entity data at the server computer in an entity database;

mapping, using the server computer, an entity-specific subset of the entity data from the entity database, wherein the entity-specific subset pertains to a corresponding entity;

differentiating, using the server computer, portions of the entity-specific subset according to which of the plurality of data sources the portion derived from;

determining, using the server computer, a relevance associated with the entity-specific subset and the data source, wherein the relevance is determined by a computer process to cross-reference the entity data and data source;

assigning, by the server computer, a weight to the entity data and the data source associated with the entity data based on the relevance; and

updating the entity database with the entity data associated with the corresponding entity.

5. The method of claim 2, wherein the location data includes geographical data identifying the location of the target user associated with the plurality of data sources.

6. The computer-implemented method of claim 1, wherein determining the initial order of relevance or updated order of relevance is based on criteria.

7. The computer-implemented method of claim 6, wherein the criteria includes a number of responses initiated by the target user to a specific entity evaluation or entity.

8. The computer-implemented method of claim 6, wherein the criteria includes one or more entities or entity evaluations that the target user recently responded to.

9. The computer-implemented method of claim 6, wherein the criteria includes a current location of the target user.

10. The computer-implemented method of claim 6, wherein the criteria includes a frequency in which the target user interacts with a relevant entity.

11. The computer-implemented method of claim 1, wherein the response via the user device includes finger gestures, clicking, shaking the user device, a coordinate motion between two user devices, blowing on the user device, spinning the user device, making a sound, or speaking a voice command.

12. The computer-implemented method of claim 3, wherein the communicating further comprises:

determining a set of media objects associated with the set of relevant entity evaluations; and

displaying the set of media objects with the set of relevant entity evaluations.

13. The computer-implemented method of claim 3, further comprising:

determining a navigation manner of the target user;

storing the navigation manner in the user database; and

adjusting the initial categorization and initial order of relevance based on the navigation manner in the updated user database.

14. The computer-implemented method of claim 6, wherein the criteria includes a frequency in which an entity or entity evaluation has been previously queried.

15. The computer-implemented method of claim 3, further comprising:

computing a confidence score for each entity in the set of relevant entity evaluations;

updating the order of relevance based on the confidence score; and

communicating to the target user the set of relevant entity evaluations based on the updated order of relevance.

16. A server computer comprising a processor and a non-transitory computer-readable medium, the non-transitory computer-readable medium comprising code executable by the processor to implement a method for evaluation of an entity for a target user based at least on stored data about the target user, wherein the entity is an entity of a set of entities, and evaluation is related to the entity, the method comprising:

receiving, at the server computer, entity data relating to an entity in the set of entities, wherein the entity data includes a relevance and a weight to the entity data;

generating, by the server computer, a set of entity evaluations to the target user based on the weight to the entity data and the relevance;

determining, at the server computer, a set of evaluations for one or more entities relevant to the primary user based on the entity data, user data, and relevance;

determining initial order of relevance of the set of relevant entities based on the relevance and weight of the set of entity evaluations; and

communicating to the target user, on a user device, the initial order of relevance of the set of relevant entities to the target user, wherein the target user provides a response to entities in the set of relevant entities via the user device.

17. The server computer of claim 16, the method further comprising:

obtaining, at the server computer, feedback data from a plurality of users, wherein the feedback data is associated to the target user in the plurality of users;

obtaining, at the server computer, location data from an external data source in one or more data sources associated with the target user, wherein the location data is associated with an entity in a plurality of entities;

storing the location data at the server computer, wherein location data associated with the plurality of entities is stored in the entity database;

determining an entity that is associated with the feedback data from the target user;

determining, by the server computer, if the entity associated with the feedback data from the target user corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the feedback data to the corresponding entity associated with the location data, wherein if the entity associated with the feedback data from the target user does not correspond with an entity associated with location data in the entity database, the entity database is updated to include the entity associated with the feedback data from the target user;

updating the entity database to include the feedback data from the target user associated the entity, with the location data associated with the corresponding entity;

generating an updated set of relevant entity evaluations to the target user based on the updated entity database;

determining an updated order of relevance of the updated set of relevant entity evaluations based on the feedback data; and

communicating to the target user, on the user device, the updated order of relevance of the updated set of relevant entity evaluations to the target user.

18. The server computer of claim 16, the method further comprising:

obtaining, at the server computer, personal data from the one or more of data sources, wherein the personal data is associated with the target user in the plurality of users;

storing the personal data at the server computer, wherein personal data associated with the plurality of users is stored in a user database;

merging, by the server computer, the personal data from the plurality of data sources;

mapping, by the server computer, the personal data from the plurality of data sources to the target user;

updating the user database with the personal data and stored data associated with the target user;

determining, at the server computer, whether an entity is associated with the personal data from the target user;

determining, at the server computer, whether the entity associated with the personal data corresponds with an entity associated with location data in the entity database;

mapping the entity associated with the personal data from the target user to the corresponding entity associated with the location data;

determining, at the server computer, inferred location data based on the mapping;

updating the entity database to include the inferred location data associated with the corresponding entity;

creating a personal profile for the target user based on the personal data, wherein the personal profile is stored in a user database;

analyzing, at the server computer, the personal data and stored data associated with the target user to determine a user-specific relevance;

updating the personal profile of the target user with the user-specific relevance;

generating an updated set of relevant entity evaluations to the target user based on the updated personal profile and the user-specific relevance;

determining an updated order of relevance of the updated set of relevant entities evaluations based on the updated personal profile of the target user; and

communicating to the target user, on the user device, the updated order of relevance of the updated set of relevant entity evaluations to the target user.

19. The server computer of claim 16, the method further comprising:

obtaining, at a server computer, data from a plurality of data sources about entities in the set of entities;

merging, using the server computer, the entity data from the plurality of data sources;

storing the entity data at the server computer in an entity database;

mapping, using the server computer, an entity-specific subset of the entity data from the entity database, wherein the entity-specific subset pertains to a corresponding entity;

differentiating, using the server computer, portions of the entity-specific subset according to which of the plurality of data sources the portion derived from;

determining, using the server computer, a relevance associated with the entity-specific subset and the data source, wherein the relevance is determined by a computer process to cross-reference the entity data and data source;

assigning, by the server computer, a weight to the entity data and the data source associated with the entity data based on the relevance; and

updating the entity database with the entity data associated with the corresponding entity.

20. The server computer of claim 17, wherein the location data includes geographical data identifying the location of the target user associated with the plurality of data sources.

21. The server computer of claim 16, wherein determining the initial order of relevance or updated order of relevance is based on criteria.

22. The server computer of claim 21, wherein the criteria includes a number of responses initiated by the target user to a specific entity evaluation or entity.

23. The server computer of claim 21, wherein the criteria includes one or more entities or entity evaluations that the target user recently responded to.

24. The server computer of claim 21, wherein the criteria includes a current location of the target user.

25. The server computer of claim 21, wherein the criteria includes a frequency in which the target user interacts with a relevant entity.

26. The server computer of claim 16, wherein the response via the user device includes finger gestures, clicking, shaking the user device, a coordinate motion between two user devices, blowing on the user device, spinning the user device, making a sound, or speaking a voice command.

27. The server computer of claim 18, wherein the communicating further comprises:

determining a set of media objects associated with the set of relevant entity evaluations; and

displaying the set of media objects with the set of relevant entity evaluations.

28. The computer-implemented method of claim 18, further comprising:

determining a navigation manner of the target user;

storing the navigation manner in the user database; and

adjusting the initial categorization and initial order of relevance based on the navigation manner in the updated user database.

29. The server computer of claim 21, wherein the criteria includes a frequency in which an entity or entity evaluation has been previously queried.

30. The server computer of claim 18, further comprising:

computing a confidence score for each entity in the set of relevant entity evaluations;

updating the order of relevance based on the confidence score; and

communicating to the target user the set of relevant entity evaluations based on the updated order of relevance.