Machine-assisted object matching

• US 10,114,858 B2
• Filed: 01/29/2018
• Issued: 10/30/2018
• Est. Priority Date: 05/13/2016
• Status: Active Grant

• Current Assignee: Sparkcognition Q Incorporated
• Original Assignee: Maana, Inc.
• Inventors: Stoica-Beck, Alina Mihaela, Mackay, Jason Forrest
• Primary Examiner(s): Le, Hung D
• Application Number:
  US15/883,030
• Publication Number:
  US 20180150506A1
• Time in Patent Office: 274 Days
• Field of Search: None
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/219 Managing data history or versioning querying versioned data G06F16/2474; querying temporal data G06F16/2477
• CPC Class Codes: G06F 16/242 Query formulation
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/3325 Reformulation based on results of preceding query
• CPC Class Codes: G06F 40/205 Parsing

Abstract

Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Claims

1. A method for managing data using one or more processors, included in one or more network computers, wherein the one or more processors execute instructions to perform actions, comprising:

employing a source data server to provide a principal data set of principal objects and another data set of other data objects;

instantiating a first engine to perform actions including;

associating one or more principal objects with one or more other objects that are selected as potential matches to the one or more of the principal objects; and

identifying each match and non-match of the one or more of the selected other objects with their associated principal object; and

instantiating a second engine to train and employ a ranker to identify a matched other object that is top-ranked in similarity by its association with the one or more principal objects;

instantiating a third engine to selectively filter the other objects to rank each matched other object higher than other objects associated with a same principal object; and

employing geolocation information from a Global Positioning System (GPS) device at a client computer to determine one or more features that are included in a display of the ranked other objects to a user to improve the user'"'"'s understanding, wherein the features include one or more of time zones, languages, currencies, or calendar formatting that is displayed to the user of the client computer when the client computer is located at a particular geo-location.

2. The method of claim 1, wherein the first engine performs further actions comprising selecting the one or more principal objects based on one or more heuristics and one or more associations with the selected other objects.

3. The method of claim 1, further comprising employing a hardware security module to provide tamper resistant safeguarding of cryptographic information.

4. The method of claim 1, further comprising:

providing raw data; and

transforming the raw data into model objects that are included in one or more of the principal data set or the other data set.

5. The method of claim 1, wherein the first engine performs further actions comprising:

training a classifier to distinguish between matches and non-matches; and

employing the classifier to discover incorrect identification of matches.

6. The method of claim 1, further comprising:

grouping the other objects in the other data set with potential duplicate objects in the other data set; and

training and employing another ranker to rank potential duplicate objects based on their similarity to their associated principal duplicate object, wherein a top-ranked potential duplicate object is indicated as a duplicate object.

7. A system for managing data, comprising:

a network computer, comprising;

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

employing a source data server to provide a principal data set of principal objects and another data set of other data objects;

instantiating a first engine to perform actions including;

associating one or more principal objects with one or more other objects that are selected as potential matches to the one or more of the principal objects; and

identifying each match and non-match of the one or more of the selected other objects with their associated principal object; and

instantiating a second engine to train and employ a ranker to identify a matched other object that is top-ranked in similarity by its association with the one or more principal objects; and

instantiating a third engine to selectively filter the other objects to rank each matched other object higher than other objects associated with a same principal object; and

employing geolocation information from a Global Positioning System (GPS) device at a client computer to determine one or more features that are included in a display of the ranked other objects to a user to improve the user'"'"'s understanding of the ranked other objects, wherein the features include one or more of time zones, languages, currencies, or calendar formatting that is displayed to the user of the client computer when the client computer is located at a particular geo-location; and

the client computer, comprising;

a GPS device;

a client computer transceiver that communicates over the network;

a client computer memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

providing the display of the ranked other objects to the user.

8. The system of claim 7, wherein the first engine performs further actions comprising selecting the one or more principal objects based on one or more heuristics and one or more associations with the selected other objects.

9. The system of claim 7, further comprising employing a hardware security module to provide tamper resistant safeguarding of cryptographic information.

10. The system of claim 7, further comprising:

providing raw data; and

transforming the raw data into model objects that are included in one or more of the principal data set or the other data set.

11. The system of claim 7, wherein the first engine performs further actions comprising:

training a classifier to distinguish between matches and non-matches; and

employing the classifier to discover incorrect identification of matches.

12. The system of claim 7, further comprising:

grouping the other objects in the other data set with potential duplicate objects in the other data set; and

training and employing another ranker to rank potential duplicate objects based on their similarity to their associated principal duplicate object, wherein a top-ranked potential duplicate object is indicated as a duplicate object.

13. The system of claim 7, further comprising, distributing one or more instances of one or more of the first engine, the second engine, or the third engine across two or more separate network computers, wherein the one or more instances may execute in parallel or concurrently.

14. A network computer for managing data, comprising:

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

employing a source data server to provide a principal data set of principal objects and another data set of other data objects;

instantiating a first engine to perform actions including;

associating one or more principal objects with one or more other objects that are selected as potential matches to the one or more of the principal objects; and

identifying each match and non-match of the one or more of the selected other objects with their associated principal object; and

instantiating a second engine to train and employ a ranker to identify a matched other object that is top-ranked in similarity by its association with the one or more principal objects; and

instantiating a third engine to selectively filter the other objects to rank each matched other object higher than other objects associated with a same principal object; and

employing geolocation information from a Global Positioning System (GPS) device at a client computer to determine one or more features that are included in a display of the ranked other objects to a user to improve the user'"'"'s understanding of the ranked other objects, wherein the features include one or more of time zones, languages, currencies, or calendar formatting that is displayed to the user of the client computer when the client computer is located at a particular geo-location.

15. The network computer of claim 14, wherein the first engine performs further actions comprising selecting the one or more principal objects based on one or more heuristics and one or more associations with the selected other objects.

16. The network computer of claim 14, further comprising employing a hardware security module to provide tamper resistant safeguarding of cryptographic information.

17. The network computer of claim 14, further comprising:

providing raw data; and

transforming the raw data into model objects that are included in one or more of the principal data set or the other data set.

18. The network computer of claim 14, wherein the first engine performs further actions comprising:

training a classifier to distinguish between matches and non-matches; and

employing the classifier to discover incorrect identification of matches.

19. The network computer of claim 14, further comprising:

grouping the other objects in the other data set with potential duplicate objects in the other data set; and

training and employing another ranker to rank potential duplicate objects based on their similarity to their associated principal duplicate object, wherein a top-ranked potential duplicate object is indicated as a duplicate object.

20. A processor readable non-transitory storage media that includes instructions for managing data, wherein execution of the instructions by one or more hardware processors performs actions, comprising:

employing a source data server to provide a principal data set of principal objects and another data set of other data objects;

instantiating a first engine to perform actions including;

associating one or more principal objects with one or more other objects that are selected as potential matches to the one or more of the principal objects; and

identifying each match and non-match of the one or more of the selected other objects with their associated principal object; and

instantiating a second engine to train and employ a ranker to identify a matched other object that is top-ranked in similarity by its association with the one or more principal objects;

instantiating a third engine to selectively filter the other objects to rank each matched other object higher than other objects associated with a same principal object; and

employing geolocation information from a Global Positioning System (GPS) device at a client computer to determine one or more features that are included in a display of the ranked other objects to a user to improve the user'"'"'s understanding of the ranked other objects, wherein the features include one or more of time zones, languages, currencies, or calendar formatting that is displayed to the user of the client computer when the client computer is located at a particular geo-location.