Methods and Systems to Help Detect Identity Fraud

• US 20070162761A1
• Filed: 12/20/2006
• Published: 07/12/2007
• Est. Priority Date: 12/23/2005
• Status: Abandoned Application

• Current Assignee: L-1 Secure Credentialing, LLC (Advent International Corporation)
• Original Assignee: L-1 Secure Credentialing, LLC (Advent International Corporation)
• Inventors: Davis, Bruce, Conwell, William
• Application Number:
  US11/613,891
• Publication Number:
  US 20070162761A1
• Time in Patent Office: Days
• US Class Current: 713/182
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/43 Querying
• CPC Class Codes: G06F 16/489 using time information
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/783 using metadata automatically derived from the content
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G06Q 50/18 Legal services
• CPC Class Codes: G06Q 50/265 Personal security, identity or safety
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 9/3247 involving digital signatures

Abstract

The disclosed technology generally relates to methods and systems to aid in verifying a person'"'"'s identity, e.g., in connection with applying for an identity document (such as a passport or driver'"'"'s license), or in connection with qualifying to enter a secured area (such as at an airport). Many arrangements involve testing the person concerning specific knowledge with which he or she should be familiar, e.g., by reason of living in a particular residence and neighborhood, by reason of their particular employment, or by reason of their particular education. An appendix particularly addresses crowdsourcing technology, including its applicability in redressing some of the shortcomings of fingerprint-based content identification.

Claims

1. A method comprising:

collecting information about an applicant;

storing said information in a first data structure;

by reference to the information stored in the data structure, identifying a knowledge domain with which the applicant is likely familiar;

selecting a question relating to said knowledge domain, the answer to said question not earlier having been provided by the applicant;

posing the selected question to the applicant;

receiving an applicant response to the posed question; and

generating a score based at least in part on an assessment of the received applicant response.

2. The method of claim 1 that includes using certain of said collected information to obtain additional information relating to the applicant from an external database, and storing said additional information in the data structure.

3. The method of claim 1 in which the collected information indicates that the applicant is a lawyer, and in which the question posed to the applicant is one related to a legal subject.

4. The method of claim 3, wherein the question is one to which a lawyer-applicant is more likely to know the answer than a non-lawyer-applicant, due to the lawyer-applicant'"'"'s legal training.

5. The method of claim 4, wherein the question is one relating to the meaning of a Latin phrase.

6. The method of claim 1 wherein the knowledge domain comprises facts with which lawyers are more likely to be familiar than non-lawyers.

7. The method of claim 1 wherein the knowledge domain comprises facts with which the applicant is likely to be more familiar than members of the general public, by reason of applicant'"'"'s employment.

8. The method of claim 1 wherein the knowledge domain comprises facts with which the applicant is likely to be more familiar than other members of the general public, by reason of applicant'"'"'s education.

9. The method of claim 1 wherein the knowledge domain comprises facts with which the applicant is likely to be more familiar than other members of the general public, by reason of applicant'"'"'s current or prior residence address.

10. The method of claim 9 wherein said posing includes presenting to the applicant a photograph or map depicting a neighborhood scene.

11. The method of claim 10 wherein the method includes posing to the applicant plural questions testing the applicant'"'"'s familiarity with the neighborhood depicted in said photograph or map.

12. The method of claim 1 that further includes storing data relating to a corpus of plural questions in a second data structure, each question data having at least two classification data associated therewith, said classification data being useful in identifying applicants who are likely to be able to answer questions based on said question data.

13. The method of claim 12 wherein one classification data relates to a geographic location.

14. The method of claim 12 wherein one classification data relates to an expertise in law.

15. The method of claim 12 wherein one classification data relates to age.

16. The method of claim 12 wherein one classification data relates to education.

17. The method of claim 12 that further includes:

modeling a multi-dimensional vector space in which a distance between a particular applicant and a particular question data can be gauged, the gauging of said distance including assessing information about said particular applicant stored in the first data structure, in conjunction with classification data associated with said particular question data stored in the second data structure;

wherein relative proximity between a particular applicant and particular question data suggests that said applicant is relatively likely to be able to correctly answer questions based on said question data;

gauging the relative distance between the particular applicant and plural question data in the corpus;

determining thereby a subset of question data stored in the corpus, said subset comprising question data particularly suited for said applicant; and

posing questions to said particular applicant, based on question data in said determined subset.