Individual Identification by Attribute

• US 20080228768A1
• Filed: 03/12/2008
• Published: 09/18/2008
• Est. Priority Date: 03/16/2007
• Status: Abandoned Application

• Current Assignee: 23andme Incorporated (23andMe Holding Co.)
• Original Assignee: Expanse Networks, Inc.
• Inventors: Eldering, Charles Anthony, Kenedy, Andrew Alexander
• Application Number:
  US12/047,203
• Publication Number:
  US 20080228768A1
• Time in Patent Office: Days
• US Class Current: 707/6
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• 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 16/9538 Presentation of query results
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/40 Population genetics; Linkage disequilibrium
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 : relating to practices or guidelines
• CPC Class Codes: H04L 67/306 : User profiles

Abstract

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individual who might benefit from being connected.

Claims

1. A method for identifying a potential partner, comprising:

a) identifying a first set of complementary partner attributes;

b) identifying a second set of similar partner attributes;

c) accessing a database containing attributes for a population; and

d) identifying at least one potential partner having both the first set of complementary partner attributes and the second set of similar partner attributes.

2. The method of claim 1, wherein the complementary partner attributes are pangenetic attributes.

3. The method of claim 1, wherein the complementary partner attributes are non-pangenetic attributes.

4. The method of claim 1, wherein the similar partner attributes are pangenetic attributes.

5. The method of claim 1, wherein the complementary partner attributes and similar partner attributes are pangenetic attributes.

6. The method of claim 1, wherein the complementary partner attributes and similar partner attributes are non-pangenetic attributes.

7. The method of claim 1, wherein the complementary partner attributes contain both pangenetic and non-pangenetic attributes.

8. The method of claim 1, wherein the similar partner attributes contain both pangenetic and non-pangenetic attributes.

9. The method of claim 1, wherein the complementary partner attributes and the similar partner attributes contain both pangenetic and non-pangenetic attributes.

10. The method of claim 1, wherein identifying the first set of complementary partner attributes includes accessing data related to a set of successful relationships.

11. The method of claim 1, wherein identifying the second set of similar partner attributes includes accessing data related to a set of successful relationships.

12. A method for identifying a potential partner, comprising:

a) receiving a user identifier;

b) retrieving a user attribute profile, wherein the user attribute profile contains pangenetic and non-pangenetic attributes;

c) accessing a database of potential partner attribute profiles, wherein the potential partner attribute profiles contain pangenetic and non-pangenetic attributes;

d) defining a set of complementary partner attributes and a set of similar partner attributes; and

e) determining a set of potential partners having the defined set of complementary partner attributes and the defined set of similar partner attributes.

13. The method of claim 12, wherein the set of complementary partner attributes includes pangenetic attributes defining at least a portion of the major histocompatibility complex.

14. The method of claim 12, wherein the set of complementary partner attributes includes pangenetic attributes defining a set of favorable reproduction attributes.

15. The method of claim 12, wherein the set of similar partner attributes is defined based on empirical studies.

16. A system for identifying potential partners for a user, comprising:

a) a user interface for receiving a user ID and for collecting data related to user relationships;

b) a profiler for establishing a user attribute profile, wherein the user attribute profile is associated with the user and wherein the user attribute profile contains pangenetic and non-pangenetic attributes;

c) a database containing potential partner attribute profiles, wherein the potential partner attribute profiles contain pangenetic and non-pangenetic attributes; and

d) a processor for matching the user attribute profile with one or more potential partner attribute profiles wherein the matching is performed based on matching a set of complementary attributes and a set of similar attributes.

17. A method for establishing connections within a genetic based social network, comprising;

a) accessing a first user attribute profile wherein the first user attribute profile contains pangenetic attributes;

b) accessing a second user attribute profile wherein the second user attribute profile contains pangenetic attributes; and

c) determining a degree of separation between the first user and the second user.

18. The method of claim 17, wherein the determination of the degree of separation between the first user and the second user is based on a predetermined number of pangenetic attributes.

19. The method of claim 17, wherein the determination of the degree of separation between the first user and the second user is based on a predetermined number of non-pangenetic attributes.

20. The method of claim 17, further comprising:

d) issuing an invitation to the second user to establish a connection to the first user when the degree of separation between the first user and the second user is less than a predetermined threshold.

21. A method of establishing a genetic based social network, comprising:

a) establishing a set of non-pangenetic attributes as the basis for establishment of a group within the social network;

b) accessing a database containing attribute profiles for a plurality of individuals, wherein the attribute profiles contain pangenetic and non-pangenetic attributes;

c) identifying a set of individuals having the set of non-pangenetic attributes;

d) inviting one or more of the identified individuals to join the group; and

e) creating links between individuals accepting the invitation to join the group wherein the links allow for analysis of the pangenetic attributes of the group.

22. The method of claim 21, further comprising:

f) determining one or more sets of overlapping pangenetic attributes for members in the group.

23. A method of establishing genetic links within a social network, comprising:

a) receiving a request from a first user within a social network to perform a comparison of pangenetic data with a second user within the social network, wherein the first user and the second user are connected by n degrees of non-pangenetic attribute separation;

b) performing a comparison of the pangenetic attributes of the first user with the pangenetic attributes of the second user to determine a degree of pangenetic separation; and

c) storing the degree of pangenetic separation associated with the first user and the second user along with the degree of non-pangenetic association associated with the first user and the second user.

24. The method of claim 23, wherein the first user and the second user are known to each other.

25. The method of claim 23, wherein the first user and the second user are unknown to each other.

26. A genetic based social network, comprising:

a) a database containing attribute profiles for a plurality of individuals, wherein the attribute profiles contain pangenetic and non-pangenetic attributes;

b) a linking subsystem for maintaining links between the individuals; and

c) a comparison engine for determining pangenetic overlap between the individuals.