Creation of Attribute Combination Databases Using Expanded Attribute Profiles

• US 20080228797A1
• Filed: 08/08/2007
• 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:
  US11/835,794
• Publication Number:
  US 20080228797A1
• Time in Patent Office: Days
• US Class Current: 707/101
• 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 and system for compiling attribute combinations are presented in which expanded attribute profiles associated with a group of query-attribute-positive individuals and expanded attribute profiles associated with a group of query-attribute-negative individuals are accessed, and combinations of attributes having a higher frequency of occurrence in the set of expanded attribute profiles associated with the group of query-attribute-positive individuals are identified. The identified attribute combinations and corresponding statistical results can then be stored to create a compilation of attribute combinations that co-occur with the query attribute.

Claims

1. A computer based method for compiling attribute combinations, comprising:

a) receiving a query attribute;

b) accessing a set of expanded attribute profiles associated with a group of query-attribute-positive individuals, wherein the set of expanded attribute profiles comprises a set of primary attributes and a set of secondary attributes, wherein the set of secondary attributes is derived from the set of primary attributes and wherein the set of secondary attributes has lower resolution than the set of primary attributes;

c) accessing a set of expanded attribute profiles associated with a group of query-attribute-negative individuals, wherein the set of expanded attribute profiles comprises a set of primary attributes and a set of secondary attributes, wherein the set of secondary attributes is derived from the set of primary attributes and wherein the set of secondary attributes has lower resolution than the set of primary attributes;

d) identifying attribute combinations having a higher frequency of occurrence in the set of expanded attribute profiles associated with the group of query-attribute-positive individuals than in the set of expanded attribute profiles associated with the group of query-attribute-negative individuals; and

e) storing the identified attribute combinations to create a compilation of attribute combinations that co-occur with the query attribute.

2. The computer based method of claim 1, wherein storing the identified attribute combinations further comprises storing the frequencies of occurrence of the identified attribute combinations for the set of expanded attribute profiles associated with the group of query-attribute-positive individuals and for the set of expanded attribute profiles associated with the group of query-attribute-negative individuals.

3. The computer based method of claim 2 further comprising:

f) storing statistical results, generated based on the frequencies of occurrence, which indicate the strength of association of each of the identified attribute combinations with the query attribute.

4. The computer based method of claim 3, further comprising:

g) eliminating one or more attribute combinations from said compilation based on one or more statistical requirements for strength of association with the query attribute selected from the group comprising;

greater than a minimum statistical result, and statistical significance.

5. The computer based method of claim 3, further comprising:

g) ranking the attribute combinations based on the statistical results.

6. The computer based method of claim 3, further comprising:

g) transmitting one or more of the identified attribute combinations and statistical results as output.

7. The computer based method of claim 1, wherein the query attribute can be a query attribute combination consisting of two or more attributes.

8. The computer based method of claim 1, wherein the query-attribute-positive individuals and the query-attribute-negative individuals constitute a population or subpopulation that is preselected based on having an association with one or more particular attributes.

9. The computer based method of claim 1, wherein the identity of one or more individuals is masked or anonymized.

10. The computer based method of claim 1, wherein a secondary attribute encompasses a primary attribute.

11. The computer based method of claim 1, wherein at least one secondary attribute is a categorical attribute that encompasses at least one primary attribute that is a numerical attribute.

12. The computer based method of claim 1, wherein at least one secondary attribute is derived by compounding the values of two or more primary attributes.

13. The computer based method of claim 1, wherein at least one primary attribute has a continuous value and at least one secondary attribute derived from that primary attribute has a discrete value.

14. The computer based method of claim 1, wherein at least one secondary attribute is derived through a heuristic rule applied to one or more primary attributes.

15. The computer based method of claim 1, wherein at least one secondary attribute comprises an inequality statement containing a quantitative value, wherein the quantitative value is either larger or smaller than that of the primary attribute from which it was derived.

16. The computer based method of claim 1, wherein two or more of the secondary attributes comprise a sequence of inequality statements containing progressively larger quantitative values.

17. The computer based method of claim 1, wherein two or more of the secondary attributes comprise a sequence of inequality statements containing progressively smaller quantitative values.

18. A computer based system for compiling attribute combinations, comprising:

a) a data receiving subsystem for receiving a query attribute;

b) a data accessing subsystem for;

i) accessing a set of expanded attribute profiles associated with a group of query-attribute-positive individuals, wherein the set of expanded attribute profiles comprises a set of primary attributes and a set of secondary attributes, wherein the set of secondary attributes are derived from the set of primary attributes and wherein the set of secondary attributes has a lower resolution than the set of primary attributes;

ii) accessing a set of expanded attribute profiles associated with a group of query-attribute-negative individuals, wherein the set of expanded attribute profiles comprises a set of primary attributes and a set of secondary attributes, wherein the set of secondary attributes are derived from the set of primary attributes and wherein the set of secondary attributes has a lower resolution than the set of primary attributes;

c) a data processing subsystem comprising;

i) a data comparison subsystem for identifying attribute combinations having a higher frequency of occurrence in the set of expanded attribute profiles associated with the group of query-attribute-positive individuals than in the set of expanded attribute profiles associated with the group of query-attribute-negative individuals;

ii) a statistical computation subsystem for generating statistical results that indicate the strength of association of the identified attribute combinations with the query attribute based on the frequencies of occurrence of the identified attribute combinations in the set of expanded attribute profiles associated with the group of query-attribute-positive individuals and in the set of expanded attribute profiles associated with the group of query-attribute-negative individuals; and

iii) a data storage subsystem for storing the identified attribute combinations and the statistical results to create a compilation of attribute combinations that co-occur with the query attribute.

19. The computer based system of claim 18, wherein the data processing subsystem further comprises:

iv) a data filtering subsystem for eliminating attribute combinations from said compilation based on the statistical results; and

v) a data ranking subsystem for ranking the attribute combinations based on the statistical results.

20. The computer based system of claim 18, further comprising:

d) a data communications subsystem for transmitting one or more of the identified attribute combinations and one or more of the statistical results as output.