Methods involving artificial intelligence

• US 7,366,707 B2
• Filed: 05/29/2003
• Issued: 04/29/2008
• Est. Priority Date: 05/30/2002
• Status: Expired due to Fees

• Current Assignee: L'Oreal SA
• Original Assignee: L'Oreal SA
• Inventors: Thiria, Sylvie, de Lacharriere, Olivier, Badran, Fouad, Bastien, Philippe
• Primary Examiner(s): Holmes; Michael B.
• Application Number:
  US10/446,926
• Publication Number:
  US 20040034610A1
• Time in Patent Office: 1,797 Days
• Field of Search: 706/12, 706/62
• US Class Current: 706/62
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires

Abstract

One aspect of the present invention relates to methods of generating a profile data set. In an exemplary embodiment, data is accessed and the accessed data is processed using a dynamic cluster method, mobile center method, and/or a k-means algorithm, each using neighborhood data. Other aspects relate to methods of generating a diagnosis, advice, and/or other information. Further aspects relate to dynamic surveying and systems.

Claims

1. A dynamic survey method;

comprising;

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries, wherein, for at least some of the queries presented, the method further comprises selecting a next query as a function of at least one answer to a previous query, and

wherein the accessed data comprises information identifying characteristics and information for predicting evolution of the characteristics.

16. The method of claim 1, wherein during organization of the accessed data by the artificial intelligence engine, the artificial intelligence engine produces data representing modalities of characteristics in the accessed data.

17. The method of claim 16, wherein the data representing modalities of the characteristics is binary data.

18. The method of claim 16, wherein the data representing modalities is produced by the artificial intelligence engine using at least one of an unconstrained binary coding, additive binary coding, and disjunctive binary coding.

2. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries, wherein, for at least some of the queries presented, the method further comprises selecting a next query as a function of at least one answer to a previous query, and

wherein the accessed data comprises a profile data set.

3. The method of claim 2, wherein at least one member in the profile data set is excluded based on the at least one answer to the previous query and the next query is selected to exclude at least one additional member in the profile data set.

4. The method of claim 3, wherein at least one member remaining in the profile data set is used to provide a diagnostic of the subject.

5. The method of claim 3, wherein at least one member remaining in the profile data set is used to provide advice to the subject.

6. The method of claim 3, wherein at least one member remaining in the profile data set is used to provide information to the subject.

8. The method of claim 2, wherein the next query is selected to narrow a portion of the profile data set.

9. The method of claim 8, wherein the next query is selected to present the fewest number of queries to the subject before using at least one member remaining in the profile data set to provide at least one of a diagnostic, advice, and information to the subject.

10. The method of claim 2, wherein during organization of the accessed data by the artificial intelligence engine, the artificial intelligence engine produces data representing modalities of characteristics in the accessed data.

11. The method of claim 10, wherein the data representing modalities of the characteristics is binary data.

12. The method of claim 11, wherein the data representing modalities is produced by the artificial intelligence engine using at least one of an unconstrained binary coding, additive binary coding, and disjunctive binary coding.

13. The method of claim 2, wherein during organization of the accessed data by the artificial intelligence engine, the artificial intelligence engine uses neighborhood data.

14. The method of claim 2, wherein the artificial intelligence engine uses at least one of a dynamic cluster method, k-means algorithm, and hierarchical clustering algorithm.

15. The method of claim 14, wherein the artificial intelligence engine uses neighborhood data.

7. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries, wherein, for at least some of the queries presented, the method further comprises selecting a next guery as a function of at least one answer to a previous guery, and

wherein the next query is selected to narrow a portion of the accessed data.

19. The method of claim 7, wherein at least one of accessing the data, accessing the queries, and presenting the subset to the subject is performed over a network.

23. The method of claim 7, wherein the next query is selected sequentially.

25. The method of claim 7, wherein the next query is selected sequentially through a sequence of queries.

26. The method of claim 7, wherein the artificial intelligence engine is at least one of a neural network, constraint program, fuzzy logic program, classification program, and logic program.

27. The method of claim 7, wherein the next query is selected to present the fewest number of queries to the subject before using at least one member remaining in the profile data set to provide at least one of a diagnostic, advice, and information to the subject.

29. The method of claim 7, wherein at least one of the queries is selected using the artificial intelligence engine.

30. The method of claim 7, wherein at least one answer to at least some of the queries are used to process at least some of the accessed data.

33. A system, comprising:

a data processor; and

a storage medium functionally coupled to the data processor, wherein the storage medium contains instructions to be executed by the data processor for performing the method of claim 7.

34. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method of claim 7.

20. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries,

wherein, for at least some of the queries presented, the method further comprises selecting a next query as a function of at least one answer to a previous query, and

wherein the accessed data is organized using neighborhood data.

21. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries,

wherein, for at least some of the queries presented, the method further comprises selecting a next query as a function of at least one answer to a previous query, and

wherein the accessed data is organized using at least one of a dynamic cluster method, k-means algorithm, and hierarchical clustering algorithm.

22. The method of claim 21, wherein the accessed data is organized using neighborhood data.

24. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries, wherein, for at least some of the queries presented, the method further comprises selecting a next guery as a function of at least one answer to a previous guery, wherein the next guery is selected seguentially.

wherein at least one answer to at least one query is used to narrow a portion of the accessed data, and wherein the next query is selected sequentially and presented to the subject such that at least a portion of remaining accessed data is used to provide at least one of a diagnostic, advice, and information to the subject.

31. A system, comprising:

a data processor; and

a storage medium functionally coupled to the data processor, wherein the storage medium contains instructions to be executed by the data processor for performing the method of claim 24.

32. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method of claim 24.

28. A dynamic survey method, comprising:

accessing data organized by an artificial intelligence engine;

accessing queries; and

presenting to a subject a subset of queries from the accessed queries, wherein, for at least some of the queries presented, the method further comprises selecting a next query as a function of at least one answer to a previous query, and

wherein at least one of the queries is selected using an additional artificial intelligence engine.