Methods involving artificial intelligence

• US 20080215610A1
• Filed: 04/17/2008
• Published: 09/04/2008
• Est. Priority Date: 05/30/2002
• Status: Abandoned Application

• Current Assignee: L'Oreal SA
• Original Assignee: L'Oreal SA
• Inventors: Thiria, Sylvie, Badran, Fouad, De Lacharriere, Olivier, Bastien, Philippe
• Application Number:
  US12/081,559
• Publication Number:
  US 20080215610A1
• Time in Patent Office: Days
• US Class Current: 707/102
• 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. -177. (canceled)

178. A method of generating advice, the method comprising:

accessing data organized by an artificial intelligence engine, the data being about a plurality of groups of characteristics, wherein the data comprises at least one link between at least a first group of the plurality of groups and a second group of the plurality of groups;

receiving information reflecting that a subject exhibits the first group of characteristics; and

processing the received information and the accessed data,

wherein the processing generates advice related to the subject'"'"'s predisposition to exhibit the second group of characteristics.

179. The method of claim 178, wherein the artificial intelligence engine comprises a neural network.

180. The method of claim 179, wherein the neural network comprises a Kohonen map.

181. The method of claim 179, wherein the neural network comprises a neural clustering algorithm.

182. The method of claim 181, wherein the neural clustering algorithm is chosen from a dynamic cluster method, a k-means clustering algorithm, and a hierarchical clustering algorithm.

186. The method of claim 178, wherein at least one of the first group and the second group comprises only a single characteristic.

187. The method of claim 178, wherein the received information comprises qualitative information.

188. The method of claim 178, further comprising binary encoding the received information.

189. The method of claim 188, wherein the binary encoding comprises at least one of unconstrained binary encoding, additive binary encoding, and disjunctive binary encoding.

190. The method of claim 188, wherein the accessed data comprises binary encoded data, and wherein the processing comprises selecting a portion of the binary encoded accessed data most closely resembling the binary encoded received information.

193. The method of claim 178, wherein the accessed data comprises qualitative data.

194. The method of claim 178, wherein the accessed data comprises binary encoded data.

196. The method of claim 194, wherein the binary encoded data is binary encoded using at least one of unconstrained binary encoding, additive binary encoding, and disjunctive binary encoding.

198. The method of claim 194, wherein the accessed data is in the form of a profile data set representative of the plurality of groups.

202. The method of claim 178, wherein the advice is related to the subject being likely to exhibit the second group of characteristics.

203. The method of claim 178, wherein the advice is related to the subject being unlikely to exhibit the second group of characteristics.

209. The method of claim 178, wherein the advice comprises information about at least one product.

211. The method of claim 209, further comprising

offering at least one product for sale to the subject.

212. The method of claim 209, wherein the processing comprises determining that the subject has a predisposition to exhibit a condition, and wherein the at least one product comprises a product for treating the condition.

214. The method of claim 178, further comprising

accessing a plurality of queries, and

presenting to the 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 the subject'"'"'s answer to a previous query.

216. The method of claim 214, wherein at least one of the queries prompts the subject to indicate whether at least some aspects of an initial profile resemble at least some aspects of the subject.

217. The method of claim 178, wherein the method further comprises presenting at least one query to the subject and wherein the receiving information comprises receiving at least one answer from the subject.

221. The method of claim 178, wherein the artificial intelligence engine comprises at least one of a neural network, constraint program, fuzzy logic program, classification program, and logic program.

256. 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 178.

263. A computer program product, comprising a computer-readable medium, wherein the computer-readable medium contains instructions for executing the method of claim 178.

183. -185. (canceled)

191. (canceled)

192. (canceled)

195. (canceled)

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199. -201. (canceled)

204. -208. (canceled)

210. (canceled)

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257. -262. (canceled)

264. (canceled)