Neural-based internet search engine with fuzzy and learning processes implemented by backward propogation

• US 20050055340A1
• Filed: 09/16/2003
• Published: 03/10/2005
• Est. Priority Date: 07/26/2002
• Status: Abandoned Application

• Current Assignee: Brainbow Incorporated
• Original Assignee: Brainbow Incorporated
• Inventors: Dresden, Scott
• Application Number:
  US10/664,787
• Publication Number:
  US 20050055340A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G02B 6/02195 characterised by means for tuning the grating
• CPC Class Codes: G02B 6/2932 comprising a directional router, e.g. directional coupler, circulator
• CPC Class Codes: G02B 6/29394 Compensating wavelength dispersion G02B6/29376 takes precedence; dispersion compensated optical transmission systems H04B10/2507
• CPC Class Codes: G02B 6/29395 configurable, e.g. tunable or reconfigurable switching G02B6/35
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: H04B 10/2519 using Bragg gratings

Abstract

The present invention provides an Internet search engine system and method that improves searching for documents or pages by processing the characteristics of a pool of data through a neural network governed by a set of rules and fuzzy logic applications. The rules and applications may be implemented at the input (or low) level or the computational/output (or high) level. Search terms and personal and situational data may activate various rule sets, and learning from human and machine feedback adjust and recombine the rule sets to improve accuracy for future searches as well as reduce computation time.

Claims

1. A method for processing a search request including the steps of:

determining if a search request activates at least one of a set of search rules;

if said search request activates said at least one search rule, then applying said search rule;

setting a set of input weight adjustments based on said at least one search rule;

processing a set of inputs responsive to a collection of data, said set of inputs adjusted by said set of weight adjustments, said processing resulting in a set of filtered data; and

adapting a search engine based on learning, said learning including at least comparing said set of filtered data to either a set of previously filtered data or a feedback mechanism.

2. The method as recited in claim 1, wherein said set of search rules is adapted for a future search request.

3. The method as recited in claim 1, wherein said search request is adapted to activate an alternate search rule in said set of search rules.

4. The method as recited in claim 1, wherein said search request is adapted to not activate said search rule.

5. The method as recited in claim 1, wherein said search request is adapted to activate a portion of said search rule.

6. The method as recited in claim 1, further including the step of loading user data wherein said search rule may be activated by said user data.

7. The method as recited in claim 1, further including the step of accessing external data, wherein said search rule may also be activated or altered by said user data.

28. The method recited in claim 1 further comprising the act of setting screening rules based on said at least one search rule and

adjusting said set of filtered data according to said screening rules to produce a subset of filtered data.

29. The method as recited in claim 28 wherein said adapting step includes comparison of said subset of filtered data to any previously returned subset of filtered data.

8. A search engine apparatus comprising:

a computing device with at least one processor operatively coupled to an interface having an input and output, said computing device connected to at least one data storage device and an internal temporary storage device, said data storage including a set of data characteristics;

a set of one or more input nodes capable of accessing said data, each responding to at least one of said set of data characteristics;

a first module executable on said computing device for processing output responses from said set of one or more input nodes;

a second module executable on said computing device for generating and applying a set of rules, said set of rules including control of said set of one or more input nodes, said second module including at least one contingent set inclusion rule;

an adaptation module executable on said computing device responsive to processed responses from said first module and a set of one or more learning mechanisms, said adaptation mechanism providing said second module with at least one of a confirmed, new or updated rule; and

wherein a search result is generated by one or more rules from said set of rules and being applied to said processed response and provided to a user via said output.

9. The search engine apparatus as recited in claim 8, wherein said set of one or more input nodes is a virtual program executed by said first module.

10. The search engine as recited in claim 8, wherein said data storage device is connected to the Internet and includes a connection to a computing device with capability of gathering data from individual Internet sites.

11. The search engine as recited in claim 8, wherein said control includes at least an activation function, said activation function selecting a subset of said set of one or more input nodes.

12. The search engine as recited in claim 8 wherein said control includes at least a weighting function.

13. The search engine as recited in claim 8, wherein said first module includes at least one routine executable on said computing device independently of any other routines in said first module, said at least one routine responsive to said adaptation and for processing at least a portion of said responses.

14. The search engine as recited in claim 13, wherein said at least one routine may be combined with at least one second routine executable on said computing device.

15. The search engine as recited in claim 8, wherein said data characteristics include characteristics related to content.

16. The search engine as recited in claim 8, wherein said data characteristics include characteristics related to site performance.

17. The search engine as recited in claim 8, wherein said data characteristics include characteristics related to evaluations provided by users.

18. The search engine as recited in claim 8, wherein said data characteristics include characteristics related to keywords.

19. The search engine as recited in claim 8, wherein said second module is capable of accessing data regarding a user, said data regarding a user activating at least one of said set of rules.

20. The search engine as recited in claim 8, wherein said second module is capable of accessing data regarding a scenario, said data regarding a scenario being for activating a least one of said set of rules.

21. The search engine as recited in claim 8, wherein said learning mechanism includes a user feedback mechanism operatively coupled to said adaptation module, said feedback mechanism providing user input related to a search result.

22. The search engine as recited in claim 8, wherein said learning mechanism includes a user behavior tracking mechanism operatively coupled to said adaptation module, said behavior tracking mechanism for tracking behavior related to a search result.

23. The search engine as recited in claim 8, wherein said learning mechanism includes a machine learning unit operatively coupled to said adaptation module, said machine learning unit for comparing said temporarily stored result to previously stored results.

24. The search engine as recited in claim 8, further including a parameter generation unit coupled to said computing device, said parameter generation unit for accessing and storing data, wherein said second module applies rules when a signal data criteria is detected by said parameter generation unit.

25. The search engine as recited in claim 8, where said second module includes a fuzzy logic generation and execution unit.

26. The search engine as recited in claim 25, wherein said fuzzy logic generation and execution unit is operatively coupled to said set of input nodes.

27. The search engine apparatus as recited in claim 8, wherein said connection is a wide area network connection, a local area network connection, an Ethernet connection, a DSL connection, a T1 line connection, a T3 connection, a cable modem connection, or a modem connected through a phone line.

30. A method for finding a document or page located on a network through a uniform resource locator in which a search engine including executable instructions running on one or more computing devices evaluates data regarding the characteristics of a set of said pages or documents and returns a set of one or more relevant documents in response to a search inquiry consisting of search terms wherein the improvement includes using a neural network to evaluate said data and return said set of one or more relevant documents, said neural network being virtual and trainable.

31. The method as recited in claim 30, wherein fuzzy logic is applied to said neural network at either a low level or high level or both.

32. The method as recited in claim 30, wherein said neural network is controlled by a set of one or more expert rules either directly or indirectly through fuzzy logic or both.

33. The method as recited in claim 32, wherein said set of one or more expert rules is activated by user data.

34. The method as recited in claim 32, wherein said set of one or more expert rules is activated by at least a portion of said search inquiry.

35. The method as recited in claim 30, wherein said act of training said neural network includes evaluating said set of one or more relevant documents by either comparing said set of one or more relevant documents to a previously returned search result or through a feedback mechanism.