Method for implementing search engine

• US 7,685,117 B2
• Filed: 06/07/2004
• Issued: 03/23/2010
• Est. Priority Date: 06/05/2003
• Status: Active Grant

• Current Assignee: Xylon LLC (Intellectual Ventures LLC)
• Original Assignee: Hayley Logistics LLC (Intellectual Ventures LLC)
• Inventors: Gross, John N.
• Primary Examiner(s): Trujillo; James
• Assistant Examiner(s): SOMERS, MARC S
• Application Number:
  US10/862,929
• Publication Number:
  US 20040260688A1
• Time in Patent Office: 2,115 Days
• Field of Search: 707/3, 707/5, 705/10, 705/26, 705/27
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9566 URL specific, e.g. using aliases, detecting broken or misspelled links
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A system and method for implementing/influencing a search engine which provides search results to users based on characteristics of certain trendsetter web pages identified on the Internet. The trendsetter web page are determined by studying historical adoption behavior of a group within the universe of websites, or by reference to known indicia.

Claims

1. A method of influencing behavior of an electronic World Wide Web (WWW) Internet search engine, to identify search items in response to a query from an online network user, comprising:

compiling a set of N popular items {X₁, X₂, . . . , X_i, . . . , X_N},

wherein for each popular item X_i amongst the set of N popular items, a plurality of M early adopter web pages {Y[X_i]₁. . . Y[X_i]_M} is determined resulting in N separate sets of M early adopter web pages, said M early adopter web pages being determined based on a time of adoption of said popular item X_i at said M early adopter web pages;

correlating said N separate sets of M early adopter web pages to identify a trend predictor value of each of the early adopter web pages and to determine which of the early adopter web pages occur most frequently in the set {Y[X_i]_j}, where 1≦

i≦

N and 1≦

j≦

M,

wherein said most frequently occurring early adopter web pages are qualified as trend predictors;

measuring an adoption rating for a first search item identified in a set of web pages, said set of web pages including one or more web pages in said N separate sets of at least M early adopter web pages; and

responding to a query from a user for search items at least in part based on said adoption rating for said first search item.

2. The method of claim 1, wherein said M early adopter web pages are aggregated to identify trend predictor websites.

3. The method of claim 2, wherein said trend predictor websites are ranked and identified in an online viewable list.

4. The method of claim 1, wherein said items are uniform resource locators (URLs).

5. The method of claim 1, wherein said items are electronic documents.

6. The method of claim 1, wherein said items are hypertext links to electronic documents.

7. The method of claim 1, wherein said items are products.

8. The method of claim 1, wherein said items are services.

9. The method of claim 1, wherein said adoption rating is scaled in accordance with an adoption rank achieved by a web page.

10. The method of claim 1, further comprising:

measuring a change in prevalence rate for the first search item among the set of N popular items for a newly introduced item.

11. The method of claim 10, further comprising:

generating a popularity prediction rating for the newly introduced item.

12. A method of classifying web pages, comprising:

using a computer to identify a set of web pages;

extracting, electronically, a plurality of M early adopter web pages from said set of web pages, which plurality of M early adopter web pages are determined by ranking said set of web pages in accordance with a time in which they first make a reference to a predetermined set of N popular items {X₁, X₂, . . . , X_i, . . . , X_N},

wherein for each popular item X_i amongst the set of N popular items, the set of M early adopter web pages {Y[X_i]₁ . . . Y[X_i]_M} is determined, resulting in N separate sets of M early adopter web pages;

correlating, electronically, said N separate sets of M early adopter web pages to identify a trend predictor value of each of the early adopter web pages and to determine which of the early adopter web pages occur most frequently in the set {Y[X_i]_j}, where 1≦

i≦

N and 1≦

j≦

M,

wherein said most frequently occurring early adopter web pages are qualified as trend predictors; and

increasing a ranking for a web page in response to a search request posed to a search engine when said web page is from said set of M early adopter web pages.

13. The method of claim 12, further comprising:

generating a demand score identifying a predicted demand for a selected item by measuring a prevalence rate for said selected item across said set of M early adopter web pages.

14. The method of claim 13, wherein said demand score is generated to compile a set of search items responsive to a search query presented to a search engine.

15. The method of claim 12, further comprising:

identifying said predetermined set of N popular items by computing which of a group of items have achieved a prevalence rate on said set of web pages which exceeds a threshold value.

16. The method of claim 12, further comprising:

identifying said predetermined set of N popular items by identifying which of a group of items have achieved a popularity rating on the Internet which exceeds a threshold value.

17. The method of claim 12, further comprising:

monitoring said plurality of M early adopter web pages to identify new references to new items.

18. A method of influencing behavior of an electronic search engine system, to provide results to queries initiated by a community of online users of the World Wide Web, comprising:

identifying N popular items {X₁, X₂, . . . , X_i, . . . , X_N}, as measured by interest demonstrated by online users for such items,

wherein for each popular item X_i amongst the N popular items, a plurality of M early adopter web pages {Y[X_i]₁. . . Y[X_i]_M} is determined resulting in N separate sets of M early adopter web pages;

correlating said N separate sets of M early adopter web pages to identify a trend predictor value of each of the early adopter web pages and to determine which of the early adopter web pages occur most frequently in the set {Y[X_i]_j}, where 1≦

i≦

N and 1≦

j≦

M,

wherein said most frequently occurring early adopter web pages are designated as trend predictor web pages;

measuring a trend predictor web page adoption rating for a particular item;

modifying a search result provided by the search engine to an online user based on said adoption rating,

wherein said adoption rating is used, at least during certain time periods, to influence which search results are presented to a user query;

eliminating statistically said trend predictor web pages that have adopted over a certain threshold of popular items, to avoid biasing of the search result; and

presenting online advertising to users of trend predictor web pages based on said trendsetter web page adoption rating.

19. The method of claim 18, wherein said M early adopter web pages are grouped into sets in accordance with a plurality of different categories of items.

20. The method of claim 19, wherein a different set of M early adopter web pages are used on a search by search basis.

21. The method of claim 18, wherein said M early adopter web pages are grouped into sets in accordance with a plurality of different categories of online users.

22. The method of claim 18, wherein said M early adopter web pages are web pages affiliated with one or more individuals identified as trendsetter.