Qualitative Search Engine Based On Factors Of Consumer Trust Specification

• US 20110252015A1
• Filed: 05/16/2011
• Published: 10/13/2011
• Est. Priority Date: 07/02/2007
• Status: Abandoned Application

• Current Assignee: Kristina Butvydas Bard, Norma Ferrara
• Original Assignee: Kristina Butvydas Bard, Norma Ferrara
• Inventors: Ferrara, Norma, Bard, Kristina Butvydas
• Application Number:
  US13/108,349
• Publication Number:
  US 20110252015A1
• Time in Patent Office: Days
• US Class Current: 707/706
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

A method of providing a search engine for use on global computer networks which identifies and merges categories of information that reflect, influence and imitate intelligent choice by concurrently searching one or more of eight factors of consumer trust: books, experts, news and articles, associations, celebrities and pro choice, awards, web information and blogs and people'"'"'s choice. The results from the search of these selected consumer trust factors are then combined to generate a final report.

Claims

1. A method of providing a search engine for use on global computer networks which identifies and merges categories of information that reflect, influence and imitate intelligent choice, said method comprising the steps of:

concurrently searching a product, service or topic using one or more of psychosocial indicators of books, experts, news and articles, associations, celebrities and pro choices, awards, global computer networks information and blogs, and people'"'"'s choice to generate respective search results for each of said indicators;

arranging said search results for each of said indicators from most credible to least credible; and

combining said filtered search results of said indicators to form a report that ranks the searched product, service or topic from most preferred to least preferred.

2. The method of claim 1, wherein said step of concurrently searching comprises four layers for each of said indicators, said layers comprising:

activating building blocks, precursors and sources as a first layer to generate first layer results;

inputting said first layer results into a second layer comprising semantic and citation analysis searching to form second layer results;

inputting said second layer results into a third layer that comprises general filtering based on the respective category to form third layer results; and

inputting said third layer results into a fourth layer that comprises special filtering based on the respective category to form fourth layer results.

3. The method of claim 2, further comprising the step of generating a report for a respective indicator.

4. The method of claim 1, wherein said step of combining said search results comprises applying weights to the search results from each of said indicators.

5. The method of claim 2, wherein said special filtering for said indicator of books comprises searching best-selling statistics, date of publication, book reviews, volume in print/sales, merge of top publishers and supplier data.

6. The method of claim 2, wherein said special filtering for said indicator of experts comprises various news sources on specific products, services or topics written by known experts.

7. The method of claim 2, wherein said special filtering for said indicator of news comprises qualitative media indicator data from current demographics, audience education, circulation which determine believability.

8. The method of claim 2, wherein said special filtering for said indicator of associations comprises qualitative association data, longevity, journals, membership demographics and industry standard development.

9. The method of claim 2, wherein said special filtering for said indicator of celebrities and pro choice comprises qualitative celebrity and proc indicator data including industry rank a number of pros using the product or service.

10. The method of claim 2, wherein said special filtering for said indicator of awards comprises manufacturer data, association sites and ranking of said association sites according to their qualitative data.

11. The method of claim 2, wherein said special filtering for said indicator of global computer networks information and blogs comprises qualitative indicators of sources'"'"' believability including paid versus unpaid, longevity, volume on subject, and retailer versus manufacturer data.

12. The method of claim 2, wherein said special filtering for said indicator of people'"'"'s choice comprises real-time and archived data and polled summaries with statistical ranking for quality, design and cost.

13. A system of a search engine for use on global computer networks which identifies and merges categories of information that reflect, influence and imitate intelligent choice, said system comprising a web server that performs the steps of:

concurrently searching a product, service or topic using one or more of psychosocial indicators of books, experts, news and articles, associations, celebrities and pro choices, awards, global computer networks information and blogs, and people'"'"'s choice to generate respective search results for each of said indicators;

arranging said search results for each of said indicators from most credible to least credible; and

combining said search results of said indicators to form a report that ranks the searched product, service or topic from most preferred to least preferred.

14. The system of claim 13, wherein said step of concurrently searching comprises four layers for each of said indicators, said layers comprising:

activating building blocks, precursors and sources as a first layer to generate first layer results;

inputting said first layer results into a second layer comprising semantic and citation analysis searching to form second layer results;

inputting said second layer results into a third layer that comprises general filtering based on the respective category to form third layer results; and

inputting said third layer results into a fourth layer that comprises special filtering based on the respective category to form fourth layer results.

15. The system of claim 14, the web server further performs the step of generating a report for a respective category.

16. The system of claim 13, wherein said step of combining said search results comprises applying weights to the search results from each of said indicators.

17. The system of claim 14, wherein said special filtering for said indicator of books comprises searching best-selling statistics, date of publication, book reviews, volume in print/sales, merge of top publishers and supplier data.

18. The system of claim 14, wherein said special filtering for said indicator of experts comprises various news sources on specific products, services or topics written by known experts.

19. The system of claim 14, wherein said special filtering for said indicator of news comprises qualitative media indicator data from current demographics, audience education, circulation which determine believability.

20. The system of claim 14, wherein said special filtering for said indicator of associations comprises qualitative association data, longevity, journals, membership demographics and industry standard development.

21. The system of claim 14, wherein said special filtering for said indicator of celebrities and pro choice comprises qualitative celebrity and proc indicator data including industry rank a number of pros using the product or service.

22. The system of claim 14, wherein said special filtering for said indicator of awards comprises manufacturer data, association sites and ranking of said association sites according to their qualitative data.

23. The system of claim 14, wherein said special filtering for said indicator of global computer networks information and blogs comprises qualitative indicators of sources'"'"' believability including paid versus unpaid, longevity, volume on subject, and retailer versus manufacturer data.

24. The system of claim 14, wherein said special filtering for said indicator of people'"'"'s choice comprises real-time and archived data and polled summaries with statistical ranking for quality, design and cost.