Machine implemented methods of ranking merchants

• US 8,126,779 B2
• Filed: 11/21/2007
• Issued: 02/28/2012
• Est. Priority Date: 04/11/1999
• Status: Active Grant

• Current Assignee: William Paul Wanker
• Original Assignee: William Paul Wanker
• Inventors: Wanker, William Paul
• Primary Examiner(s): Allen, Will
• Application Number:
  US11/944,145
• Publication Number:
  US 20080071638A1
• Time in Patent Office: 1,560 Days
• Field of Search: 705 261- 272, 707703-735
• US Class Current: 705/26.1
• CPC Class Codes: G06F 17/40 Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0623 Item investigation
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

In a consumer information system, a method of comparing products and services including providing product specific information, providing merchant specific information on the specified product, providing merchant specific business information, providing product specific information collected by third parties, providing merchant specific information collected by third parties, applying weighting factors to the information provided, producing an aggregate value for each merchant based on the weighting factors as applied to the information provided, and producing a ranking of the merchants offering the specified product based on the aggregate value for each merchant. The system provides for consumers to select a set of standardized weighting factors or to modify the standardized weighting factors or to create their own weighting factors. The system provides detailed information to consumers on the breakdown of factors influencing the rating.

Claims

1. A machine-implemented method of ranking merchants, the method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a potential consumer transaction, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

prior to receiving the set of weighting factors, outputting to the consumer data identifying a plurality of suggested weighting factor paradigms, each said paradigm representing a plurality of suggested weighting factors for possible use as said set of weighting factors corresponding to stored comparison data, each said paradigm representing a different preference set;

receiving the query;

accessing the stored comparison data in response to the query;

generating a ranking of the merchants, by a data processing device, based on the set of weighting factors and the comparison data; and

returning the ranking to the consumer as a response to the query.

2. A method as recited in claim 1, wherein the comparison data includes non-opinion data.

3. A method as recited in claim 1, wherein generating the ranking comprises:

calculating a score for each of the merchants, based on the set of weighting factors and the comparison data, to produce a plurality of scores, wherein the ranking is based on the scores.

4. A method as recited in claim 1, further comprising:

prior to receiving the set of weighting factors, outputting a set of suggested weighting factors to the consumer, for use as said set of weighting factors corresponding to stored comparison data.

5. A method as recited in claim 4, further comprising:

enabling the consumer to accept or modify any of the set of suggested weighting factors before using the set of suggested weighting factors.

6. A method as recited in claim 5, further comprising:

receiving from the consumer a user input specifying a change to one of the suggested weighting factors; and

maintaining a record of said change in a historical record of consumer input information.

7. A method as recited in claim 6, generating or updating a set of suggested weighting factors for use in relation to a subsequent query from a consumer, based on the historical record.

8. A method as recited in claim 1, wherein at least one of the weighting factors corresponds to a predefined category of stored comparison data, the predefined category including a plurality of comparison data items each of which also has a separate individual weighting factor.

9. A method as recited in claim 1, wherein the ranking comprises a list of merchants and returning the ranking to the consumer comprises causing the list of merchants to be output to the consumer;

and wherein the method further comprises;

for each merchant in the list, causing a plurality of category indicators to be output to the consumer in association with the corresponding merchant, each category indicator representing a different category of comparison data; and

causing a corresponding category score or rank to be output to the consumer in association with each said category indicator.

10. A method as recited in claim 9, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, causing information representing a detailed breakdown of the corresponding category score or rank of the category to be output to the consumer, wherein said information representing a detailed breakdown of the corresponding category score or ranking includes a plurality of comparison data items for the category and a plurality of corresponding scores or ranks for the data items.

11. A method as recited in claim 9, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, resorting the list based on the category corresponding to the selected indicator.

12. A method as recited in claim 1, further comprising enabling a first user to lock at least one of the weighting factors so that the weighting factor cannot be altered by another user.

13. A method as recited in claim 1, wherein the query specifies a group of products or services, and wherein the ranking of merchants is generated by applying the set of weighting factors to the comparison data, relative to the group of products or services as a whole.

14. A machine-implemented method of ranking merchants, the method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a product or service, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

prior to receiving the set of weighting factors, outputting to the consumer data identifying a plurality of suggested weighting factor paradigms, each said paradigm representing a plurality of suggested weighting factors for possible use as said set of weighting factors corresponding to stored comparison data, each said paradigm representing a different preference set;

receiving the query;

accessing the stored comparison data in response to the query, wherein the comparison data includes non-opinion data;

generating, by a data processing device, a ranking of the merchants based on the set of weighting factors and the comparison data; and

returning the ranking to the consumer as a response to the query, including calculating a score for each of the merchants, based on the set of weighting factors and the comparison data, to produce a plurality of scores, wherein the ranking is based on the scores.

15. A method as recited in claim 14, further comprising:

prior to receiving the set of weighting factors, outputting a set of suggested weighting factors to the consumer, for use as said set of weighting factors corresponding to stored comparison data.

16. A method as recited in claim 15, further comprising:

enabling the consumer to accept or modify any of the set of suggested weighting factors before using the set of suggested weighting factors.

17. A method as recited in claim 16, further comprising:

receiving from the consumer a user input specifying a change to one of the suggested weighting factors; and

maintaining a record of said change in a historical record of consumer input information.

18. A method as recited in claim 17, generating or updating a set of suggested weighting factors for use in relation to a subsequent query from a consumer, based on the historical record.

19. A method as recited in claim 14, wherein at least one of the weighting factors corresponds to a predefined category of stored comparison data, the predefined category including a plurality of comparison data items each of which also has a separate individual weighting factor.

20. A method as recited in claim 14, wherein the ranking comprises a list of merchants and returning the ranking to the consumer comprises causing the list of merchants to be output to the consumer;

and wherein the method further comprises;

for each merchant in the list, causing a plurality of category indicators to be output to the consumer in association with the corresponding merchant, each category indicator representing a different category of comparison data; and

causing a corresponding category score or rank to be output to the consumer in association with each said category indicator.

21. A method as recited in claim 20, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, causing information representing a detailed breakdown of the corresponding category score or rank of the category to be output to the consumer, wherein said information representing a detailed breakdown of the corresponding category score or ranking includes a plurality of comparison data items for the category and a plurality of corresponding scores or ranks for the data items.

22. A method as recited in claim 20, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, resorting the list based on the category corresponding to the selected indicator.

23. A method as recited in claim 14, further comprising enabling a first user to lock at least one of the weighting factors so that the weighting factor cannot be altered by another user.

24. A method as recited in claim 14, wherein the query specifies a group of products or services, and wherein the ranking of merchants is generated by applying the set of weighting factors to the comparison data, relative to the group of products or services as a whole.

25. A machine-implemented method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a potential consumer transaction, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

prior to receiving the set of weighting factors,

outputting a set of suggested weighting factors to the consumer, for use as said set of weighting factors corresponding to stored comparison data;

enabling the consumer to accept or modify any of the set of suggested weighting factors before using the set of suggested weighting factors;

receiving from the consumer a user input specifying a change to one of the suggested weighting factors;

maintaining a record of said change in a historical record of consumer input information;

generating or updating a set of suggested weighting factors for use in relation to a subsequent query from a consumer, based on the historical record;

receiving the query;

accessing the stored comparison data in response to the query;

generating, by a data processing device, a ranking of the merchants based on the set of weighting factors and the comparison data; and

returning the ranking to the consumer as a response to the query.

26. A machine-implemented method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a potential consumer transaction, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

receiving the query;

accessing the stored comparison data in response to the query;

generating, by a data processing device, a ranking of the merchants based on the set of weighting factors and the comparison data;

returning the ranking to the consumer as a response to the query, wherein the ranking comprises a list of merchants and wherein returning the ranking to the consumer comprises causing the list of merchants to be output to the consumer;

for each merchant in the list, causing a plurality of category indicators to be output to the consumer in association with the corresponding merchant, each category indicator representing a different category of comparison data; and

causing a corresponding category score or rank to be output to the consumer in association with each said category indicator.

27. A method as recited in claim 26, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, causing information representing a detailed breakdown of the corresponding category score or rank of the category to be output to the consumer, wherein said information representing a detailed breakdown of the corresponding category score or ranking includes a plurality of comparison data items for the category and a plurality of corresponding scores or ranks for the data items.

28. A method as recited in claim 27, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, resorting the list based on the category corresponding to the selected indicator.

29. A machine-implemented method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a product or service, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

prior to receiving the set of weighting factors,

outputting a set of suggested weighting factors to the consumer, for use as said set of weighting factors corresponding to stored comparison data;

enabling the consumer to accept or modify any of the set of suggested weighting factors before using the set of suggested weighting factors;

receiving from the consumer a user input specifying a change to one of the suggested weighting factors;

maintaining a record of said change in a historical record of consumer input information;

generating or updating a set of suggested weighting factors for use in relation to a subsequent query from a consumer, based on the historical record;

receiving the query;

accessing the stored comparison data in response to the query, wherein the comparison data includes non-opinion data;

generating, by a data processing device, a ranking of the merchants based on the set of weighting factors and the comparison data; and

returning the ranking to the consumer as a response to the query, including calculating a score for each of the merchants, based on the set of weighting factors and the comparison data, to produce a plurality of scores, wherein the ranking is based on the scores.

30. A machine-implemented method comprising:

receiving a set of weighting factors from a consumer, the set of weighting factors pertaining to a query related to a product or service, the weighting factors corresponding to stored comparison data related to a plurality of merchants, wherein at least one weighting factor of the set of weighting factors has been input by the consumer prior to execution of the query;

receiving the query;

accessing the stored comparison data in response to the query, wherein the comparison data includes non-opinion data;

generating, by a data processing device, a ranking of the merchants based on the set of weighting factors and the comparison data; and

returning the ranking to the consumer as a response to the query, including calculating a score for each of the merchants, based on the set of weighting factors and the comparison data, to produce a plurality of scores, wherein the ranking is based on the scores and comprises a list of merchants, and wherein returning the ranking to the consumer comprises causing the list of merchants to be output to the consumer;

for each merchant in the list, causing a plurality of category indicators to be output to the consumer in association with the corresponding merchant, each category indicator representing a different category of comparison data; and

causing a corresponding category score or rank to be output to the consumer in association with each said category indicator.

31. A method as recited in claim 30, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, causing information representing a detailed breakdown of the corresponding category score or rank of the category to be output to the consumer, wherein said information representing a detailed breakdown of the corresponding category score or ranking includes a plurality of comparison data items for the category and a plurality of corresponding scores or ranks for the data items.

32. A method as recited in claim 31, further comprising:

receiving from the consumer a user input selecting one of the category indicators; and

in response to the user input selecting one of the category indicators, resorting the list based on the category corresponding to the selected indicator.