CONTENT AGGREGATION METHOD AND APPARATUS FOR ON-LINE PURCHASING SYSTEM

• US 20120191719A1
• Filed: 03/30/2012
• Published: 07/26/2012
• Est. Priority Date: 05/09/2000
• Status: Active Grant

• Current Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Original Assignee: CBS Interactive Inc. (Paramount Global (f/k/a ViacomCBS Inc.))
• Inventors: Musgrove, Timothy Allen, Walsh, Robin Hiroko
• Granted Patent: US 8,930,370 B2
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 30/0605 Supply or demand aggregation
• CPC Class Codes: Y10S 707/922 Communications
• CPC Class Codes: Y10S 707/948 Product or catalog
• CPC Class Codes: Y10S 707/959 Network

Abstract

The method comprises processing plural product information records from the product information sources into one or more groups based on which product information records are likely to correspond to the same product, correlating a unique product ID corresponding to the product associated with each of said groups to identify the product, comparing each identified product to categories of a taxonomy to determine a category for the identified products in the taxonomy, and determining attributes for each categorized product based on the product information records corresponding to each group, creating product specifications based on the determined attributes and storing the product specification in the corresponding determined categories of the taxonomy.

Claims

1. A computer-implemented method, performed by one or more computing devices, for aggregating product information for use in a product database including various products arranged in categories within a taxonomy, the method comprising the steps of:

receiving, by at least one of the one or more computing devices, a plurality of records;

clustering, by at least one of the one or more computing devices, the plurality of records to identify a set of records likely corresponding to a product;

scraping, by at least one of the one or more computing devices, one or more attribute values from each of the records in the set of records likely corresponding to the product;

merging, by at least one of the one or more computing devices, the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product; and

categorizing, by at least one of the one or more computing devices, the product in the taxonomy according to the one or more attribute values.

2. The method of claim 1, wherein the step of scraping one or more attribute values for each of the records in the set of records likely corresponding to the product comprises:

identifying one or more normalized attribute values for each record;

assigning each normalized attribute value a confidence rating;

identifying one or more keyword attribute values for each record; and

assigning each keyword attribute value a confidence rating; and

wherein said step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product comprises;

merging the normalized attribute values based on each normalized attribute value'"'"'s confidence rating; and

merging the keyword attribute values based on each keyword value'"'"'s confidence rating.

3. The method of claim 1, further comprising:

clustering a set of products together having a threshold number of common attribute values;

generating a new category in the taxonomy corresponding to the common attribute values; and

categorizing the set of products in the new category.

4. The method of claim 3, wherein the method further comprises:

clustering a set of new categories together having a threshold number of products within each new category having a threshold number of common attribute values;

generating a new super-category in the taxonomy corresponding to the common attribute values; and

categorizing the set of new categories in the new super-category.

5. The method of claim 4, wherein each new category in the set of new categories corresponds to a product series and wherein the new super-category corresponds to a product family.

6. The method of claim 1, wherein the step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product further comprises:

generating one or more configurations for the product; and

assigning the attribute values of one of the records in the set of records likely corresponding to the product to one of the configurations of the product, and

wherein said step of categorizing the product in the taxonomy according to the one or more attribute values comprises generating one or more sub-categories below the product in the taxonomy corresponding to the configurations for the product.

7. A system for aggregating product information for use in a product database including various products arranged in categories within a taxonomy comprising:

a memory; and

a processor operatively coupled to the memory, the processor configured to perform the steps of;

receiving a plurality of records;

clustering the plurality of records to identify a set of records likely corresponding to a product;

scraping one or more attribute values from each of the records in the set of records likely corresponding to the product;

merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product; and

categorizing the product in the taxonomy according to the one or more attribute values.

8. The system of claim 7, wherein the step of scraping one or more attribute values for each of the records in the set of records likely corresponding to the product comprises:

identifying one or more normalized attribute values for each record;

assigning each normalized attribute value a confidence rating;

identifying one or more keyword attribute values for each record; and

assigning each keyword attribute value a confidence rating; and

wherein said step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product comprises;

merging the normalized attribute values based on each normalized attribute value'"'"'s confidence rating; and

merging the keyword attribute values based on each keyword value'"'"'s confidence rating.

9. The system of claim 7, wherein the method further comprises:

clustering a set of products together having a threshold number of common attribute values;

generating a new category in the taxonomy corresponding to the common attribute values; and

categorizing the set of products in the new category.

10. The system of claim 9, wherein the method further comprises:

clustering a set of new categories together having a threshold number of products within each new category having a threshold number of common attribute values;

generating a new super-category in the taxonomy corresponding to the common attribute values; and

categorizing the set of new categories in the new super-category.

11. The system of claim 10, wherein each new category in the set of new categories corresponds to a product series and wherein the new super-category corresponds to a product family.

12. The system of claim 7, wherein the step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product further comprises:

generating one or more configurations for the product; and

assigning the attribute values of one of the records in the set of records likely corresponding to the product to one of the configurations of the product, and

wherein said step of categorizing the product in the taxonomy according to the one or more attribute values comprises generating one or more sub-categories below the product in the taxonomy corresponding to the configurations for the product.

13. A non-transitory computer-readable medium having computer-readable code stored thereon that, when executed by a computing device, performs a method for aggregating product information for use in a product database including various products arranged in categories within a taxonomy, the method comprising:

receiving a plurality of records;

clustering the plurality of records to identify a set of records likely corresponding to a product;

scraping one or more attribute values from each of the records in the set of records likely corresponding to the product;

merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product; and

categorizing the product in the taxonomy according to the one or more attribute values.

14. The medium of claim 13, wherein the step of scraping one or more attribute values for each of the records in the set of records likely corresponding to the product comprises:

identifying one or more normalized attribute values for each record;

assigning each normalized attribute value a confidence rating;

identifying one or more keyword attribute values for each record; and

assigning each keyword attribute value a confidence rating; and

wherein said step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product comprises;

merging the normalized attribute values based on each normalized attribute value'"'"'s confidence rating; and

merging the keyword attribute values based on each keyword value'"'"'s confidence rating.

15. The medium of claim 13, wherein the method further comprises:

clustering a set of products together having a threshold number of common attribute values;

generating a new category in the taxonomy corresponding to the common attribute values; and

categorizing the set of products in the new category.

16. The medium of claim 15, wherein the method further comprises:

clustering a set of new categories together having a threshold number of products within each new category having a threshold number of common attribute values;

generating a new super-category in the taxonomy corresponding to the common attribute values; and

categorizing the set of new categories in the new super-category.

17. The medium of claim 16, wherein each new category in the set of new categories corresponds to a product series and wherein the new super-category corresponds to a product family.

18. The medium of claim 13, wherein the step of merging the scraped attribute values from each of the records in the set of records likely to correspond to the product to determine one or more attribute values for the product further comprises:

generating one or more configurations for the product; and

assigning the attribute values of one of the records in the set of records likely corresponding to the product to one of the configurations of the product, and

wherein said step of categorizing the product in the taxonomy according to the one or more attribute values comprises generating one or more sub-categories below the product in the taxonomy corresponding to the configurations for the product.