Content aggregation method and apparatus for on-line purchasing system

• US 8,930,370 B2
• Filed: 03/30/2012
• Issued: 01/06/2015
• Est. Priority Date: 05/09/2000
• Status: Expired due to Fees

• 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
• Primary Examiner(s): SYED, FARHAN M
• Application Number:
  US13/436,801
• Publication Number:
  US 20120191719A1
• Time in Patent Office: 1,012 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;

grouping, by at least one of the one or more computing devices, one or more records of the plurality of records into a cluster, wherein the cluster corresponds to a product and the one or more records are grouped into the cluster based on a probability determination that the one or more records correspond to the 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 cluster to generate one or more scraped attribute values;

merging, by at least one of the one or more computing devices, the one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values for the product based at least in part on a confidence rating associated with at least one of the one or more scraped attribute values; and

categorizing, by at least one of the one or more computing devices, the product in the taxonomy according to the one or more product 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 cluster to generate one or more scraped attribute values 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 one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values 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 product attribute values;

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

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

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

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

generating one or more configurations for the product; and

assigning one or more scraped attribute values of one of the records in the cluster 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 product attribute values comprises generating one or more sub-categories below the product in the taxonomy, and wherein the one or more sub-categories correspond to the configurations for the product.

16. The method of claim 1, further comprising:

assigning, by at least one of the one or more computing devices, a product identifier to the cluster.

17. The method of claim 1, wherein the probability determination is based at least in part on a degree of textual overlap between the one or more product records.

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

one or more processors; and

one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to;

receive a plurality of records;

group one or more records of the plurality of records into a cluster, wherein the cluster corresponds to a product and the one or more records are grouped into the cluster based on a probability determination that the one or more records correspond to the product;

scrape one or more attribute values from each of the records in the cluster to generate one or more scraped attribute values;

merge the one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values for the product based at least in part on a confidence rating associated with at least one of the one or more scraped attribute values; and

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

7. The system of claim 6, wherein the step of scraping one or more attribute values for each of the records in the cluster to generate one or more scraped attribute values 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 one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values 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.

8. The system of claim 6, wherein at least one of the one or more memories has further instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:

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

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

categorize the set of products in the new category.

9. The system of claim 8, wherein at least one of the one or more memories has further instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:

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

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

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

10. The system of claim 6, wherein the step of merging the one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values for the product further comprises:

generating one or more configurations for the product; and

assigning one or more scraped attribute values of one of the records in the cluster 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 product attribute values comprises generating one or more sub-categories below the product in the taxonomy, and wherein the one or more sub-categories correspond to the configurations for the product.

18. The system of claim 8, wherein at least one of the one or more memories has further instructions stored thereon that, when executed by at least one of the one or more processors, cause at least one of the one or more processors to:

assign a product identifier to the cluster.

19. The system of claim 8, wherein the probability determination is based at least in part on a degree of textual overlap between the one or more product records.

11. At least one non-transitory computer-readable medium storing computer-readable instructions that, when executed by one or more a computing devices, cause at least one of the one or more computing devices to:

receive a plurality of records;

group one or more records of the plurality of records into a cluster, wherein the cluster corresponds to a product and the one or more records are grouped into the cluster based on a probability determination that the one or more records correspond to the product;

scrape one or more attribute values from each of the records in the cluster to generate one or more scraped attribute values;

merge the one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values for the product based at least in part on a confidence rating associated with at least one of the one or more scraped attribute values; and

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

12. The at least one non-transitory computer-readable medium of claim 11, wherein the step of scraping one or more attribute values for each of the records in the cluster to generate one or more scraped attribute values 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 one or more scraped attribute values from each of the records in the cluster to determine one or more product 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.

13. The at least one non-transitory computer-readable medium of claim 11, further storing computer-readable instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to:

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

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

categorize the set of products in the new category.

14. The at least one non-transitory computer-readable medium of claim 13, further storing computer-readable instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to:

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

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

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

15. The at least one non-transitory computer-readable medium of claim 11, wherein the step of merging the one or more scraped attribute values from each of the records in the cluster to determine one or more product attribute values for the product further comprises:

generating one or more configurations for the product; and

assigning one or more scraped attribute values of one of the records in the cluster 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 product attribute values comprises generating one or more sub-categories below the product in the taxonomy, and wherein the one or more sub-categories correspond to the configurations for the product.

20. The at least one non-transitory computer-readable medium of claim 13, further storing computer-readable instructions that, when executed by at least one of the one or more computing devices, cause at least one of the one or more computing devices to:

assign a product identifier to the cluster.