System and method for smart categorization of content in a content management system

Patent metadata

Field	Value
Publication number	US-2022012268-A1
Application number	US-202117486524-A
Country	US
Kind code	A1
Filing date	Sep 27, 2021
Priority date	Oct 18, 2018
Publication date	Jan 13, 2022
Grant date	—

Abstract

In accordance with an embodiment, systems and methods described herein can be used, for example with a content management system, to provide recommendations to categorize/classify content into user-defined categories, which in turn provides an opportunity for content managers to place new content into accurate categories effortlessly, based on previously evaluated/categorized content. A recommendation system or tool can use artificial intelligence (AI) techniques to continuously learn from past data, and assist in placing content into a relevant category through automatic categorization/classification of newly created/edited content. The recommendation tool can be implemented and applied across diverse domains by generating feature vectors from contents, creating clusters in the feature space based on previously categorized content, and recommending a category for new content through feature space distance calculation from the clusters.

First claim

What is claimed is:

1 . A system for smart categorization of content in a content management system, comprising:
one or more computers including a processor, that provide access to a content management system; a content categorization engine provided at the one or more computers, the content categorization engine having access to a taxonomy; a recommendation system including the content categorization engine, the recommendation system generating feature vectors from contents at the content management system, the recommendation system having access to a database of the content categorization engine; wherein the generation of the feature vectors is based at least on an evaluation of previously categorized content within the taxonomy; wherein the generated feature vectors are utilized in categorizing new content into the taxonomy.

2 . The system of claim 1 , wherein the recommendation system creates clusters in feature space based on previously categorized content.

3 . The system of claim 2 , wherein the recommendation system generates one or more recommendations for the new content into the taxonomy through feature space distance calculation from the clusters.

4 . The system of claim 1 ,
wherein the recommendation system is used to create a new taxonomy or modify the taxonomy.

5 . The system of claim 1 ,
wherein the database of the content categorization engine comprises a historical record of user acceptances of prior categorization recommendations; and wherein the database of the content categorization engine comprises a historical record of user rejections of prior categorization recommendations.

6 . The system of claim 5 ,
wherein the recommendation system generates one or more recommendations for the new content into the taxonomy based upon the historical record of user acceptances of prior categorization records and the historical record of user rejections of prior categorization recommendations.

7 . The system of claim 1 ,
wherein the recommendation system generates a recommendation for a creation of a new category within the taxonomy for a plurality of uncategorized content items.

8 . A method for smart categorization of content in a content management system, comprising:
providing one or more computers, including a processor, that provide access to a content management system; providing a content categorization engine at the one or more computers, the content categorization engine having access to a taxonomy; generating, by a recommendation system including the content categorization engine, feature vectors from contents at the content management system, the recommendation system having access to a database of the content categorization engine, wherein the generation of the feature vectors is based at least on an evaluation of previously categorized content within the taxonomy; utilizing the generated feature vectors in categorizing new content into the taxonomy.

9 . The method of claim 8 , further comprising:
creating, by the recommendation system, clusters in feature space based on the previously categorized content within the taxonomy.

10 . The method of claim 9 , further comprising:
Generating, by the recommendation system, one or more recommendations for the new content into the taxonomy through feature space distance calculation from the clusters.

11 . The method of claim 8 ,
wherein the recommendation system is used to create a new taxonomy or modify the taxonomy.

12 . The method of claim 8 ,
wherein the database of the content categorization engine comprises a historical record of user acceptances of prior categorization recommendations; and wherein the database of the content categorization engine comprises a historical record of user rejections of prior categorization recommendations.

13 . The method of claim 12 , further comprising:
generating, by the recommendation system, one or more recommendations for the new content into the taxonomy based upon the historical record of user acceptances of prior categorization records and the historical record of user rejections of prior categorization recommendations.

14 . The method of claim 8 , further comprising
generating, by the recommendation system, a recommendation for a creation of a new category within the taxonomy for a plurality of uncategorized content items.

15 . A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer cause the computer to perform steps comprising:
providing one or more computers, including a processor, that provide access to a content management system; providing a content categorization engine at the one or more computers, the content categorization engine having access to a taxonomy; generating, by a recommendation system including the content categorization engine, feature vectors from contents at the content management system, the recommendation system having access to a database of the content categorization engine, wherein the generation of the feature vectors is based at least on an evaluation of previously categorized content within the taxonomy; utilizing the generated feature vectors in categorizing new content into the taxonomy.

16 . The non-transitory computer readable storage medium of claim 15 , the steps further comprising:
creating, by the recommendation system, clusters in feature space based on the previously categorized content within the taxonomy.

17 . The non-transitory computer readable storage medium of claim 16 , the steps further comprising:
Generating, by the recommendation system, one or more recommendations for the new content into the taxonomy through feature space distance calculation from the clusters.

18 . The non-transitory computer readable storage medium of claim 15 ,
wherein the recommendation system is used to create a new taxonomy or modify the taxonomy.

19 . The non-transitory computer readable storage medium of claim 15 ,
wherein the database of the content categorization engine comprises a historical record of user acceptances of prior categorization recommendations; and wherein the database of the content categorization engine comprises a historical record of user rejections of prior categorization recommendations.

20 . The non-transitory computer readable storage medium of claim 19 , the steps further comprising:
generating, by the recommendation system, one or more recommendations for the new content into the taxonomy based upon the historical record of user acceptances of prior categorization records and the historical record of user rejections of prior categorization recommendations.

Assignees

• Oracle Int Corp

Inventors

• Ghoshal Sandip
• Kamireddy Sreeharsha
• Maryala Jaswanth
• Peter Vivek
• Kadlabalu Hareesh S

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N20/00

  Machine learning · CPC title