Granted Patent B2

US 12,393,713 · App. 18/354,730 · Granted Aug 19, 2025

Inventors: Yu-Siang Chen (Minxiong Township, TW); Ching-Chun Liu (Taipei, TW); Joey H. Y. Tseng (Taipei, TW); Amanda P L Yang (Taipei, TW)

Assignee: International Business Machines Corporation

G06F21/6218G06N20/00G10L15/063G10L15/18G10L2015/088

Abstract

Claims (62)

1. A method performed by a processing device for processing digital content, the method comprising:

building a sensitive sentence classification model;

receiving a digital content, wherein the digital content is intended for one or more groups of content consumers identified by a content producer within a user interface;

assigning a plurality of consumers within an organization to the one or more groups of content consumers based on a hierarchal structure of the organization constructed based on an analysis of organizational information;

processing the digital content using the sensitive sentence classification model;

generating a consumer specific digital content for each of the one or more groups of content consumers; and

displaying the consumer specific digital content to a plurality of consumers, wherein each of the plurality of consumers may access the consumer specific digital content corresponding to their content consumer group.

2. The method of claim 1 , wherein building the sensitive sentence classification model further comprises:

training the sensitive sentence classification model using a customized training dataset and an existing dataset, wherein the customized training dataset and the existing dataset are preprocessed using a Word2Vec algorithm, and wherein the customized training dataset is comprised of keywords and entities derived from organizational information, and wherein the existing dataset is comprised of one or more existing sensitive word datasets sourced from one or more publicly available resources.

3. The method of claim 2 , wherein the customized training dataset is further comprised of a plurality of manually identified spoken language examples.

4. The method of claim 1 , wherein processing the digital content using the sensitive sentence classification model further comprises:

storing the digital content received in its original form in an original content database;

identifying one or more sensitive data segments using the sensitive sentence classification model; and

storing the one or more sensitive data segments in a processed content database according to data type, timestamp, and corresponding consumer roles, wherein the corresponding consumer roles include at least one of the one or more groups of content consumers.

5. The method of claim 4 , wherein identifying the one or more sensitive data segments further comprises:

receiving one or more additional sensitive data segments manually identified by the content producer;

storing the one or more additional sensitive data segments in the processed content database; and

retraining the sensitive sentence classification model based on the one or more additional sensitive data segments manually identified by the content producer.

6. The method of claim 1 , wherein an Image Recognition Neural Network deep learning model is utilized in identifying visual content within the digital content which may be filtered using the sensitive sentence classification model.

7. The method of claim 6 , further comprising:

blocking at least a portion of the visual content of the digital content for at least one of the one or more groups of content consumers.

8. The method of claim 1 , further comprising:

defining consumer role settings for each of the one or more groups of content consumers based on the hierarchal structure, wherein the consumer role settings designates information which is considered sensitive for each of the one or more groups based on roles within the organization.

9. The method of claim 1 , wherein the digital content is comprised of at least audio content and visual content.

10. The method of claim 9 , further comprising:

designating, by the content producer within the user interface, a masking period for at least one of the or more groups of content consumers.

11. The method of claim 10 , further comprising:

masking, by an output content processor, at least a portion of the audio content or video content for the masking period of the at least one of the one or more groups of content consumers.

12. The method of claim 1 , wherein the processing of the digital content further comprises:

delaying the processing of the digital content received in real time for an intervening time period, wherein the digital content is processed using the sensitive sentence classification model during the intervening time period.

13. A computer system for processing digital content, comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:

building a sensitive sentence classification model;

receiving a digital content, wherein the digital content is intended for one or more groups of content consumers identified by a content producer within a user interface;

assigning a plurality of consumers within an organization to the one or more groups of content consumers based on a hierarchal structure of the organization constructed based on an analysis of organizational information;

processing the digital content using the sensitive sentence classification model;

generating a consumer specific digital content for each of the one or more groups of content consumers; and

displaying the consumer specific digital content to a plurality of consumers, wherein each of the plurality of consumers may access the consumer specific digital content corresponding to their content consumer group.

14. The computer system of claim 13 , wherein building the sensitive sentence classification model further comprises:

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to train the sensitive sentence classification model using a customized training dataset and an existing dataset, wherein the customized training dataset and the existing dataset are preprocessed using a Word2Vec algorithm, and wherein the customized training dataset is comprised of keywords and entities derived from organizational information, and wherein the existing dataset is comprised of one or more existing sensitive word datasets sourced from one or more publicly available resources.

15. The computer system of claim 13 , wherein processing the digital content using the sensitive sentence classification model further comprises:

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to store the digital content received in its original form in an original content database;

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to identify one or more sensitive data segments using the sensitive sentence classification model; and

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to store the one or more sensitive data segments in a processed content database according to data type, timestamp, and corresponding consumer roles, wherein the corresponding consumer roles include at least one of the one or more groups of content consumers.

16. The computer system of claim 15 , wherein identifying the one or more sensitive data segments further comprises:

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to receive one or more additional sensitive data segments manually identified by the content producer;

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to store the one or more additional sensitive data segments in the processed content database; and

program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to retrain the sensitive sentence classification model based on the one or more additional sensitive data segments manually identified by the content producer.

17. A computer program product for processing digital content, comprising:

one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:

building a sensitive sentence classification model;

receiving a digital content, wherein the digital content is intended for one or more groups of content consumers identified by a content producer within a user interface;

assigning a plurality of consumers within an organization to the one or more groups of content consumers based on a hierarchal structure of the organization constructed based on an analysis of organizational information;

processing the digital content using the sensitive sentence classification model;

generating a consumer specific digital content for each of the one or more groups of content consumers; and

displaying the consumer specific digital content to a plurality of consumers, wherein each of the plurality of consumers may access the consumer specific digital content corresponding to their content consumer group.

18. The computer program product of claim 17 , wherein building the sensitive sentence classification model further comprises:

program instructions, stored on at least one of the one or more computer-readable storage media, to train the sensitive sentence classification model using a customized training dataset and an existing dataset, wherein the customized training dataset and the existing dataset are preprocessed using a Word2Vec algorithm, and wherein the customized training dataset is comprised of keywords and entities derived from organizational information, and wherein the existing dataset is comprised of one or more existing sensitive word datasets sourced from one or more publicly available resources.

19. The computer program product of claim 17 , wherein processing the digital content using the sensitive sentence classification model further comprises:

program instructions, stored on at least one of the one or more computer-readable storage media, to store the digital content received in its original form in an original content database;

program instructions, stored on at least one of the one or more computer-readable storage media, to identify one or more sensitive data segments using the sensitive sentence classification model; and

program instructions, stored on at least one of the one or more computer-readable storage media, to store the one or more sensitive data segments in a processed content database according to data type, timestamp, and corresponding consumer roles, wherein the corresponding consumer roles include at least one of the one or more groups of content consumers.

Assignments (1)

Continuity (1)

References Cited (42)