18263597. Selective Content Masking for Collaborative Computing simplified abstract (Google LLC)
Contents
- 1 Selective Content Masking for Collaborative Computing
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Selective Content Masking for Collaborative Computing - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Selective Content Masking for Collaborative Computing
Organization Name
Inventor(s)
Dhandapani Shanmugam of Bangalore (IN)
Sreenivas Makam of Bangalore (IN)
Selective Content Masking for Collaborative Computing - A simplified explanation of the abstract
This abstract first appeared for US patent application 18263597 titled 'Selective Content Masking for Collaborative Computing
Simplified Explanation
The abstract describes a machine-learned sharing system that masks sensitive information in shared content. Here is a simplified explanation of the patent application:
- The system receives content to display to users.
- It converts the content into image data.
- The image data is input into a machine-learned model that masks sensitive content.
- The system receives a mask indicating regions with sensitive content.
- It then displays the content with the sensitive information masked.
Potential Applications
This technology could be applied in social media platforms, messaging apps, and content sharing websites to protect user privacy and prevent the inadvertent sharing of sensitive information.
Problems Solved
This technology addresses the issue of accidentally sharing sensitive information in digital content, which can lead to privacy breaches and data leaks.
Benefits
The system provides users with a way to share content while protecting their privacy and sensitive information. It helps prevent unauthorized access to personal data and maintains user confidentiality.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of secure messaging apps that prioritize user privacy and data protection.
Possible Prior Art
One possible prior art for this technology could be image redaction tools used in government and legal settings to hide sensitive information in documents and images.
Unanswered Questions
How does the machine-learned model determine which regions of the content stream contain sensitive information?
The abstract does not provide details on the specific methods or algorithms used by the machine-learned model to identify sensitive content within the shared content.
What measures are in place to ensure the accuracy and effectiveness of the masking process?
It is unclear from the abstract how the system verifies the accuracy of the masking process and ensures that sensitive information is properly concealed in the shared content.
Original Abstract Submitted
A machine-learned sharing system and methods are provided for sharing content with users while masking sensitive information. The system receives a content stream for display to one or more users, converts the content stream into image data representative of at least a portion of the content stream, inputs the image data into a machine-learned model configured for masking sensitive content within shared content, receives from the machine-learned model a first mask indicative of a region within the first content stream that contains sensitive content, and renders a display of the content stream that masks the sensitive content based at least in part on the first mask indicative of the region of the first content stream having the sensitive content.
