18543897. SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION - 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
SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Sayed Hassan Abdelaziz of Redmond WA (US)
Maria Puertas Calvo of Seattle WA (US)
Laurentiu Bogdan Cristofor of Redmond WA (US)
Rajat Luthra of Redmond WA (US)
SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18543897 titled 'SUPERVISED LEARNING SYSTEM FOR IDENTITY COMPROMISE RISK COMPUTATION
Simplified Explanation
The patent application abstract describes systems for enhancing computer security based on user risk scores through the application of machine learning techniques. By dynamically generating and modifying user risk scores, these systems aim to improve accuracy and usability.
- Machine learning applied to user risk profile components
- Multiple tiers of machine learning used
- Dynamic generation and modification of user risk scores
Potential Applications
The technology could be applied in various industries such as finance, healthcare, and e-commerce to enhance security measures for user accounts and sensitive information.
Problems Solved
1. Inaccurate user risk scores 2. Lack of usability in existing security systems
Benefits
1. Improved accuracy of user risk scores 2. Enhanced security measures 3. User-friendly interface for managing security settings
Potential Commercial Applications
Enhanced security software for businesses Secure authentication systems for online platforms Data protection services for financial institutions
Possible Prior Art
One potential prior art could be the use of machine learning in cybersecurity to improve threat detection and response times. Another could be the application of dynamic risk scoring in financial fraud detection systems.
What are the limitations of the technology described in the patent application?
The patent application does not address the potential challenges of implementing the proposed systems in real-world environments, such as compatibility issues with existing security infrastructure and the scalability of the technology.
How does this technology compare to existing solutions in the market?
The patent application does not provide a comparison with existing solutions in the market, making it difficult to assess the unique advantages of the proposed systems over current security technologies.
Original Abstract Submitted
Systems are provided for improving computer security systems that are based on user risk scores. These systems can be used to improve both the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to different the user risk profile components used to generate the user risk scores and in such a manner as to dynamically generate and modify the corresponding user risk scores.
