18235213. GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS simplified abstract (NVIDIA Corporation)
Contents
- 1 GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS - 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 How does the technology handle large volumes of data for multiple accounts efficiently?
- 1.11 What measures are in place to ensure the privacy and security of user data during the training and inference processes?
- 1.12 Original Abstract Submitted
GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS
Organization Name
Inventor(s)
Rachel Allen of Arlington VA (US)
Gorkem Batmaz of Cambridge (GB)
Michael Demoret of Denver CO (US)
Bartley Richardson of Alexandria VA (US)
GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18235213 titled 'GENERATING MODELS FOR DETECTION OF ANOMALOUS PATTERNS
Simplified Explanation
The abstract describes technologies for generating fine-grained, unsupervised behavior models for each user to monitor and detect anomalous patterns in multiple accounts. An unsupervised training pipeline is used to create user models associated with accounts, which are trained to detect anomalous patterns using feature data. An inference pipeline then detects anomalous patterns in data associated with different accounts using the corresponding user models.
- User models are trained for each account to detect anomalous patterns.
- An unsupervised training pipeline generates fine-grained behavior models for each user.
- An inference pipeline detects anomalous patterns in data associated with different accounts using the corresponding user models.
Potential Applications
The technology can be applied in various industries such as finance, cybersecurity, and healthcare for anomaly detection and monitoring user behavior.
Problems Solved
1. Detection of anomalous patterns in user behavior across multiple accounts. 2. Monitoring and identifying unusual activities in real-time.
Benefits
1. Improved security measures for accounts. 2. Early detection of potential threats or fraudulent activities. 3. Enhanced user experience through personalized monitoring.
Potential Commercial Applications
Enhancing cybersecurity systems with advanced anomaly detection technology.
Possible Prior Art
Prior art in anomaly detection systems using machine learning algorithms and user behavior analysis could be relevant to this technology.
Unanswered Questions
How does the technology handle large volumes of data for multiple accounts efficiently?
The abstract does not provide details on the scalability of the technology and how it manages big data processing for numerous accounts.
What measures are in place to ensure the privacy and security of user data during the training and inference processes?
The abstract does not mention specific protocols or mechanisms for safeguarding user data privacy and security throughout the model training and inference stages.
Original Abstract Submitted
Technologies for generating a set of models for each account, where each model is a fine-grained, unsupervised behavior model trained for each user to monitor and detect anomalous patterns are described. An unsupervised training pipeline can generate user models, each being associated with one of multiple accounts and is trained to detect an anomalous pattern using feature data associated with the one account. Each account is associated with at least one of a user, a machine, or a service. An inference pipeline can detect a first anomalous pattern in first data associated with a first account using a first user model. The inference pipeline can detect a second anomalous pattern in second data associated with a second account using a second user model.
