18047119. SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED simplified abstract (Dell Products L.P.)
Contents
- 1 SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED
Organization Name
Inventor(s)
Ofir Ezrielev of Be'er Sheva (IL)
Amihai Savir of Newton MA (US)
Noga Gershon of Be'er Sheva (IL)
SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED - A simplified explanation of the abstract
This abstract first appeared for US patent application 18047119 titled 'SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED
Simplified Explanation
The abstract describes a system that can train an artificial intelligence risk model using labeled training data of user accounts and products, along with corresponding support costs. The system utilizes reconstructive self-supervised learning to generate complete features for user accounts and can predict support costs for products based on input data.
- The system trains an artificial intelligence risk model using labeled training data of user accounts and products.
- Reconstructive self-supervised learning is used to generate complete features for user accounts.
- The system can predict support costs for products based on input data.
Potential Applications
This technology could be applied in various industries such as finance, insurance, and customer support to predict support costs for different products and optimize resource allocation.
Problems Solved
This technology helps in predicting support costs accurately, identifying potential risks associated with user accounts, and improving decision-making processes related to resource allocation.
Benefits
The system can help companies save costs by accurately predicting support costs, improve customer satisfaction by efficiently allocating resources, and enhance risk management strategies.
Potential Commercial Applications
Predictive support cost analysis for insurance companies Risk assessment for financial institutions Resource allocation optimization for customer support centers
Possible Prior Art
One possible prior art could be the use of machine learning models in financial risk assessment and resource allocation in customer support centers.
Unanswered Questions
How does the system handle data privacy and security concerns?
The article does not provide information on how the system ensures the privacy and security of user data during the training and prediction processes.
What are the limitations of the system in terms of scalability and real-time processing?
The article does not address the scalability of the system when dealing with a large volume of user accounts and products, as well as its ability to process data in real-time.
Original Abstract Submitted
A system can train an artificial intelligence risk model to produce a trained model, wherein labeled training data for the training comprises respective features of user accounts and products, and corresponding labels of respective support costs applicable to supporting the products. The system can perform reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account. The system can, in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, produce an output that indicates a predicted cost that corresponds to the input.
