Dell products l.p. (20240127110). SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED simplified abstract
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 20240127110 titled 'SELF-SUPERVISED LEARNING ON INFORMATION NOT PROVIDED
Simplified Explanation
The patent application describes a system that can train an artificial intelligence risk model using labeled training data of user accounts and products to predict support costs.
- The system utilizes reconstructive self-supervised learning on user account features to generate a complete set of features for each account.
- When given a complete set of features and a product as input, the trained model can output a predicted cost associated with supporting that product for the user.
Potential Applications
This technology could be applied in:
- Customer support cost prediction
- Risk assessment for financial products
Problems Solved
This technology addresses:
- Predicting support costs accurately
- Improving risk management for products
Benefits
The benefits of this technology include:
- Cost savings through accurate predictions
- Enhanced decision-making based on risk assessment
Potential Commercial Applications
A potential commercial application for this technology could be:
- Integration into customer support software for cost optimization
Possible Prior Art
One possible prior art for this technology could be:
- Predictive analytics models for customer support cost estimation
Unanswered Questions
How does the system handle new products that were not part of the training data?
The system may need to be retrained with new labeled data to incorporate new products into its predictions.
What is the computational complexity of the reconstructive self-supervised learning process?
The computational complexity may vary depending on the size and complexity of the user account features being processed.
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.
