18047141. IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS simplified abstract (Dell Products L.P.)
Contents
- 1 IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS - 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
IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS
Organization Name
Inventor(s)
Ofir Ezrielev of Be'er Sheva (IL)
Amihai Savir of Newton MA (US)
Noga Gershon of Be'er Sheva (IL)
IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18047141 titled 'IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS
Simplified Explanation
The abstract describes a system that uses an explainable artificial intelligence risk model to predict maintenance costs of different computing configurations over a time period. The system can compare predicted maintenance costs of two different computing configurations and save the difference if the second configuration is predicted to have lower maintenance costs.
- The system determines predicted maintenance costs of different computing configurations based on inputs.
- It compares the predicted maintenance costs of two different computing configurations.
- If the second configuration is predicted to have lower maintenance costs, the system saves the difference between the two costs.
Potential Applications
This technology could be applied in various industries where maintenance costs of different configurations need to be predicted and compared, such as:
- IT infrastructure management
- Manufacturing equipment maintenance
- Fleet management
Problems Solved
This technology helps in:
- Predicting maintenance costs accurately
- Comparing maintenance costs of different configurations
- Saving costs by choosing configurations with lower maintenance costs
Benefits
The benefits of this technology include:
- Cost savings through optimized configuration choices
- Improved decision-making based on predicted maintenance costs
- Efficient resource allocation for maintenance activities
Potential Commercial Applications
The technology could be commercially applied in:
- Maintenance service providers
- Software companies offering configuration optimization tools
- Consulting firms specializing in cost-saving strategies
Possible Prior Art
One possible prior art could be traditional predictive maintenance models that use historical data to estimate maintenance costs. Another could be cost comparison tools for different products or services.
What are the limitations of the explainable artificial intelligence risk model used in this system?
The limitations of the explainable artificial intelligence risk model used in this system could include:
- Dependency on the quality and quantity of input data
- Interpretability of the model's predictions
- Generalization to new computing configurations
How does the system handle unexpected maintenance costs that were not predicted by the artificial intelligence model?
The system may not be equipped to handle unexpected maintenance costs that were not predicted by the artificial intelligence model. In such cases, manual intervention or adjustments to the model may be required to account for these unforeseen costs.
Original Abstract Submitted
A system can determine a first output from an explainable artificial intelligence risk model based on a first input, wherein the first input indicates a first computing configuration, and wherein the first output indicates a first predicted maintenance cost of the first computing configuration during a time period. The system can determine a second output from the explainable artificial intelligence risk model based on a second input, wherein the second input indicates a second computing configuration that differs from the first computing configuration, and wherein the second output indicates a second predicted maintenance cost of the second computing configuration during the time period. The system can, in response to determining that the second predicted maintenance cost is less than the first predicted maintenance cost, saving an indication of a difference between the second predicted maintenance cost and the first predicted maintenance cost.
