View source for Dell products l.p. (20240126670). IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS simplified abstract

=IDENTIFYING TECHNOLOGY REFRESH THROUGH EXPLAINABLE RISK REDUCTIONS=


==Organization Name==

[[:Category:dell products l.p.|dell products l.p.]]


[[Category:dell products l.p.]]

==Inventor(s)==

[[:Category:Ofir Ezrielev of Be'er Sheva (IL)|Ofir Ezrielev of Be'er Sheva (IL)]][[Category:Ofir Ezrielev of Be'er Sheva (IL)]]

[[:Category:Amihai Savir of Newton MA (US)|Amihai Savir of Newton MA (US)]][[Category:Amihai Savir of Newton MA (US)]]

[[:Category:Noga Gershon of Be'er Sheva (IL)|Noga Gershon of Be'er Sheva (IL)]][[Category: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 20240126670 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 for different computing configurations over a time period. It can compare the predicted costs for two configurations and save the difference if the second configuration is cheaper.

* The system determines predicted maintenance costs for different computing configurations.
* It saves the difference in predicted maintenance costs if the second configuration is cheaper.
* The system uses an explainable artificial intelligence risk model for predictions.

== Potential Applications ==
This technology could be applied in various industries where maintenance costs for different configurations need to be predicted and compared, such as:
* IT infrastructure management
* Manufacturing equipment maintenance
* Fleet management for transportation companies

== Problems Solved ==
This technology helps in:
* Predicting maintenance costs accurately
* Comparing costs for different computing configurations
* Making informed decisions based on cost predictions

== Benefits ==
The benefits of this technology include:
* Cost savings by choosing the most cost-effective configuration
* Improved maintenance planning and budgeting
* Enhanced efficiency in resource allocation

== Potential Commercial Applications ==
The potential commercial applications of this technology include:
* Software companies offering predictive maintenance solutions
* Consulting firms providing cost optimization services
* Maintenance service providers using predictive analytics for client solutions

== Possible Prior Art ==
One possible prior art for this technology could be predictive maintenance software that uses machine learning algorithms to forecast maintenance costs for industrial equipment. Another could be cost comparison tools for different product configurations in the manufacturing industry.

=== What are the limitations of the system described in the abstract? ===

The abstract does not mention the scalability of the system for large-scale computing configurations or the accuracy of the predicted maintenance costs over an extended time period.

=== How does the system handle changes in maintenance requirements over time? ===

The abstract does not provide information on how the system adapts to changes in maintenance requirements or how it updates predictions based on real-time data.


==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.



[[Category:G06F11/34]]