Dell products l.p. (20240095750). TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING simplified abstract
Contents
- 1 TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING - 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
TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING
Organization Name
Inventor(s)
Bijan Kumar Mohanty of Austin TX (US)
Kulin Shaival Chokshi of Round Rock TX (US)
Shijin Babu of Round Rock TX (US)
David J. Linsey of Marietta GA (US)
TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095750 titled 'TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING
Simplified Explanation
The method described in the abstract involves analyzing work order data using machine learning algorithms to predict the resolution of technical support issues at different service locations. Based on this prediction, a recommendation is generated for responding to the issue at a specific service location.
- The method involves receiving work order data identifying technical support issues.
- The work order data is analyzed using machine learning algorithms.
- Predictions are made on whether the technical support issues will be resolved at different service locations.
- Recommendations are generated based on the predictions for responding to the issues at specific service locations.
Potential Applications
This technology could be applied in various industries where technical support is required, such as IT support services, customer service centers, and maintenance operations.
Problems Solved
This technology helps in predicting the resolution of technical support issues, allowing for more efficient allocation of resources and timely response to customer needs.
Benefits
The benefits of this technology include improved customer satisfaction, optimized resource allocation, and faster resolution of technical support issues.
Potential Commercial Applications
A potential commercial application of this technology could be in the field of customer service management software, where it can help companies streamline their support operations and improve service quality.
Possible Prior Art
One possible prior art for this technology could be predictive maintenance systems used in industrial settings, where machine learning algorithms are employed to predict equipment failures and schedule maintenance proactively.
=== What are the limitations of this technology in predicting technical support issue resolution? The abstract does not mention the accuracy rate of the predictions made by the machine learning algorithms in resolving technical support issues.
=== How does this technology handle complex technical support issues that may require human intervention? The abstract does not specify how the recommendation generated by the technology incorporates the need for human intervention in resolving complex technical support issues.
Original Abstract Submitted
a method comprises receiving work order data, wherein the work order data identifies at least one technical support issue requiring resolution. the work order data is analyzed using one or more machine learning algorithms. the method further comprises predicting, based at least in part on the analyzing, whether the at least one technical support issue will be resolved at one or more respective service locations of a plurality of service locations. based at least in part on the predicting, a recommendation to respond to the at least one technical support issue at a given service location of the plurality of service locations is generated.
