17946223. TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING simplified abstract (Dell Products L.P.)
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 17946223 titled 'TECHNICAL SUPPORT SERVICE LOCATION RECOMMENDATION USING MACHINE LEARNING
Simplified Explanation
The method described in the abstract involves using machine learning algorithms to analyze work order data and predict the resolution of technical support issues at different service locations. Based on this prediction, a recommendation is generated for responding to the technical support issue at a specific service location.
- Machine learning algorithms are used to analyze work order data.
- Predictions are made on the resolution of technical support issues at different service locations.
- Recommendations are generated based on the predictions for responding to technical support 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 optimizing the allocation of resources for resolving technical support issues by predicting the likelihood of resolution at different service locations.
Benefits
The use of machine learning algorithms can improve the efficiency and effectiveness of technical support operations by providing data-driven recommendations for responding to issues.
Potential Commercial Applications
This technology could be valuable for companies providing technical support services, as it can help in streamlining operations and improving customer satisfaction.
Possible Prior Art
One possible prior art could be the use of predictive analytics in customer service operations to optimize resource allocation and improve service delivery.
What are the potential limitations of this technology in real-world applications?
- One potential limitation could be the accuracy of the predictions made by the machine learning algorithms, which may vary based on the quality of the input data.
- Another limitation could be the scalability of the technology to handle a large volume of work order data and service locations efficiently.
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.
