Service Location Recommendation Platform for Technical Support

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Solution Overview

Problem

As electronic products become increasingly sophisticated, it is challenging for enterprises to determine and execute effective support and repair options, due to varying product configurations and symptoms, which complicates efficient response to hardware and software issues and maximizes compute and personnel resources.

Innovation Solution

A service location recommendation platform that utilizes machine learning algorithms to analyze work order data and predict whether technical support issues will be resolved at specific service locations, thereby recommending the most appropriate service location for issue resolution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If enterprises manually determine support and repair options for sophisticated electronic products, then they can provide personalized support, but the complexity of determining effective support options increases due to varying product configurations and symptoms

Engineering Contradiction:

Improveease of determining support optionsVSAvoidcomplexity of support operations

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces a service location recommendation platform as an intermediary system that includes a machine learning model. This platform receives work order data containing product configurations and symptoms, analyzes the data through the machine learning model, and generates service location recommendations. The intermediary platform handles the complexity of matching varying product configurations with appropriate service locations, thereby simplifying the operational process for enterprises dealing with sophisticated electronic products.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical decision-making processes with an automated machine learning-based system. Instead of relying on human analysts to manually evaluate work order data and determine service locations, the system uses machine learning algorithms to automatically analyze product configurations, symptoms, and historical data, generating recommendations without human intervention. This substitution reduces operational complexity while maintaining or improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If enterprises use traditional methods to respond to hardware and software issues, then they can handle support requests, but the efficiency of responding to issues and maximizing compute and personnel resources decreases

Engineering Contradiction:

Improveefficiency of responding to issuesVSAvoidtime to resolve issues

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-training the machine learning model with historical work order data, product configuration data, and service location data before actual support operations begin. The model learns from past patterns and relationships, enabling it to quickly generate accurate service location recommendations for new issues. This preliminary preparation allows the system to efficiently respond to new support requests without requiring time-consuming manual analysis, thereby improving productivity and reducing resolution time.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If enterprises dispatch service technicians to multiple locations for issue resolution, then they can attempt different support options, but the number of visits and resource utilization increases

Engineering Contradiction:

Improveissue resolution success rateVSAvoidnumber of visits required

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent incorporates feedback mechanisms by training the machine learning model on historical work order data that includes information about which service locations successfully resolved issues and which required multiple visits or off-site repairs. The model learns from this feedback to identify patterns and characteristics that correlate with successful first-visit resolutions. By applying this learned knowledge to new work orders, the system recommends service locations with higher probability of resolving issues in a single visit, thereby improving reliability while reducing the number of visits required.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12327256B2Technical support service location recommendation using machine learning

Publication Date: 2025.06.10 DELL PROD LP

AI summary

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.