Regression-Based Service Request Classification
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Solution Overview
Problem
In customer support systems, users face difficulties navigating complex menu options with confusing terminology, leading to incorrect queue placement and mismatched assistance, resulting in inefficient service request routing.
Innovation Solution
Implementing a regression-based classifier that uses a single descriptive input from users, combined with their request history and demographics, to classify and route service requests, with continuous training based on administrator verification to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user is presented with multiple menu options to choose from, then the system can route the user to the appropriate assistance, but the user may be confused by complex terminology and navigate incorrectly
Solution Approach 1:
The patent extracts the classification task from the user's responsibility and transfers it to the system. Instead of requiring users to navigate complex menus and understand terminology, the system presents a single open-ended description field where users simply describe their problem in natural language. The system then automatically classifies this description into the appropriate category, eliminating the need for users to interpret complex menu structures.
Solution Approach 2:
The system performs self-service classification by automatically analyzing the user's free-form description and routing it to the appropriate queue without human intervention. The classification algorithm independently processes the description, determines the correct category, and assigns the request accordingly, making the system self-sufficient in the classification task.
2Productivity
If the system uses traditional menu-based classification, then users can be directed to appropriate queues, but users may be placed in wrong queues due to incorrect navigation
Solution Approach 1:
The patent replaces the mechanical menu-navigation system with an intelligent text-analysis system. Instead of relying on users to mechanically select options from predefined menus, the system uses natural language processing and classification algorithms to automatically interpret the user's description and determine the appropriate queue, substituting automated intelligence for manual navigation.
Solution Approach 2:
The system changes the input parameter from structured menu selections to unstructured free-form text descriptions. This parameter change allows the system to leverage language processing capabilities and classification algorithms, transforming the routing mechanism from option-based to description-based classification.
3Adaptability or versatility
If the system presents comprehensive menu options covering all service types, then all user needs can be addressed, but users unfamiliar with terminology cannot find appropriate options
Solution Approach 1:
The patent inverts the traditional approach by not presenting users with service categories to choose from, but rather having users describe their problem and letting the system determine the category. This inversion places the cognitive burden on the system rather than the user, maintaining comprehensive service coverage while eliminating terminology barriers.
Data Source
AI summary
Approaches presented herein enable intelligent service request classification and assignment learning. More specifically, a request comprising a free form text or spoken description is received from a user. The request description is parsed and classified by a regression-based classifier. The regression-based classifier classifies based on, for example: the description itself; the requestor's history of requests, and/or supplemental demographics about a requestor. Optionally, a user may verify the classification or select from a plurality of returned classifications. A service provider or administrator confirms that a classification is correct. If not, the incorrectly classified request is queued. If so, the correctly classified request is added to a set of training data to be used in classifying future requests.