Text Message Vector Classification for Customer Service Routing
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Solution Overview
Problem
Conventional methods for processing and interpreting short text messages in customer service contexts face challenges such as ambiguity, errors in spelling and punctuation, and inefficiencies in routing messages to the appropriate service representative, leading to suboptimal classification and response generation.
Innovation Solution
The system constructs word vectors for text messages and compares them to orthogonal class vectors using a machine learning model trained on a corpus of annotated messages, employing back-fitting processes to enhance vector representations and improve classification accuracy, handling non-linear data and spelling mistakes, and providing a transformation matrix for efficient message routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional message processing and scoring approaches are used, then the system can process text messages, but the scores cluster around values that do not provide sufficient information for accurate routing decisions
Solution Approach 1:
The patent transforms the one-dimensional score into a multi-dimensional vector representation that captures nuanced semantic relationships. By mapping messages to vectors in a high-dimensional space where each dimension represents a different aspect of meaning, the system preserves far more information than a single scalar score, enabling more accurate routing decisions while maintaining computational efficiency.
2Reliability
If conventional classification methods are used, then the system can categorize messages, but it struggles with ambiguous meanings and spelling errors in short text messages
Solution Approach 1:
The patent introduces vector representations as an intermediary layer between the raw text input and the classification output. This vector intermediary captures the semantic meaning of messages in a continuous space, allowing the system to handle ambiguity and spelling errors by finding the nearest meaningful representation rather than requiring exact word matches, thereby improving reliability without excessive complexity.
3Productivity
If aggressive classification weighting is applied during training, then one class receives high weighting, but new unseen documents are classified too aggressively into that class even when they belong to another class
Solution Approach 1:
The patent changes the parameter representation from discrete class labels with aggressive weighting to continuous vector representations with learned similarity metrics. This allows the system to maintain fast classification through efficient vector operations while avoiding the bias toward over-weighted classes by using geometric distance measures that naturally balance all class relationships in the vector space.
Data Source
AI summary
Systems, apparatuses, and methods for the interpretation and routing of short text messages, such as those that might be received as part of a “chat” between a customer and a customer service representative. In some embodiments, this is achieved by constructing word “vectors” based on the text in a message, with a token corresponding to each word. The word vectors are then compared to a set of mutually orthogonal unit vectors representing the “classes” or “categories” of messages that are received and are intended to be acted upon by a person or automated process. The orthogonal class unit vectors are generated by training a machine learning model using a set of previously classified text or messages.