Semantic Phrase Derivation for Flexible Contact Center Matching
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Solution Overview
Problem
Existing contact center tools struggle with deciphering and organizing customer interaction data due to binary logic, requiring time-intensive and unreliable manual techniques for phrase derivation, and exact word-for-word matching, which lacks flexibility and accuracy.
Innovation Solution
A computer-implemented method and system for intelligent phrase derivation using a model that determines and arranges derivative phrases based on characteristics such as intent, employing machine learning and big data principles to generate contextually similar phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual techniques are used for phrase derivation, then users can generate alternative phrases, but the process becomes time-intensive and unreliable
Solution Approach 1:
The patent replaces manual mechanical processes (users hard-keying, stemming, altering, and rearranging words) with an automated computational system that uses machine learning models and natural language processing algorithms to generate derivative phrases automatically, eliminating the time-intensive manual effort while improving consistency and reliability
Solution Approach 2:
The system enables self-service phrase derivation by automatically generating derivative phrases from seed phrases without requiring user intervention for each phrase transformation. The automated system handles stemming, pluralization, tense changes, and synonym generation independently, freeing users from repetitive manual tasks
2Measurement precision
If exact word-for-word matching is used, then matching precision is high, but flexibility and accuracy in contextually similar phrases are reduced
Solution Approach 1:
The patent transforms the matching approach by changing parameters from exact string comparison to semantic similarity assessment. The system uses natural language processing to analyze meaning, context, and intent behind phrases, allowing matches based on conceptual equivalence rather than literal word identity, thereby achieving both precision and flexibility
Solution Approach 2:
The patent introduces an intermediary layer of semantic analysis between the seed phrase and the matching process. Instead of directly comparing words, the system uses NLP models to interpret and translate phrases into their underlying meaning representations, enabling accurate matching of contextually similar phrases even when wording differs
3Reliability
If users generate exhaustive lists of alternative phrases, then coverage of phrase variations is improved, but the complexity and effort of phrase generation increases
Solution Approach 1:
The patent creates a universal phrase generation system that handles multiple types of phrase transformations (stemming, pluralization, tense changes, synonym generation, grammatical variations) through a single integrated platform. This multi-functional approach covers comprehensive phrase variations without requiring users to manually create exhaustive lists for each transformation type
Solution Approach 2:
The system dynamically adapts phrase generation based on the input seed phrase and contextual requirements. Rather than using fixed, exhaustive templates, the automated system flexibly generates relevant derivative phrases on-demand, adjusting the type and number of variations based on the specific needs of each case, thereby reducing unnecessary complexity
Data Source
AI summary
Disclosed herein are computer-implemented methods for intelligent phrase generation. Example methods include acquiring a bulk data input that includes one or more seed phrases that are requested for derivation, inputting the bulk data input into a model, and returning an arrangement result to a user. The model is configured to determine one or more derivative phrases from each of the seed phrases in the bulk data input, each of the one or more derivative phrases corresponding to a respective seed phrase. The model is configured to determine one or more arrangements with which to arrange each of the derivative phrases in the one or more derivative phrases. The model is configured to determine a characteristic of the respective seed phrase, the one or more arrangement corresponding to the characteristic of the respective seed phrase.