Frustration Detection System Using Linguistic Rules
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Solution Overview
Problem
Current methods for detecting frustration in call center interactions are manual, time-consuming, subjective, and limited in scope, leading to inconsistent and inaccurate assessments, which can result in unaddressed consumer and agent dissatisfaction, potentially damaging companies' reputations and revenue.
Innovation Solution
A computer-based system that automatically detects frustration using linguistic rules and machine learning algorithms to identify natural language patterns related to frustration, with weights and metadata for override attributes, enabling consistent and precise analysis across interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of interactions is performed by human agents, then frustration detection can be conducted, but it is time-consuming and has low productivity
Solution Approach 1:
The patent replaces the manual mechanical process of human agents reviewing interactions with an automated computer-based system that uses natural language processing and machine learning algorithms to detect frustration. This substitution dramatically increases productivity while maintaining or improving detection accuracy through consistent application of linguistic rules and metadata analysis.
2Quantity of substance
If manual frustration detection is performed, then some interactions can be reviewed, but the scope is limited and coverage is insufficient
Solution Approach 1:
The system enables automated self-service frustration detection where the computer-based system independently analyzes interactions without requiring human agent intervention for each review. This allows the organization to analyze a much larger quantity of interactions simultaneously, providing comprehensive coverage while eliminating the time investment required for manual review of each interaction.
3Adaptability or versatility
If manual analysis is used to define dissatisfaction levels, then subjective judgment is applied, but consistency and reliability are poor
Solution Approach 1:
The patent transforms the subjective parameter of dissatisfaction definition into objective parameters through linguistic rules and metadata attributes. The system uses weighted linguistic patterns, override attributes, and structured metadata to consistently classify frustration levels across all interactions, eliminating variability between different human agents while maintaining the ability to adapt to different organizational definitions through configurable rules.
4Productivity
If automated linguistic rule-based detection is used, then productivity increases, but handling contextual nuances and overrides becomes complex
Solution Approach 1:
The patent segments the frustration detection system into distinct modular components: linguistic rules for pattern identification, weights for pattern importance, metadata attributes for contextual information, and override attributes for special cases. This segmentation allows the complex system to handle high volumes of interactions efficiently while managing complexity through organized, reusable rule modules that can be independently configured and maintained.
Data Source
AI summary
A computer based system and method for automatically detecting frustration in an interaction, may include: identifying in the interaction using a set of linguistic rules, natural language patterns related to frustration, wherein the linguistic rules further define weights associated with the natural language patterns and rule metadata; reviewing the rule metadata associated with the identified natural language patterns to identify override attributes, wherein if the rule metadata does not include override attributes, then a frustration level in the interaction is determined based on the identified natural language patterns and weights associated with the identified natural language patterns; and if the rule metadata includes override attributes than the frustration level is determined based on the identified override attributes.