Contact Center Intent Recognition via Contextual Analysis
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Solution Overview
Problem
Conventional interactive systems, such as IVR and speech recognition systems, fail to accurately identify user intent due to lack of consideration for the entire context of user speech, leading to inadequate customer service and experience.
Innovation Solution
A contact center apparatus equipped with an AI-driven intent recognition and prediction engine that processes user interaction data, including speech utterances, grammar, past behavior, demographic data, and social media history, to dynamically forecast and tailor services, directing users to appropriate agents and performing proactive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition systems are used to identify user speech characteristics, then basic speech recognition is achieved, but user intent cannot be accurately identified due to lack of contextual consideration
Solution Approach 1:
The system segments the intent identification process into multiple independent components: speech recognition module, contextual analysis module, intent extraction module, and prediction module. Each component processes specific aspects of user interaction data separately before integrating results, allowing accurate intent identification without requiring a monolithic complex system
Solution Approach 2:
The patent transitions from two-dimensional speech recognition (transcribing words) to multi-dimensional analysis by incorporating temporal context, historical interaction patterns, semantic relationships, and predictive modeling. This dimensional expansion enables accurate intent identification by analyzing speech characteristics across multiple dimensions simultaneously
2Productivity
If conventional IVR systems are used to handle customer interactions, then routine data gathering is automated, but customer service quality and experience remain inadequate
Solution Approach 1:
The system performs preliminary actions by analyzing historical interaction data, customer profiles, and contextual information before the actual customer service interaction occurs. The prediction module forecasts customer needs and the intent extraction module pre-processes interaction patterns, enabling the system to anticipate and prepare appropriate responses that maintain high service quality while preserving automation benefits
Solution Approach 2:
The system incorporates feedback mechanisms where interaction outcomes are continuously analyzed and fed back into the predictive models and intent extraction algorithms. This feedback loop enables the system to learn from actual customer interactions and improve its service quality over time, resolving the contradiction between automation and service reliability
3Measurement precision
If limited user interaction data is processed, then system response time is fast, but intent recognition accuracy is insufficient
Solution Approach 1:
The system applies partial action by selectively processing only the most relevant portions of interaction data for each specific intent recognition task. The contextual analysis module identifies and processes only the necessary historical interactions and speech characteristics related to the current query, rather than processing all available data, thus achieving high accuracy with reduced processing time
Solution Approach 2:
The system performs preliminary processing of large datasets to create optimized index structures and pre-computed feature representations. By preparing and organizing interaction data in advance, the system can quickly retrieve and process only the necessary information during actual intent recognition, achieving both high accuracy and fast response times
Data Source
AI summary
Embodiments of the innovation relate to, in a contact center apparatus, a method for recognizing user intent associated with user interaction with the contact center apparatus. The method includes receiving user interaction data; performing a feature extraction process on the user interaction data to generate feature data; performing an intent extraction operation on the feature data to extract topics included with the feature data; executing a classification engine on the topics extracted from the feature data to classify a user intent associated with the user interaction data; and directing the user to a corresponding working agent based upon the classified user intent