Voice Intent Prediction for Real-Time Agent Knowledge Retrieval
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Solution Overview
Problem
Existing customer service systems struggle to accurately determine customer intents during interactions, leading to repeat contacts and increased costs due to incomplete resolution of issues, particularly in voice-only environments.
Innovation Solution
A system utilizing a speech-to-text engine to transcribe customer interactions, tokenizing and vectorizing utterances, and employing machine learning models to predict intents, which are then used to retrieve relevant knowledge base articles and display them to agents in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-only interaction is used for phone support, then ease of operation is improved, but measurement precision of customer intent deteriorates
Solution Approach 1:
The patent introduces an automated intent classification system as an intermediary between the voice-only customer interaction and the support agent. This system transcribes spoken words into text, tokenizes them, and uses machine learning models to predict customer intent, thereby enabling precise intent measurement while maintaining voice-only ease of operation.
Solution Approach 2:
The patent replaces the mechanical human agent interpretation process with an automated computational system. Instead of relying on human agents to manually interpret voice cues and determine intent, the system uses speech-to-text conversion, natural language processing, and machine learning classification to automatically detect and categorize customer intent from voice interactions.
2Measurement precision
If automated intent prediction is implemented, then measurement precision of customer intent is improved, but device complexity increases
Solution Approach 1:
The patent segments the intent prediction system into distinct functional modules: speech-to-text conversion, text tokenization, vectorization, machine learning classification, and result integration. Each module performs a specific task in the intent detection pipeline, making the overall complex system more manageable and maintainable through functional decomposition.
Solution Approach 2:
The patent creates a multi-functional automated assistant system that can handle various customer service tasks including intent classification, information retrieval from knowledge bases, and real-time agent support. This universal system replaces multiple separate tools and processes, reducing operational complexity despite the advanced technology involved.
3Productivity
If real-time intent prediction is provided to agents, then productivity is improved, but loss of time for processing increases
Solution Approach 1:
The patent implements preliminary action by pre-processing customer input into standardized formats (transcription, tokenization, vectorization) before the actual intent classification occurs. The system also pre-loads relevant knowledge base articles and prepares response templates, so that when intent is predicted, the agent receives ready-to-use information immediately, minimizing processing time loss.
Solution Approach 2:
The patent ensures continuity of useful action by making the intent prediction system operate in real-time throughout the customer interaction. Rather than batch processing after calls end, the system continuously analyzes spoken input, updates intent predictions dynamically, and provides ongoing support to agents, eliminating idle time and maintaining continuous productive engagement.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: tokenizing a text string into utterance tokens; vectorizing the utterance tokens; providing the utterance tokens to a machine learning model as input to the machine learning model; receiving, as output from the machine learning model, a predicted intent; formatting a query of a content repository, wherein the query includes the predicted intent; receiving, based on the query, an artifact from the content repository; and displaying the artifact via an interface.