Voice Response System Contextual Reference Analysis
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Solution Overview
Problem
Current voice response systems, such as chatbots, are unable to understand references to previously executed tasks or responses within user communications, requiring users to specify exact actions or details, which limits their conversational efficiency.
Innovation Solution
Implementing a method for voice response systems to identify and analyze references to previous responses within received communications, using a cognitive analysis engine that searches historical data and generates responses based on similar previous actions, and utilizing data sources like calendars and social media activity to improve understanding and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice response systems analyze references to previous responses using cognitive analysis engine and historical data, then response accuracy and contextual understanding are improved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by storing and indexing previous responses and their contextual data in advance. When a new communication is received, the system can quickly retrieve relevant historical responses through pre-established indexes and data structures, rather than analyzing all historical data from scratch. This reduces processing complexity while maintaining high response accuracy.
Solution Approach 2:
The patent introduces a cognitive analysis engine as an intermediary component that specifically handles the complex task of analyzing references to previous responses. This intermediary module processes contextual information and historical data, then provides refined results to the main voice response system. By isolating the complex analysis function in a dedicated intermediary component, the overall system complexity is managed more effectively.
2Device complexity
If voice response systems use simple response generation without analyzing previous responses, then system complexity is reduced, but conversational efficiency and user experience deteriorate
Solution Approach 1:
The system applies partial action by selectively analyzing only those communications that contain references to previous responses, rather than performing full contextual analysis on every single communication. The cognitive analysis engine identifies and processes only the relevant portions of communications that require historical context, thereby maintaining conversational efficiency while improving response quality where needed.
3Manufacturing precision
If voice response systems require users to specify exact actions and details in each communication, then response precision is improved, but ease of operation and user convenience worsen
Solution Approach 1:
The system implements feedback by analyzing user communications for references to previous responses and using that historical context to automatically determine appropriate actions. When users mention previous tasks or responses, the system retrieves the relevant historical data and uses it to infer the intended action, providing accurate responses without requiring users to specify every detail explicitly. This feedback loop between historical responses and current communications maintains precision while improving convenience.
Data Source
AI summary
Embodiments for managing a voice response system by one or more processors are described. At least one communication from an individual is received. A reference to a previous response executed by a voice response system is identified within the at least one received communication. A response to the at least one received communication is determined based on the previous response executed by the voice response system.