Contextual Prediction Selection for IVR Response Accuracy
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Solution Overview
Problem
Existing Interactive Voice Response (IVR) systems rely on classical prediction processing, which only considers the most recent user input, often leading to frustration when additional context is required for accurate responses.
Innovation Solution
A three-tiered selection method that dynamically chooses between contextual and classical prediction processing based on confidence levels and sentiment analysis to provide accurate responses, leveraging historical conversation data when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual prediction processing is used to leverage historical conversation data, then response accuracy is improved, but resource consumption increases
Solution Approach 1:
The system dynamically adjusts the prediction processing mode (classical vs. contextual) based on real-time assessment of user intent clarity and conversation state. The processor selectively activates contextual processing only when classical processing fails to achieve sufficient confidence, making the resource consumption adaptive rather than static.
Solution Approach 2:
The system changes the processing parameter (prediction depth) based on confidence thresholds. When the confidence score from classical processing exceeds a threshold, the system uses lightweight classical processing; otherwise, it switches to more resource-intensive contextual processing that incorporates historical conversation data and sentiment analysis.
2Productivity
If a three-tiered selection method is implemented to dynamically choose processing modes, then system efficiency is improved, but device complexity increases
Solution Approach 1:
The prediction processing system is segmented into three distinct tiers: classical processing tier, contextual processing tier, and sentiment analysis tier. Each tier operates independently with specific confidence thresholds, allowing the system to achieve high efficiency through selective activation while maintaining manageable complexity through clear functional separation.
Solution Approach 2:
The system introduces confidence scoring as an intermediary mechanism that mediates between different processing tiers. The confidence score acts as a decision variable that determines whether to escalate from classical to contextual processing, simplifying the control logic despite the multi-tiered architecture.
Data Source
AI summary
Apparatus, methods and systems for contextual prediction processing is provided. Methods may include receiving a conversation from an entity. The conversation may include current utterance, previous utterances and details. Methods may include using an action-topic ontology to build, using data retrieved from the current utterance, a conversation frame that corresponds to the current utterance. Methods may include merging the conversation frame with data, retrieved from the previous utterances and the details, to generate a target conversation frame. Methods may include validating the target conversation frame to prevent looping over historic data in the event that the current utterance fails to add relevant information. Methods may include generating an enhanced contextual utterance based on algorithms and the target conversation frame. The enhanced contextual utterance may be used to understand the current utterance in a context of the conversation. Methods may include returning the enhanced contextual utterance to the entity.