Ambiguity Resolving Conversational Engine
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Solution Overview
Problem
Conversational understanding systems face errors due to unclear user input, mispronunciation, misspelling, or ambiguous language, leading to incorrect responses or actions, and requesting user feedback can be tedious for users.
Innovation Solution
A system with an ambiguity resolving conversation understanding engine that determines the type and degree of ambiguity in user input and provides appropriate feedback, such as audio and visual prompts, to request additional input or clarify intentions, using machine learning models and interaction with a server for data retrieval and ambiguity resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system requests feedback or confirmation from users before taking action, then the accuracy of understanding user input is improved, but the ease of operation deteriorates due to users finding responding tedious and irritating
Solution Approach 1:
The system performs preliminary analysis of user input to determine ambiguity levels and confidence scores before executing actions. By pre-assessing the quality of input understanding, the system can proactively decide when confirmation is necessary and when it can be skipped, reducing unnecessary user interactions while maintaining accuracy for ambiguous cases
Solution Approach 2:
The system implements selective feedback mechanisms where confirmation requests are dynamically generated based on ambiguity detection. Instead of universally requesting feedback, the system provides targeted confirmation only when input ambiguity exceeds thresholds or confidence scores indicate potential errors, making the interaction more efficient and less tedious for users
2Reliability
If the system requests feedback or confirmation from users, then the reliability of performing correct actions is improved, but the loss of time increases due to additional interaction steps
Solution Approach 1:
The system conducts preliminary ambiguity assessment and confidence evaluation before action execution. By pre-identifying potentially erroneous interpretations through ambiguity detection algorithms, the system can prepare appropriate confirmation requests in advance, reducing the time penalty of post-action corrections and ensuring reliable execution only when confidence is sufficient
Solution Approach 2:
The system dynamically adjusts confirmation thresholds based on input characteristics, context, and user history. By changing the parameter of confirmation likelihood based on measured ambiguity levels and confidence scores, the system optimizes the balance between reliability and time loss, requesting confirmation only when necessary rather than universally
3Device complexity
If the system uses simple error handling without ambiguity detection, then the device complexity is reduced, but the productivity deteriorates due to incorrect responses and user frustration
Solution Approach 1:
The system segments the error handling process into distinct modules: ambiguity detection, confidence scoring, threshold comparison, and selective confirmation generation. By dividing the complexity into separate functional components, each handling a specific aspect of input validation, the system achieves robust error handling without overwhelming system complexity, improving productivity through targeted intervention
Solution Approach 2:
The system introduces an intermediary ambiguity detection layer between input reception and action execution. This intermediary component analyzes input quality, determines ambiguity levels, and mediates whether confirmation is needed before proceeding. This intermediate step prevents incorrect actions without requiring complete system redesign, maintaining relative simplicity while significantly improving productivity by avoiding erroneous executions
Data Source
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AI summary
Systems, components, devices, and methods for resolving ambiguity in a conversational understanding system are provided. A non-limiting example is a system or method for resolving ambiguity in a conversational understanding system. The method includes the steps of receiving a natural language input and identifying an agent action based on the natural language input. The method also includes the steps of determining an ambiguity value associated with the agent action and evaluating the ambiguity value against an ambiguity condition. The method includes the steps of when determined that the ambiguity value meets the ambiguity condition: selecting a prompting action based on the ambiguity associated with the identified agent action, performing the prompting action, receiving additional input in response to the prompting action, and updating the agent action to resolve the ambiguity based on the additional input. The method also includes the step of performing the agent action.