Personalizing Conversational AI via Predicted User Knowledge
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Solution Overview
Problem
Conversational AI systems lack recipient design, failing to tailor their responses to the knowledge level of individual users, making interactions less understandable.
Innovation Solution
The development of techniques to predict a user's familiarity with concepts using a background knowledge model and aggregated user models, allowing for personalized output utterances that adjust based on the user's knowledge level, with standard or nonstandard responses provided accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard output utterances are provided assuming concepts are known, then system complexity is reduced, but user understanding deteriorates when users lack background knowledge
Solution Approach 1:
The system performs preliminary analysis of user background knowledge before generating output utterances. By predicting user familiarity with concepts in advance (step 104-106), the system prepares appropriate utterance versions beforehand, selecting between standard and nonstandard forms based on predicted knowledge levels, thus avoiding the need for complex real-time adaptation during conversation.
Solution Approach 2:
The system applies different levels of detail and explanation to different parts of the conversation based on user knowledge levels. Instead of uniformly simplifying or complexing all utterances, the system tailors each output utterance locally to match the user's demonstrated familiarity with specific concepts, providing detailed explanations only where needed while maintaining conciseness where the user already understands.
2Loss of information
If personalized responses are generated based on predicted knowledge level, then user understanding is improved, but system complexity increases
Solution Approach 1:
The system segments output utterances into different versions (standard and nonstandard) based on concept familiarity. By dividing the response generation into discrete segments corresponding to different knowledge levels, the system can select appropriate segments without requiring a completely complex adaptive framework, reducing overall system complexity while still providing personalized responses.
Solution Approach 2:
The system changes parameters of output utterances (level of detail, technical terminology, explanation depth) based on predicted user knowledge levels. By adjusting these parameters systematically according to knowledge level predictions, the system achieves personalization through controlled parameter variation rather than requiring entirely different response generation mechanisms.
3Adaptability or versatility
If recipient design is implemented to tailor utterances, then interaction quality is improved, but computational requirements increase
Solution Approach 1:
The system implements recipient design selectively rather than comprehensively for all aspects of every utterance. By focusing tailoring efforts on key elements like concept explanations and technical terminology while maintaining standard structures for routine interactions, the system achieves improved interaction quality without the full computational overhead of complete personalization.
Solution Approach 2:
The system uses a universal background knowledge model that serves multiple functions: predicting user familiarity, generating appropriate utterances, and adapting to different users. This multi-functional model reduces computational requirements by avoiding the need for separate specialized systems for each adaptation task.
Data Source
AI summary
Techniques for personalizing an AI automated conversational system are provided. In one aspect, a method for personalizing an automated conversational system includes: making predictions of a familiarity of a user with concepts needed to understand a standard output utterance based on the familiarity of an aggregate of users and a background knowledge model of the concepts and related concepts, wherein the standard output utterance assumes that the concepts are known; and giving, by the automated conversational system, an output utterance that is tailored to the user given the predictions. For instance, the automated conversational system can give the standard output utterance to the user when it is predicted that the user is familiar with the concepts, or a nonstandard output utterance when it is it is predicted that the user is unfamiliar with at least one of the concepts.