Proactive User Intention Prediction via Contextual Questioning
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Solution Overview
Problem
Conventional virtual assistants fail to accurately predict user intentions due to inadequate understanding of context information, leading to incorrect information provision and actions, as they are passive and do not initiate interactions with users.
Innovation Solution
An electronic device that predicts user intentions by using context information to determine questions, receive responses, and iteratively refine predictions, allowing for improved context understanding and proactive interaction with the user to provide relevant information and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional virtual assistants passively wait for user commands, then device complexity is reduced, but user intention prediction accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by proactively initiating interactions with users based on predicted intentions. Instead of waiting for user commands, the virtual assistant predicts what the user wants to do and starts the interaction process, thereby improving intention prediction accuracy without significantly increasing device complexity
Solution Approach 2:
The system implements feedback mechanisms by analyzing user responses to refine intention predictions. User feedback from interactions is used to continuously improve the accuracy of intention prediction, creating a closed-loop system that learns and adapts while maintaining reasonable system complexity
2Ease of operation
If virtual assistants do not initiate interactions, then ease of operation is improved, but information completeness deteriorates
Solution Approach 1:
The system performs preliminary actions by proactively initiating interactions based on predicted user intentions. This allows the system to gather context information that would otherwise be unavailable, improving information completeness while maintaining ease of operation through natural, context-aware interactions
Solution Approach 2:
The system changes operational parameters by dynamically adjusting when and how to initiate interactions based on confidence levels of intention predictions. When prediction confidence is high, the system proactively engages; when low, it waits for user input, thereby balancing information gathering with ease of operation
3Device complexity
If context information is insufficient, then device complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The system applies multi-functionality by using a unified context processing framework that handles multiple types of context information (user preferences, environmental data, interaction history) through a single intelligent model. This improves prediction accuracy without proportionally increasing device complexity by avoiding separate processing systems for each data type
Data Source
AI summary
An electronic device for predicting an intention of a user is configured to provide a question to the user and predict at least one first intention of the user based on context information associated with the user. The device is also configured to determine a question based on the at least one first intention of the user. The device is further configured to provide the question to the user. The device is additionally configured to receive a response to the question from the user. The device is also configured to predict at least one second intention of the user based on the at least one first intention of the user and the response to the question from the user.