Automated Assistant Response Adaptation via Dynamic Familiarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated assistants lack dynamic adaptation of responses based on user familiarity, leading to inefficient interactions and resource utilization, as they do not account for changing user familiarity over time or the specific intents and historical interactions.
Innovation Solution
A system that generates a dynamic familiarity measure based on historical interactions and intent-specific parameters, adapting responses to be more abbreviated and resource-efficient as user familiarity increases, using techniques such as response abridgment, pronoun substitution, and non-speech sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the automated assistant provides detailed and robust responses to all users, then user guidance and comprehension are improved, but network resource usage and interaction duration increase
Solution Approach 1:
The system dynamically adapts response characteristics based on the calculated familiarity measure between user and assistant. When familiarity is high, responses are abbreviated; when familiarity is low, responses are more detailed and robust. This dynamic adjustment optimizes the balance between providing adequate guidance and conserving network resources.
Solution Approach 2:
The system applies different response qualities to different users based on their individual familiarity measures. Instead of using a uniform response strategy for all users, the assistant tailors the level of detail and robustness locally to each user's needs, ensuring efficient resource usage while maintaining appropriate guidance levels.
2Ease of operation
If the automated assistant provides detailed responses to all users, then user comprehension is improved, but interaction duration increases
Solution Approach 1:
The system dynamically adjusts response length and detail based on the familiarity measure. For users with high familiarity, abbreviated responses reduce interaction duration while maintaining comprehension. For users with low familiarity, more detailed responses ensure understanding without unnecessarily extending interactions for experienced users.
Solution Approach 2:
The system changes the parameter of response detail level based on the calculated familiarity measure. By adjusting this parameter dynamically, the system optimizes interaction duration while preserving user comprehension through appropriately tailored response lengths and complexities.
3Device complexity
If the automated assistant uses standardized responses for all users, then system complexity is reduced, but adaptability to user needs deteriorates
Solution Approach 1:
The system automatically calculates the familiarity measure and selects appropriate response characteristics without requiring manual intervention or complex configuration. This self-service approach enables adaptability while keeping system complexity manageable through automated decision-making based on interaction history.
Solution Approach 2:
The system uses historical interaction data as feedback to continuously refine the familiarity measure and adjust response characteristics accordingly. This feedback mechanism enables the system to adapt to user needs dynamically while maintaining relatively simple architecture through data-driven decision-making.
4Loss of information
If the automated assistant provides comprehensive responses to all users, then information completeness is improved, but network resource usage increases
Solution Approach 1:
The system dynamically adjusts the level of information provided in responses based on the familiarity measure. For familiar users, abbreviated responses convey essential information with reduced network resource usage. For unfamiliar users, comprehensive responses ensure information completeness while using more network resources, optimizing the balance between these competing requirements.
Solution Approach 2:
The system applies different information completeness levels locally to different users based on their familiarity. This localized approach ensures that each user receives appropriately tailored information without unnecessarily transmitting excessive data to users who already have high familiarity with the system.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Techniques are disclosed that enable dynamically adapting an automated assistant response using a dynamic familiarity measure. Various implementations process received user input to determine at least one intent, and generate a familiarity measure by processing intent specific parameters and intent agnostic parameters using a machine learning model. An automated assistant response is then determined that is based on the intent and that is based on the familiarity measure. The assistant response is responsive to the user input, and is adapted to the familiarity measure. For example, the assistant response can be more abbreviated and/or more resource efficient as the familiarity measure becomes more indicative of familiarity.