Automated Assistant Reasoning Transparency Mechanism
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Solution Overview
Problem
Users may not understand why an automated assistant performs certain actions or provides specific responses to user inputs, leading to confusion and potential security concerns regarding data usage.
Innovation Solution
Implementing a mechanism that provides users with reasoning behind the automated assistant's actions, by processing user inputs to determine relevant data and presenting this reasoning to the user upon request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the automated assistant provides responses without explaining reasoning, then the system operates efficiently with lower complexity, but users do not understand why specific actions are taken leading to confusion and security concerns
Solution Approach 1:
The system pre-generates and stores reasoning explanations for different types of assistant actions before they are needed. When a user requests an explanation, the system retrieves the pre-computed reasoning from storage rather than generating it in real-time, thereby providing transparent information about data usage while avoiding the computational complexity of on-demand reasoning generation.
Solution Approach 2:
The patent extracts the reasoning explanation component from the main assistant response flow. Instead of integrating reasoning generation into every assistant action, the system separates the explanation provision as a distinct, on-demand feature that only activates when users explicitly request it, thus maintaining system efficiency while providing transparency.
2Loss of information
If the automated assistant provides detailed reasoning for all actions, then user understanding and trust improve, but computational resources are consumed and response time increases
Solution Approach 1:
The system provides reasoning explanations only for specific types of actions or when explicitly requested by users, rather than generating comprehensive explanations for all possible assistant actions. This partial approach to explanation provision maintains user understanding for critical decisions while conserving computational resources by avoiding unnecessary reasoning generation for routine operations.
Solution Approach 2:
The system pre-computes and stores reasoning explanations for common assistant actions, so that when users request explanations, the system can retrieve pre-generated reasoning rather than performing computationally intensive real-time analysis. This preliminary action approach provides detailed reasoning when needed while minimizing ongoing computational resource consumption.
3Loss of time
If the automated assistant does not provide reasoning, then the system remains simple and fast, but users may provide additional inputs to clarify misunderstandings, prolonging the dialog duration
Solution Approach 1:
The system implements a feedback mechanism where users can request explanations for assistant actions through simple user inputs. When users provide feedback indicating confusion or requesting clarification, the system responds with relevant reasoning explanations, thereby resolving misunderstandings and preventing the need for additional clarifying inputs without significantly increasing system complexity.
Solution Approach 2:
The system prepares and stores reasoning explanations in advance, so that when users express confusion or request clarification, the system can immediately provide the needed information from pre-computed reasoning rather than engaging in extended back-and-forth dialog to clarify misunderstandings, thus reducing overall dialog duration.
4Loss of information
If the automated assistant processes and stores reasoning data, then user transparency and security understanding improve, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts and stores only the essential reasoning explanations separately from the main assistant processing data. By isolating and storing only the explanatory text rather than all processing data, the system provides data usage transparency while minimizing data storage requirements and maintaining security.
Solution Approach 2:
The system segments data storage into two distinct components: operational data for executing assistant actions and reasoning explanations for providing transparency. This segmentation allows the system to store and provide access to reasoning information without requiring storage of all underlying processing data, thereby reducing overall data storage requirements while maintaining user transparency.
Data Source
AI summary
Implementations described herein relate to causing certain reasoning with respect to why an automated assistant performed (or did not perform) certain fulfillment and/or alternate fulfillment of an assistant command. For example, implementations can receive user input that includes the assistant command, process the user input to determine data to be utilized in performance of the certain fulfillment or the alternate fulfillment of the assistant command, and cause the automated assistant to utilize the data to perform the certain fulfillment or the alternate fulfillment of the assistant command. In some implementations, output that includes the certain reasoning can be provided for presentation to a user in response to additional user input that requests the certain reasoning. In some implementations, a selectable element can be visually rendered and, when selected by the user, the output that includes the certain reasoning can be provided for presentation to the user.