Condition-Based Utility Scaling for Automated Assistant Responses
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Solution Overview
Problem
Existing automated assistants integrated with third-party applications often provide non-substantive responses when queried on topics outside their authorized scope, leading to diminished user experience and resource inefficiency for the third-party entity.
Innovation Solution
An automated assistant that provides services based on scaling criteria, allowing third-party entities to authorize specific utilities and adaptively utilize additional utilities when certain conditions are met, such as semantic similarity or budget thresholds, ensuring relevant and efficient responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the automated assistant utilizes additional utilities beyond those expressly authorized by the third party entity, then the assistant can provide more comprehensive responses to user queries, but the resource consumption and costs for the third party entity increase
Solution Approach 1:
The system dynamically adjusts the scope of utility utilization by introducing scaling criteria parameters (semantic similarity threshold, budget threshold, authorization status) that control whether additional utilities can be invoked. This allows the assistant to adapt its response capability based on real-time evaluation of these parameters rather than having a fixed utility scope.
Solution Approach 2:
The utility authorization model transitions from a static predefined scope to a dynamic adaptive scope. The system continuously evaluates semantic similarity between user queries and authorized utilities, and adjusts utility invocation decisions based on budget thresholds and authorization status, enabling flexible resource allocation.
2Use of energy by moving object
If the automated assistant restricts utility usage to only those expressly authorized by the third party entity, then resource consumption is controlled, but the assistant cannot respond to queries outside the authorized scope
Solution Approach 1:
The system introduces an intermediary evaluation layer (semantic similarity assessment and scaling criteria checking) between the user query and the utility invocation. This intermediary determines whether queries outside the strictly authorized scope can be handled by finding semantically similar authorized utilities, thus bridging the gap between restricted resources and comprehensive response capability.
Solution Approach 2:
Authorized utilities are designed to handle multiple related functions through semantic generalization. A single authorized utility can respond to various queries that are semantically similar to its core function, enabling one utility to serve multiple purposes and reducing the need for numerous specifically authorized utilities.
3Loss of energy
If the automated assistant provides error indications for queries outside authorized scope, then resource misallocation is prevented, but user experience is diminished
Solution Approach 1:
The system converts potential harmful errors (non-substantive responses) into beneficial opportunities by using semantic similarity assessment. When a query falls outside authorized scope, the system evaluates whether semantically similar authorized utilities can provide helpful responses, transforming what would be an error condition into a successful query resolution.
Solution Approach 2:
The system implements feedback mechanisms where the results of semantic similarity evaluations and utility invocations are used to adjust future decisions. The third party entity receives feedback about query patterns and utility performance, enabling them to optimize their authorization scope and budget thresholds over time.
Data Source
AI summary
Implementations set forth herein relate to an automated assistant that can employ various utilities according to whether certain conditions are satisfied for a given utility and/or whether a particular input is determined to have a threshold degree of relevance to the given utility. The automated assistant can be customized by a third party entity, which can make the automated assistant available via a device and/or application. The automated assistant can operate according to certain utilities that have been permitted by the third party entity. However, the third party entity can allow the automated assistant to employ other scalable utilities when a user input is determined to have a threshold degree of relevance and/or when usage of certain scalable utilities has not exceeded a threshold. The utility can refer to a machine learning model and/or other data that can be employed to resolve an input.