Automated Assistant Utility Scaling by Query Relevance
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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
Implementing an automated assistant that provides services according to scaling criteria, allowing third-party entities to authorize specific utilities based on budget, semantic similarity, or predefined thresholds, enabling adaptive query fulfillment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the automated assistant is integrated with third-party applications to provide assistant functionality, then user accessibility and convenience are improved, but the assistant may respond with error indications or non-substantive output when queried on topics outside the authorized scope, diminishing user experience
Solution Approach 1:
The system dynamically changes the parameter of utility authorization scope. Instead of a fixed authorized scope, the system adjusts which utilities can be invoked based on the semantic relevance of the user's current query to the third-party application's domain. This allows the assistant to reliably handle both in-scope and out-of-scope queries without producing errors.
2Adaptability or versatility
If the automated assistant is authorized to provide a broad range of utilities through third-party applications, then service coverage and user satisfaction are improved, but resource consumption and costs for the third-party entity increase
Solution Approach 1:
The system applies partial action by selectively enabling only the necessary subset of utilities based on query relevance. Instead of always providing full service coverage, the system activates utilities on-demand when the query semantically matches the application domain, reducing resource consumption while maintaining versatility when needed.
Solution Approach 2:
The utility authorization scope is made dynamic rather than static. The system adjusts which utilities are available based on the current query's semantic relevance to the application domain. This dynamic adaptation allows the system to provide broad service coverage when appropriate while conserving resources when the query is outside the scope.
3Adaptability or versatility
If the automated assistant provides comprehensive service coverage across multiple domains, then user needs are better met, but the complexity of managing and monitoring utility authorizations increases
Solution Approach 1:
The system implements self-service through automated semantic relevance analysis. Instead of requiring manual configuration and monitoring of utility authorizations, the system automatically determines which utilities should be invoked based on the semantic relevance of each query to the application domain, simplifying authorization management while maintaining comprehensive service coverage.
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.