AI Assistant Responses With Contextual Usage Insights
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Solution Overview
Problem
Existing AI assistants often provide inadequate or outdated responses to user queries about application features, leading to increased computing resource consumption due to repetitive queries and inefficient use of storage and network resources.
Innovation Solution
An AI assistant system that integrates contextual usage insights by generating responses based on both application documentation and usage data, providing contextual usage data to enhance user understanding and reduce repetitive queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI assistants provide responses based only on application documentation, then response generation is simple and fast, but responses become outdated and inaccurate when application behavior changes
Solution Approach 1:
The patent merges two information sources: application documentation and actual usage data. The system combines static documentation with dynamic usage patterns to generate responses that are both accurate and up-to-date. This is achieved by integrating a usage data collection module with the AI assistant response generation system, allowing the AI to reference both documented features and actual usage patterns.
Solution Approach 2:
The system performs preliminary action by collecting and storing usage data in advance before it is needed for response generation. Usage patterns are continuously monitored and stored in a database, so when a user queries the AI assistant, the relevant usage data is already available to improve response accuracy without adding computational overhead at query time.
2Loss of information
If users submit repetitive queries due to inadequate responses, then more information can be obtained, but computing resource consumption increases
Solution Approach 1:
The patent implements feedback by analyzing user queries and identifying when users are asking repetitive questions due to inadequate previous responses. The system uses this feedback to refine its response strategy, providing more comprehensive answers that prevent future repetitive queries. The AI assistant learns from query patterns and adjusts its information provision to reduce unnecessary computing resource consumption.
Solution Approach 2:
The system performs preliminary action by providing comprehensive responses that anticipate follow-up questions. By including contextual usage insights and related information in the initial response, the AI assistant reduces the need for users to submit multiple repetitive queries, thereby reducing computing resource consumption while maintaining information completeness.
3Loss of information
If AI assistants provide detailed responses with contextual usage data, then user understanding improves, but response generation time increases
Solution Approach 1:
The patent applies local quality by selectively adding contextual usage data to responses based on the specific query and user needs. Rather than always providing detailed contextual information, the system analyzes each query and includes usage insights only when relevant to answering the user's question. This maintains user understanding while avoiding unnecessary response generation time.
Solution Approach 2:
The system uses partial action by providing just enough contextual usage data to improve user understanding without over-providing information. The AI assistant selectively includes usage patterns and contextual insights that directly relate to the user's query, avoiding the time cost of generating comprehensive detailed responses for every query while still significantly improving user understanding compared to basic documentation-only responses.
Data Source
AI summary
Some aspects relate to technologies for an artificial intelligent (AI) system that, among other things, enhances responses to concepts questions for an application with contextual usage insights. In accordance with some aspects, a user query is determined to comprise a concepts question regarding an application. Responsive to determining the user query comprises the concepts question, documentation regarding the application relevant to the user query is identified. A generative model generates text for a response to the concepts question using the documentation regarding the application. Additionally, a determination is made to add contextual usage insights to the response. Responsive to determining to add contextual usage insights to the response, usage data relevant to the user query and/or the response is retrieved. The generative model generates text for a final response using the response and the usage data, and the final response is provided to a user device for presentation.