AI Query Context Evaluation for Reducing Bandwidth
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Solution Overview
Problem
Existing systems struggle to accurately determine user intent and provide relevant content efficiently, often requiring multiple queries and generating unnecessary processing and bandwidth usage.
Innovation Solution
An artificial intelligence system that evaluates attributes over multiple user queries by generating input data based on previous queries, using machine learning models to determine attribute importance, and selecting relevant digital components for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes multiple queries sequentially to evaluate attributes, then the accuracy of user intent determination is improved, but the processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by processing previous queries and extracting attribute information in advance before the current query is fully processed. The AI agent maintains context from previous queries and pre-evaluates attribute importance, allowing faster processing of subsequent queries while maintaining high accuracy in user intent determination.
2Measurement precision
If the system generates comprehensive responses to all queries, then the relevance of provided content is improved, but the bandwidth consumption and processing load increase
Solution Approach 1:
The system extracts only the essential attribute information and key content elements needed to address the user's query. By identifying and extracting only the necessary attributes from previous queries and current queries, the system generates concise relevant responses that reduce bandwidth consumption while maintaining high content relevance through targeted information extraction.
Solution Approach 2:
The system changes the parameters of response generation by adjusting the level of detail and scope of responses based on the evaluated attribute importance. For attributes marked as required, the system provides comprehensive information, while for user preference attributes, it provides summarized information, thereby optimizing bandwidth usage while maintaining relevance.
3Measurement precision
If the system processes all queries in detail to ensure accuracy, then the quality of attribute evaluation is improved, but the processing load and system complexity increase
Solution Approach 1:
The system segments the query processing into distinct phases: processing previous queries to extract attribute information, evaluating the current query against extracted attributes, and generating responses. This segmentation allows the system to maintain high attribute evaluation quality by systematically processing information in manageable stages rather than attempting to process all queries simultaneously in detail.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence (AI) to evaluate attributes of items and use the results of the evaluation to provide relevant content. In one aspect, a method includes receiving, by an AI system and from a client device of a user, a first query of a user session. For each additional query, the AI system generates input data based on the additional query and data related to one or more previous queries received during the user session. The AI system provides the input data as an input to a machine learning model trained to output attributes of items and importance data indicating a relative importance of the attributes based on received inputs. The AI system selects one or more digital components based on the set of attributes and the importance data output by the model.