AI Deep Linking for Intent-Driven Search Results
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Solution Overview
Problem
Existing search systems struggle to accurately determine user intent and provide relevant information efficiently, leading to unnecessary network traffic, computational burden, and prolonged interaction times.
Innovation Solution
Implementing a search system with an AI subsystem that uses a language model to predict user intent based on queries and prompts, providing deep links to specific resources within the same domain, allowing for conversational responses that enhance search results and reduce computational and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the search system provides comprehensive search results with multiple resources, then the coverage of information is improved, but the network traffic and computational burden increase
Solution Approach 1:
The patent extracts only the most relevant resources from the comprehensive search results based on user intent prediction. Instead of providing all matching resources, the system identifies and presents only those resources that best satisfy the predicted user intent, thereby reducing network traffic while maintaining information quality.
Solution Approach 2:
The system applies different processing quality to different search results based on their relevance to predicted user intent. High-relevance resources receive detailed processing and presentation, while lower-relevance resources are either summarized or excluded, optimizing the balance between information coverage and resource consumption.
2Measurement precision
If the search system analyzes user intent in detail, then the relevance of search results is improved, but the computational load increases
Solution Approach 1:
The system performs partial intent analysis by focusing on key indicators and features that are most predictive of user intent, rather than conducting a complete analysis of all possible factors. This partial analysis achieves sufficient accuracy for most cases while significantly reducing computational requirements.
Solution Approach 2:
The patent employs lightweight, disposable intent prediction models that can be quickly executed and discarded after use. These simplified models provide adequate intent prediction for common scenarios without the heavy computational burden of more complex analysis systems.
3Ease of operation
If the search system provides detailed conversational responses, then the user interaction quality is improved, but the response time increases
Solution Approach 1:
The system performs preliminary processing of search results and intent prediction before generating conversational responses. By pre-processing and organizing information in advance based on predicted user needs, the system can generate high-quality conversational responses more quickly when users interact with the results.
4Loss of information
If the search system navigates to multiple resources to gather information, then the completeness of information is improved, but the number of network requests increases
Solution Approach 1:
The system extracts essential information from resources based on predicted user intent before generating responses. Instead of navigating to and processing multiple complete resources, the system extracts only the specific information needed to satisfy the predicted intent, significantly reducing network requests while maintaining information completeness for the user's needs.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for using artificial intelligence to generate responses. In one aspect, a method includes receiving a query from a client device. Search results for resources determined to be relevant to the query are provided. The search system provides, for display with a given search result of the set of search results, a prompt input interface that enables the user to input a prompt for an artificial intelligence subsystem of the search system. A prompt input is received from the client device. An artificial intelligence subsystem uses a language model to select, from a set of resources hosted by a same domain as the corresponding resource linked to by the given search result, one or more additional resources based at least on the prompt input by the user and the query.