Generative Query Suggestions for Change Event Summaries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing query suggestion techniques are often stale, reiterating past queries or last viewed content, and fail to provide tailored suggestions, leading to an inundation of results that are difficult to navigate and comprehend, especially when tracking global and interest-specific trends.
Innovation Solution
A proactive method using a generative language model to process web data and user data to generate timely, tailored query suggestions and summaries, providing natural language questions and answers about detected changes or trends, personalized to user preferences and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing query suggestion techniques are used to provide trend-based suggestions, then users can access trending topics, but the suggested queries lead to an inundation of results that are difficult to navigate and comprehend
Solution Approach 1:
The patent extracts only the most relevant and essential information from search results by generating concise summaries that highlight key changes and updates. Instead of presenting all search results, the system extracts and presents only the most significant information in a condensed format, making it easy for users to grasp essential content without navigating through numerous results.
Solution Approach 2:
The system changes the parameter of information presentation from detailed search results to condensed summaries with key highlights. By transforming the format and structure of presented information, the system maintains adaptability to trending topics while significantly improving ease of operation through more manageable and comprehensible content delivery.
2Loss of information
If proactive query suggestions are generated using generative models, then timely and tailored suggestions are provided, but computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary actions by proactively generating query suggestions before users actually search, based on detected changes and trends in data sources. By anticipating user information needs and preparing tailored suggestions in advance, the system reduces information loss while the suggestions are generated efficiently through event-driven processing rather than continuous computation.
Solution Approach 2:
The system uses generative models to automatically generate tailored query suggestions and summaries without requiring extensive manual curation or user input. The self-service nature of the generative model allows it to adapt to different topics and user contexts while maintaining efficiency through automated processing of detected changes.
3Loss of information
If users manually search for updates on global and interest-specific trends, then comprehensive information can be found, but significant time and effort are required to stay up to date
Solution Approach 1:
The system performs preliminary actions by continuously monitoring data sources and detecting changes proactively, preparing information summaries before users need them. This advance preparation ensures comprehensive trend information is available when users query, eliminating the need for manual searching while maintaining information completeness.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user interactions with suggested queries and summaries, using this information to refine and improve future suggestions. This feedback loop ensures the system maintains comprehensive coverage of relevant trends while optimizing the efficiency of information delivery to match user needs.
Data Source
AI summary
Systems and methods for proactive query and content suggestion can include obtaining web data, determining a change event occurred, and generating a query and content suggestion. Generating the query and content suggestion can include processing data descriptive of the change event with a generative model to generate one or more model-generated query suggestions. One or more web resources can be obtained then processed to generate a change event summary. The one or more query suggestions and the change event summary can then be provided for display.