Search Caption Enhancement via Off-Page Entity Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often face inefficiencies in completing tasks through search engines as they need to navigate and re-enter information, and existing search experiences fail to provide holistic information related to entities like people, places, and things, leading to unnecessary time spent finding desired information.
Innovation Solution
The architecture integrates off-page data sources like yellow pages, coupons, and social networks to enhance search result captions with relevant information, using online and offline mechanisms to identify entities and apply relevance heuristics for data presentation, thereby reducing the need for multiple queries and enhancing task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search engines provide basic search results, then users can find websites, but users must navigate websites and re-enter information to complete tasks
Solution Approach 1:
The system performs preliminary actions by extracting entity information from web pages during indexing and pre-fetching additional relevant data from off-page sources before the user actually needs it. This advance preparation eliminates the need for users to navigate multiple pages and re-enter information, directly resolving the contradiction between ease of operation and time loss.
Solution Approach 2:
The patent introduces an intermediary layer between the search engine and the target website. This intermediary system captures entity data from web pages, enriches it with additional information from external sources, and presents it in enhanced captions. This mediator handles the complex tasks of data extraction, integration, and presentation, making the search process easier and faster for users.
2Loss of information
If search results contain only basic information from target pages, then captions are simple, but holistic information about entities is incomplete
Solution Approach 1:
The system segments the information gathering process into distinct components: entity identification on web pages, data extraction, enrichment from off-page sources, and caption generation. This segmentation allows each component to be optimized independently and managed through modular processes, reducing overall system complexity while achieving complete entity information.
Solution Approach 2:
The system employs self-service mechanisms where entity data is automatically extracted from web pages during regular indexing operations, and additional information is automatically fetched from off-page sources based on entity identification. This automation eliminates manual intervention and reduces operational complexity while ensuring comprehensive information collection.
3Productivity
If additional off-page data is integrated into captions, then task completion is enhanced, but data processing complexity increases
Solution Approach 1:
The system uses periodic action by implementing offline mechanisms that periodically extract and cache entity data from web pages, and online mechanisms that retrieve this pre-processed data when needed. This periodic approach separates heavy data processing from real-time query response, improving productivity while managing complexity through time-based separation of concerns.
Solution Approach 2:
The offline mechanism performs preliminary data extraction and caching of entity information before it is needed for search queries. This advance preparation stores processed data in the index, so that during online operations, the system only needs to retrieve and display pre-processed information, significantly reducing real-time processing complexity while enhancing task completion efficiency.
Data Source
AI summary
Architecture that uses content from off-page data sources such as feeds (e.g., yellow pages, coupons, social networks, commerce, etc.) to present additional, relevant information in association with search results. The additional and relevant information is directly relevant to the implicit task the user is trying to accomplish. The architecture includes online and offline mechanisms that identify an entity represented on a web page and look-up information related to that entity in disparate data sources. Relevance heuristics are employed to determine which of the available entity data to show in the caption given the user query, the web page, and the underlying user task (other known information about the user such as geographic location).