Local Document Verification in Search Systems
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Solution Overview
Problem
Current search systems face challenges in providing accurate and resource-efficient local search results, as they often return non-local data alongside local results, wasting resources and diminishing user satisfaction.
Innovation Solution
The method involves identifying candidate local documents by associating them with businesses, verifying their local relevance through secondary document references, and providing local data only for confirmed local documents, which includes presenting this data in an answer box that can display maps and additional business information upon user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search systems return all documents responsive to a query, then the quantity of search results is maximized, but resource waste increases due to inclusion of non-local data
Solution Approach 1:
The patent extracts and identifies local documents from the set of all documents responsive to a query by using machine learning models to determine geographic location information. Only documents determined to be local are included in the final search results, eliminating non-local data and reducing resource waste while maintaining result quantity for relevant local content.
2Measurement precision
If search systems verify local relevance of documents, then the accuracy of local search results is improved, but the complexity of the search process increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the query processing and result generation stages. These models automatically determine whether documents contain local information by analyzing geographic location data, thereby improving accuracy without requiring complex manual verification processes or significant increases in system complexity.
3Loss of information
If search systems provide local data for all documents, then the completeness of information is maximized, but resource efficiency decreases
Solution Approach 1:
The patent applies local quality by providing enhanced local data (such as map locations and business information) only for documents that are determined to be local in nature. Documents without local relevance receive standard search results without additional local data processing, thereby maintaining information completeness for relevant documents while improving resource efficiency by avoiding unnecessary processing of non-local content.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing local data with search results. One of the methods includes obtaining a plurality of documents responsive to a query; determining that a first document of the plurality of documents is associated with at least one business; determining that the first document is a candidate local document in response to the determining; in response to determining that the first document is a candidate local document, verifying that the first document is a local document, the verifying based on one or more second documents of the plurality of documents; and in response to verifying that the first document is a local document, providing local data associated with the first document.