Search Query Targeting via Physical Environment Data
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Solution Overview
Problem
Existing internet-based services face challenges in delivering targeted search results and advertisements due to the issue of polysemy, where a single word or phrase can have different meanings in various contexts, making it difficult for systems to determine the user's intended meaning.
Innovation Solution
A system that analyzes past search queries and physical environment data to identify correlations between user actions and environmental conditions, applying transformations to search queries to provide more relevant results and advertisements based on real-time physical environment data, such as weather and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search results are targeted based on user attributes and behavior, then user satisfaction and ad effectiveness improve, but the system complexity increases due to polysemy resolution requirements
Solution Approach 1:
The patent introduces physical environment data as an intermediary factor that mediates between the user's search query and the intended meaning. By incorporating environmental context (weather, location, time) as a mediating element, the system resolves polysemy without requiring complex analysis of user behavior patterns or psychological states, thus improving targeting accuracy while controlling system complexity.
Solution Approach 2:
The patent changes the parameters used for targeting from traditional user-based attributes (demographics, behavior history) to physical environment parameters (temperature, location, time of day). This parameter substitution simplifies the system architecture while improving targeting reliability, as physical environment data is objectively measurable and directly contextualizes the search query without requiring complex inference.
2Measurement precision
If physical environment data is collected and analyzed to improve targeting, then search result relevance improves, but data processing complexity and requirements increase
Solution Approach 1:
The patent extracts only the essential physical environment parameters (location, weather, time) that are most relevant to search query disambiguation, separating these from the complex web of user behavior data. By taking out only the necessary environmental factors, the system achieves improved measurement precision for search relevance while minimizing data processing complexity, as these parameters are straightforward to collect and process compared to comprehensive user behavior analysis.
3Reliability
If multiple search queries are analyzed to identify environmental correlations, then targeting accuracy improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing correlations between physical environment parameters and search result performance using historical data. These pre-computed correlations are stored and can be quickly applied to new search queries without requiring real-time analysis of extensive historical data. This preliminary processing enables the system to achieve high correlation identification accuracy while minimizing the time required for actual search query analysis.
Data Source
AI summary
A computer-implemented information targeting method is disclosed. The method includes receiving a search query from a computing device, where the search query has at least two different meanings, identifying metadata associated with the search query, using the metadata to promote search results corresponding to a first meaning of the at least two meanings of the search query, and providing search results corresponding to the first meaning of the search query to the computing device. Using the metadata to promote search results may comprise analyzing (a) prior search queries that are related to the received search query, (b) metadata associated with the prior search queries, and (c) selections of search results provided in response to the prior search queries; and identifying a correlation between the metadata associated with the prior search queries and selections of search results presented in response to the prior search queries.