Geographic Location Correlation via User Action Vectors
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Solution Overview
Problem
Existing technologies lack an effective method to identify correlations between geographic locations based on user device actions across a network, limiting the ability to provide relevant information or advertisements to users.
Innovation Solution
A method and device that collect and analyze information from client devices to generate weighted associations between geographic locations, allowing for the identification of correlations by comparing vectors representing user actions, and transmitting relevant content such as advertisements to connected devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If information from multiple client devices is collected and analyzed to identify correlations between geographic locations, then the ability to provide relevant information and advertisements to users is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex task of location correlation into distinct modules: receiving location information from client devices, processing actions at locations, storing weighted associations in a database, generating location vectors, and comparing vectors to identify correlations. This segmentation reduces system complexity by organizing functions into manageable components.
Solution Approach 2:
The patent introduces intermediate data structures (weighted associations and vectors) that mediate between raw location information and correlation results. These intermediaries simplify the comparison process by transforming complex multi-dimensional location data into comparable vector representations.
2Measurement precision
If detailed action information is collected from each client device at geographic locations, then the precision of location correlation identification is improved, but the quantity of data to be processed and stored increases
Solution Approach 1:
The patent transforms detailed action information into weighted numerical associations, changing the parameter representation from qualitative action descriptions to quantitative weighted values. This parameter transformation reduces data volume while preserving the essential information needed for precise correlation identification.
Solution Approach 2:
The patent applies weighting to action information, emphasizing significant actions and de-emphasizing less important ones. This partial action approach processes only the most relevant action data, reducing overall data quantity while maintaining correlation precision.
3Reliability
If vectors representing user actions at geographic locations are generated and compared, then the accuracy of identifying correlated locations is improved, but the computational processing requirements increase
Solution Approach 1:
The patent creates vector copies of location information that can be efficiently compared without processing the original detailed action data. These vector representations serve as simplified copies that maintain correlation accuracy while reducing computational requirements for comparison operations.
Data Source
AI summary
A system and method identifies correlations between locations. A server may receive information identifying an action and a location from a plurality of users. The server may assign a weighted value to each action and store the weighted value and location in a database. The database may be used to generate vector data for each location identifying the weighted values for a number of users. In response to receiving a location from a particular user device, the server may identify a vector associated with the received location. The location vector may be compared to other location vectors to determine if there is any correlation between the vectors. Where the server identifies a correlated vector, the server may send the identification of the corresponding location or information associated with the corresponding location to the particular user device.