Mobility Data Filtering for Home Buyer Identification
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Solution Overview
Problem
Existing computing systems face difficulties in accurately identifying specific individuals interested in advertisements or services related to home purchases, as they lack data to recognize patterns between interested and non-interested individuals, and struggle to display information in a user-friendly and comprehensive manner.
Innovation Solution
The system utilizes mobility data and parcel data to identify geographic locations frequently visited by mobile devices and associate them with parcels available for sale or rent, updating user information and generating a user interface that filters and displays relevant data within a single view, incorporating filters like geography and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobility data and parcel data are collected and analyzed to identify end users interested in home purchases, then identification accuracy is improved, but data privacy and security risks increase
Solution Approach 1:
The patent introduces an intermediary processing layer that analyzes mobility data patterns without directly exposing or storing sensitive personal information. The system uses aggregated behavioral data to identify potential home buyers while maintaining privacy through indirect analysis methods, thus resolving the contradiction between accurate identification and privacy protection.
Solution Approach 2:
The system transforms raw mobility data into behavioral patterns and interest indicators through parameter changes. By converting precise location data into generalized behavior profiles (e.g., frequency of visiting real estate areas, time spent in specific zones), the system maintains identification accuracy while reducing privacy risks through data abstraction.
2Loss of information
If comprehensive user data and multiple filters are integrated into a single user interface view, then information completeness is improved, but interface complexity increases
Solution Approach 1:
The patent segments the comprehensive user interface into distinct functional modules, each handling specific data types or filter categories. By dividing the interface into organized sections (e.g., user profiles, behavioral filters, geographic filters, contact information), the system presents complete information in a structured, manageable layout that reduces perceived complexity while maintaining information completeness.
Solution Approach 2:
The system organizes comprehensive user data across multiple dimensional layers within the interface, such as hierarchical filtering options and multi-level data presentation. This dimensional organization allows users to navigate complete information sets through structured layers rather than overwhelming flat displays, reducing interface complexity while preserving information completeness.
3Measurement precision
If detailed behavioral patterns and mobility data are analyzed to create user profiles, then targeting precision is improved, but computational resources required increase
Solution Approach 1:
The patent implements partial action by analyzing only the most relevant mobility data patterns and behavioral indicators necessary for identification purposes. Rather than processing all available data comprehensively, the system focuses on key metrics (e.g., visit frequency to real estate areas, duration ofๅ็), achieving sufficient targeting precision with reduced computational overhead.
Solution Approach 2:
The system performs preliminary filtering and aggregation of mobility data before detailed analysis. By pre-processing data to identify and retain only significant patterns (e.g., filtering out routine commuting patterns and focusing on unusual real estate area visits), the system reduces the volume of data requiring intensive computational analysis while maintaining targeting precision.
Data Source
AI summary
An improved computing system can use mobility data corresponding to a particular mobile device to identify one or more geographic locations to which the mobile device traveled within a certain period of time. The computing system can use parcel data to identify parcels that are located at any one of the identified geographic locations. The computing system can further use the parcel data to identify a subset of the identified parcels that are listed for sale or rent. If the computing system identifies at least one parcel that the mobile device visited that is available on the market, this may indicate that the user who operates the mobile device may be shopping for a home. In response, the computing system can update information about the user to indicate that the user may be shopping for a home.