Predictive Location Filtering for Time-Dependent Search Results
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Solution Overview
Problem
Current mapping applications fail to effectively provide points of interest relevant to a user's future location and time needs, as they typically rely on current location or route-based searches, which are not time-dependent and may not account for the user's future position or interests.
Innovation Solution
An electronic device with positioning circuitry and a mapping application predicts a user's future position based on historical data, calendar events, and sensor inputs, and filters search results to provide time-dependent points of interest relevant to the user's expected future location, such as restaurants or gas stations at specific times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search results are based on current location or route-based searches, then the search application is simple and fast, but the results are not time-dependent and may not account for user's future position or interests
Solution Approach 1:
The system performs preliminary actions by predicting the user's future location before the actual search is needed. The prediction engine proactively determines where the user will be at a future time based on current location, historical data, and calendar events, so that when the search is executed, the results are already optimized for the future context rather than requiring complex real-time calculations
Solution Approach 2:
The search system transitions from a static, location-based approach to a dynamic, time-dependent approach. The search parameters are no longer fixed but adapt dynamically based on the predicted future location, which changes over time. This allows the system to provide relevant results at different future times while maintaining manageable complexity through the use of prediction algorithms
2Ease of operation
If the system provides points of interest based on current location only, then the system is simple to operate, but it cannot provide relevant results for future needs such as restaurants at meal time or gas stations when needed
Solution Approach 1:
The system performs self-service by automatically predicting the user's future location and context without requiring explicit user input for each future search scenario. The prediction engine uses available data (current location, historical patterns, calendar events) to autonomously determine future context, eliminating the need for users to manually specify future locations or times while maintaining high result relevance
Solution Approach 2:
The system incorporates feedback loops where prediction results are continuously refined based on user interactions and actual future locations. The system learns from discrepancies between predicted and actual locations, improving the accuracy of future predictions and ensuring that search results remain relevant to user needs without increasing operational complexity
Data Source
AI summary
Systems and methods are provided for identifying points of interest and specific locations located in the vicinity of an expected future location of a user. When a user provides a search request for a point of interest that will be of interest only at a future time, the electronic device can automatically predict an expected future location of the user, and provide points of interest near the expected future location.