Adaptive Significant-Location Clustering from User Movement Patterns
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Solution Overview
Problem
Existing location-based services struggle to accurately predict significant user locations for providing personalized assistance without requiring additional user input, leading to suboptimal user experiences.
Innovation Solution
A mobile device uses machine learning and data mining techniques to learn user movement patterns by identifying significant locations based on dwell time and transitions, constructing a state model to predict future locations, and providing adaptive user assistance without explicit user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the mobile device uses a fixed threshold time for determining significant locations, then the determination process is simple, but it cannot adapt to different user behaviors and hints, leading to inaccurate location significance detection
Solution Approach 1:
The patent applies dynamics by making the threshold time variable rather than fixed. The system dynamically adjusts the threshold time based on user hints and historical behavior patterns. When a hint is detected (such as frequent visits or long dwell time), the system reduces the threshold time, allowing faster determination of significant locations. This dynamic adjustment resolves the contradiction by enabling accurate detection adaptable to different user behaviors while maintaining operational simplicity through automated rule-based adjustments.
Solution Approach 2:
The system changes the parameter of threshold time from a static value to a variable that depends on user hints and historical data. By monitoring user interactions and behavioral patterns, the system modifies the threshold time parameter to optimize location significance detection. This parameter change allows the system to accurately identify significant locations for different users and contexts without requiring complex manual configuration.
2Adaptability or versatility
If the mobile device requires explicit user input for location-based services, then the service provision is straightforward, but it cannot proactively anticipate user needs and provide personalized assistance
Solution Approach 1:
The system performs preliminary actions by proactively determining significant locations and anticipating user needs before explicit requests are made. By analyzing user hints and historical behavior patterns, the system pre-identifies locations that are likely to be important to the user and prepares relevant services in advance. This allows the system to provide personalized assistance without requiring users to explicitly query for each service.
Solution Approach 2:
The system implements self-service by automatically learning user preferences and behavior patterns without requiring continuous user input. Once significant locations are identified through initial hint analysis, the system autonomously provides location-based services tailored to user needs. The system serves itself by maintaining and updating user profiles based on observed behaviors, enabling personalized service provision with minimal user interaction.
3Measurement precision
If the mobile device monitors user locations continuously with high precision, then the location accuracy is high, but the energy consumption increases significantly
Solution Approach 1:
The system applies partial action by selectively determining significant locations only when user hints indicate potential interest, rather than continuously monitoring all locations with equal precision. The system uses a two-tier approach: initial low-power hint detection followed by higher-precision location determination only when necessary. This partial action reduces overall energy consumption while maintaining high accuracy for locations that are actually significant to the user.
Data Source
AI summary
Systems, methods, and program products for providing services to a user by a mobile device based on the user's daily routine of movement. The mobile device determines whether a location cluster indicates a significant location for the user based on one or more hints that indicate an interest of the user in locations in the cluster. The mobile device can perform adaptive clustering to determine a size of area of the significant location based on how multiple locations converge in the location cluster. The mobile device can provide location-based services for calendar items, including predicting a time of arrival at an estimated location of a calendar item. The mobile device can provide various services related to a location of the mobile device or a significant location of the user through an application programming interface (API).