Tiered Point of Interest Data Allocation for Offline Map Storage
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Solution Overview
Problem
Users have limited access to point of interest data when using map or navigation applications offline, as existing technologies store only limited information about geographic locations such as restaurants, movie theaters, and hospitals, and lack detailed data about these points of interest.
Innovation Solution
The technology optimally allocates limited computer storage on user devices to point of interest data by categorizing it into first-tier, second-tier, and third-tier information, with first-tier data including basic information for display, second-tier data providing entity-specific details, and third-tier data offering aggregate ratings and reviews, and tailors this data based on user interests and usage patterns, ensuring that the most relevant information is stored for offline use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point of interest data is stored offline on a user device, then access to point of interest information is improved, but the amount of storage required increases significantly
Solution Approach 1:
The patent segments point of interest data into multiple tiers: first-tier data (basic information essential for offline functionality), second-tier data (additional details), and third-tier data (comprehensive information). This segmentation allows the system to store only the most critical data offline while maintaining reasonable storage requirements, resolving the contradiction between reliable offline access and storage space consumption.
Solution Approach 2:
The patent applies local quality by making offline data storage selective rather than uniform. Different geographic regions receive different levels of data detail based on user behavior patterns and likelihood of offline access. High-probability regions receive more detailed data locally, while low-probability regions receive minimal data, optimizing the balance between accessibility and storage efficiency.
2Loss of information
If comprehensive point of interest data is stored offline, then information completeness is improved, but the device storage capacity is exceeded
Solution Approach 1:
The patent divides complete point of interest information into hierarchical tiers, storing first-tier data (name, location, basic category) for all points of interest, second-tier data (detailed descriptions, hours of operation) for frequently visited locations, and third-tier data (comprehensive business information, reviews, photos) for high-priority locations. This ensures information completeness for essential data while managing storage capacity through selective detail retention.
Solution Approach 2:
The patent implements partial action by storing complete information for only the most relevant points of interest (those with high offline access probability) while storing summarized or minimal information for less relevant locations. This partial completeness approach maintains sufficient information for critical offline needs without requiring full data storage for all points of interest, thus respecting device storage constraints.
3Ease of operation
If detailed point of interest information is provided offline, then user experience is improved, but data processing and storage complexity increases
Solution Approach 1:
The patent segments data into standardized tiers with clear definitions of what information belongs at each level. This segmentation simplifies data management by providing a systematic framework for collecting, storing, and retrieving information, reducing the complexity that would otherwise arise from managing heterogeneous data structures with varying levels of detail.
Solution Approach 2:
The patent changes the parameter of data detail level from a continuous spectrum to discrete tiers (first-tier, second-tier, third-tier). This parameter transformation simplifies data management by allowing the system to switch between predefined data completeness levels rather than managing arbitrary amounts of detail, thereby reducing processing and storage complexity while maintaining user experience quality.
Data Source
AI summary
The technology described herein optimally allocates the limited computer storage on the end user device to point of interest data most likely to be used by a map application. The offline point of interest data can include first-tier, second-tier, and third-tier data about different points. The offline data can be selected based on overall usage among all people and also specially tailored for individual users interests.