AR Overlay Data Retrieval via Context-Aware Segmentation
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Solution Overview
Problem
Augmented reality (AR) devices face challenges in dynamically adapting overlay data display based on contextual settings, such as user identity and location, as existing systems often rely on local storage and lack efficient mechanisms for retrieving and updating data from remote databases in real-time.
Innovation Solution
The implementation of a system that uses a non-transitory computer-readable medium with machine-readable instructions to receive images, detect objects, determine contextual settings, and query local or remote databases for overlay data, sending requests and receiving data via network connections to visually associate relevant information with objects on a display, allowing for seamless transitions between different user contexts and data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overlay data is stored locally in the AR device, then data retrieval is fast and reliable, but the device requires extensive local storage and cannot adapt to changing contextual settings efficiently
Solution Approach 1:
The data storage system is segmented into local storage (for frequently accessed data) and remote storage (for context-specific data). The AR device maintains a local database for reliable fast access while partitioning contextual data across multiple remote databases, allowing the system to balance reliability and adaptability by querying both local and remote sources based on needs.
Solution Approach 2:
The system performs preliminary actions by pre-fetching and caching contextual data from remote databases into local storage before it is actually needed. When contextual settings change, the system proactively retrieves relevant overlay data in advance, reducing latency while maintaining the ability to adapt to different contexts.
2Adaptability or versatility
If the AR device queries remote databases for overlay data, then contextual adaptability is improved, but network dependency increases and real-time retrieval may be delayed
Solution Approach 1:
The system implements local quality by maintaining a local cache of overlay data that is specific to the device or user context. Frequently accessed or critical overlay data is stored locally to ensure fast retrieval, while less critical or highly context-specific data remains in remote databases. This creates a hierarchy where local storage provides speed and remote storage provides comprehensive adaptability.
Solution Approach 2:
The system performs preliminary data retrieval by proactively fetching overlay data from remote databases into local storage before it is needed, based on predicted contextual changes or usage patterns. This pre-caching mechanism reduces the impact of network latency by having data ready locally when contextual settings change.
3Reliability
If the system maintains extensive local storage for all possible overlay data, then data availability is ensured, but device complexity and storage requirements increase
Solution Approach 1:
The storage system is segmented into essential local storage (for critical and frequently accessed overlay data) and supplementary remote storage (for comprehensive but less frequently accessed data). This segmentation ensures that the device maintains data availability for core functionality while offloading extensive storage requirements to remote databases, reducing device complexity.
Solution Approach 2:
The system implements a universal data access architecture that can retrieve overlay data from multiple sources (local storage, remote databases, cloud services) depending on the specific needs of each contextual situation. This multi-functional approach allows the same system to serve both fast local retrieval and comprehensive remote retrieval without requiring separate dedicated storage systems.
4Measurement precision
If the AR device switches between different user contexts, then user-specific overlay data accuracy is improved, but data swapping time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-loading and caching overlay data for multiple anticipated user contexts into local storage before context switching occurs. When a user context changes, the system can quickly retrieve the appropriate cached data locally, minimizing context switching time while maintaining accurate user-specific overlay data.
Solution Approach 2:
The system implements feedback mechanisms that monitor user context changes and usage patterns, automatically adjusting which overlay data is cached locally for each user context. This feedback-driven approach optimizes the balance between data accuracy and switching speed by learning from actual usage patterns and pre-caching data that is most likely to be needed next.
Data Source
AI summary
An example of a non-transitory computer-readable medium storing machine-readable instructions. The instructions may cause a controller to receive an image and detect an object in the image. Based on a contextual setting of the electronic device, overlay data may be retrieved from a database or a remote electronic device to be visually associated with the object on a display.