AR Interface Overlay for Personalized Activity Prediction
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Solution Overview
Problem
Current computing devices lack the ability to automatically and personally analyze user activity and predict future activities using augmented reality, limiting their effectiveness in enhancing human experiences such as organization, navigation, and education.
Innovation Solution
A system that accesses an individual's personalized electronic centralized history to determine frequent activities, locate the user, and generate an augmented reality user interface on a computing device, which can be transmitted to wearable technology, using machine learning and artificial intelligence algorithms to predict future activities and display relevant information like trending prices and user reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If augmented reality user interface is generated and displayed on computing device, then user receives personalized and timely information about activities, but device complexity increases due to integration of machine learning algorithms, electronic history database, and AR display components
Solution Approach 1:
The system is divided into distinct functional modules: electronic history database module, machine learning analysis module, location tracking module, and AR interface generation module. Each module handles specific tasks independently, allowing the complex system to be managed through modular components that can be developed, tested, and maintained separately while working together to provide personalized AR information.
2Loss of information
If machine learning and artificial intelligence algorithms are used to predict future activities and analyze user behavior, then information personalization and timeliness improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by continuously training machine learning models on user electronic history data in the background, building predictive models of user behavior patterns before they are needed. This allows the system to quickly generate personalized predictions and AR interface updates in real-time without performing complex analysis from scratch during user interactions.
Solution Approach 2:
The machine learning algorithms operate continuously in the background, constantly analyzing new electronic history data as it is generated and updating predictive models without interrupting user interactions. This continuous learning process ensures that personalization improves over time while maintaining real-time responsiveness to user needs.
3Loss of information
If augmented reality overlay is displayed on physical objects, then user interaction information is enhanced, but visual distraction from physical barriers may occur
Solution Approach 1:
The AR overlay is applied selectively to specific regions and objects based on user location, activity context, and relevance to current tasks. Rather than uniformly overlaying information across the entire field of view, the system concentrates AR elements on particular physical objects or areas where they provide maximum value, such as overlaying product information on specific items or navigation cues on relevant pathways.
Data Source
AI summary
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises determining user activity based on a personalized electronic history; generating a user interface that overlays on a physical object that denotes user interaction on the physical object based, at least in part, on the determined user activity and user location; and displaying the generated user interface that overlays on the physical object on the user interface on a computing device.