ML-Curated AR Space Personalization via User Data Analysis
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Solution Overview
Problem
Current mixed reality environments lack personalized customization options, relying on preconfigured settings and failing to adapt dynamically to user preferences and tastes, despite advancements in augmented reality technology.
Innovation Solution
The implementation of machine learning techniques to dynamically customize augmented reality environments by analyzing user data from various sources, such as social media, browsing history, and purchase data, to create personalized and interactive spaces that evolve with user preferences and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If preset environments and preconfigured options are used, then system complexity is reduced and ease of manufacture is improved, but adaptability and user personalization deteriorate
Solution Approach 1:
The system automatically collects user data from multiple sources (social media, browsing history, purchase data) and uses machine learning algorithms to generate personalized environment configurations without requiring manual user input. The AI engine autonomously analyzes user preferences and applies them to customize the augmented reality space, allowing the system to serve itself in the personalization process.
Solution Approach 2:
The machine learning model dynamically adjusts multiple parameters of the augmented reality environment (colors, textures, objects, layout) based on analyzed user preferences. The system continuously modifies these parameters to reflect changing user tastes, transforming static preconfigured environments into dynamic personalized spaces.
2Adaptability or versatility
If machine learning techniques are implemented to dynamically customize environments, then adaptability and user personalization are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system divides the complex personalization task into separate functional modules: data collection from multiple sources, data processing and analysis, machine learning model execution, and environment configuration generation. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity.
Solution Approach 2:
The patent introduces an AI engine as an intermediary layer between raw user data and the augmented reality environment configuration. This intermediary processes and interprets user preferences, translating them into appropriate environment parameters, thereby simplifying the overall system architecture and making it more manageable.
3Ease of operation
If real-time customization is implemented, then user experience quality is improved, but processing speed requirements and energy consumption worsen
Solution Approach 1:
The system performs environment customization at periodic intervals rather than continuously in real-time. The AI engine analyzes user data and updates the augmented reality environment at scheduled times, balancing user experience quality with reduced computational energy consumption compared to continuous real-time processing.
Data Source
AI summary
The present invention contemplates a method of producing a walkabout reality for a user through an augmented reality engine. The augmented reality engine retrieves data associated with user behavioral characteristics and identifies user behavioral characteristics from user patterns of behavior in at least one of a third-party virtual environment and a current physical environment. The augmented reality engine further analyzes the current physical environment to determine one or more customizable elements of the current physical environment and determines a predicted visual preference of the user. The augmented reality engine identifies one or more visual elements associated with the predicted visual preference of the user and renders a virtualized current physical environment within a threshold distance of the user by superimposing at least one of the one or more visual elements associated with the third-party virtual environment onto the one or more determined features and associated feature characteristics.