AR Virtual Interest Segmentation for Contextual Content
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Solution Overview
Problem
Virtual and augmented reality systems often struggle to display relevant content to users, as they typically overwhelm users with a vast array of information, making it difficult to tailor content that is personally pertinent, leading to distractions when not relevant.
Innovation Solution
The system performs semantic and interest segmentations on images captured by AR devices to identify objects and determine their personal significance to the user, creating and displaying virtual content based on user interest, using historical eye-tracking data, GPS data, and user-submitted information, and optionally with user permission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the AR system displays a vast array of content to users, then the variety of information available is improved, but the content becomes less personally pertinent and causes distractions
Solution Approach 1:
The patent segments content into two categories: generic content and personalized content. The system performs semantic segmentation to identify objects in the environment and interest segmentation to determine personal significance. This segmentation allows the system to filter and prioritize content based on user interest, resolving the contradiction between content variety and personal pertinence by presenting a segmented, personalized subset of the vast available content.
Solution Approach 2:
The patent applies local quality by making different parts of the content delivery system have different functions. The system analyzes user-specific data (eye-tracking, GPS, interactions) to assign different levels of personalization to different content items. Content is locally optimized for each user based on their specific interests and context, rather than applying a uniform approach to all content.
2Loss of information
If the AR system tailors content to be personally pertinent to users, then the relevance of content is improved, but the system complexity increases due to multiple segmentation processes
Solution Approach 1:
The patent divides the content personalization process into two distinct segmentation stages: semantic segmentation (identifying objects) and interest segmentation (determining personal significance). This segmentation of the processing workflow allows the system to manage complexity by breaking down the complex task of personalization into manageable, modular steps that can be executed sequentially.
Solution Approach 2:
The patent performs preliminary action by pre-processing user data (eye-tracking history, GPS locations, interaction patterns) to create user profiles and interest models before content delivery. This preliminary analysis of user behavior patterns allows the system to quickly determine personal significance without complex real-time processing during content delivery, reducing operational complexity.
3Measurement precision
If the AR system collects and processes user data for interest segmentation, then the personalization accuracy is improved, but the data processing requirements and computational load increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing user data (eye-tracking, GPS, interactions) in structured formats during normal usage. This preliminary organization of data allows the interest segmentation process to query pre-processed information rather than analyzing raw data in real-time, significantly reducing computational load during content delivery while maintaining high personalization accuracy.
Solution Approach 2:
The patent creates simplified copies or representations of user data in the form of user profiles and interest models. Instead of processing all raw user data during content delivery, the system uses these compressed representations (copies) that capture essential patterns, reducing computational requirements while preserving personalization accuracy.
Data Source
AI summary
Systems and methods for virtual interest segmentation may include (1) performing a semantic segmentation of an image of a user's environment, captured by an artificial reality (AR) device being worn by the user, to identify objects within the user's environment, (2) in addition to performing the semantic segmentation, performing an interest segmentation of the image to determine a personal interest that the user may have in a particular object identified via the semantic segmentation, (3) creating virtual content relating to the particular object based on the user's personal interest in the particular object, and (4) displaying the virtual content within a display element of the AR device. Various other methods, systems, and computer-readable media are also disclosed.