AR Content Display via Object Instance Identification
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Solution Overview
Problem
Augmented reality systems face challenges in providing relevant and personalized content to users by distinguishing specific instances of mass-produced objects, leading to user overload and poor experience due to the inability to leverage unique object instances effectively.
Innovation Solution
A system that uses object detection and unique identifier matching to generate and display specific AR content by obtaining identifying information through implicit and explicit signals, such as geolocation, visual features, and sensor data, allowing users to access customized information for owned or permitted objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If AR content is displayed for all detected objects, then comprehensive information coverage is achieved, but user overload and poor experience occur due to inability to distinguish specific instances
Solution Approach 1:
The system transitions from uniform AR content display to localized content based on object instance identification. By detecting unique identifiers (QR codes, NFC tags, RFID labels) on specific objects and comparing them against stored object records, the system provides customized AR content tailored to each identified object instance, thereby improving user experience while maintaining comprehensive information coverage.
Solution Approach 2:
The patent replaces manual object identification mechanisms with automated computer vision and sensor-based detection systems. The AR device captures images and sensor data, processes them through object detection models, and automatically identifies objects based on visual features and implicit signals, eliminating the need for manual object distinction and reducing user cognitive load.
2Measurement precision
If implicit signals are used for object identification, then unique object instances can be distinguished, but system complexity increases due to multiple detection methods
Solution Approach 1:
The system merges multiple object detection approaches into a unified framework. Explicit signals (QR codes, NFC tags) and implicit signals (visual features, spatial relationships, contextual data) are processed together through a single object detection model. The model integrates results from both signal types to identify object instances, reducing overall system complexity while maintaining high distinction accuracy.
Solution Approach 2:
The object detection model is designed to handle multiple types of identifying signals universally. A single model architecture processes both explicit artificial markers and implicit environmental cues, eliminating the need for separate detection systems for each signal type. This multi-functional approach simplifies the overall detection infrastructure while enabling precise object instance differentiation.
Data Source
AI summary
A computer-implemented is disclosed. The method includes: obtaining identifying information for an object, the object being in a field of view of an AR device, wherein the identifying information comprises implicit signals representing contextual data associated with the object; determining that the object is associated with a first object record based on comparing the identifying information with stored identifiers associated with the first object record; and responsive to the determination that the object is associated with the first object record, presenting, via the AR device, AR content that is specific to the first object record.