Information Processing Apparatus for Personalized AR Object Layout
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Solution Overview
Problem
Current room coordination applications in augmented reality do not consider personalized object layouts for users, failing to effectively propose optimal placements based on user attention and object compatibility within a space.
Innovation Solution
An information processing apparatus that acquires space and user information to create attention level and goodness-of-fit maps, allowing for personalized object layout proposals by analyzing user attention and object compatibility in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic layout proposal is implemented without user-specific considerations, then layout efficiency is improved, but personalization and user experience deteriorate
Solution Approach 1:
The system performs preliminary sensing of user information (gaze direction, attention level) and space information before generating layout proposals. This allows the system to pre-adjust parameters based on user state, enabling both efficient automatic proposal and personalized adaptation without real-time computation delays.
Solution Approach 2:
The system dynamically changes layout parameters (position, orientation, scale) based on user attention levels and object compatibility scores. By adjusting these parameters according to sensed user state and spatial relationships, the system achieves personalized layouts while maintaining computational efficiency through parameter optimization rather than complete redesign.
2Measurement precision
If multiple sensors are used to acquire user and space information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system employs sensors with multi-functionality that can detect multiple types of information simultaneously. For example, cameras serve both for space mapping and user gaze detection, while IMU sensors provide both device orientation and user attention direction. This reduces the total number of sensors needed while maintaining high measurement precision.
Solution Approach 2:
The system merges data from multiple sensor sources to achieve accurate user state detection. By combining gaze direction from camera tracking with attention level from pupil dilation and head pose information, the system achieves high precision measurement without requiring separate dedicated sensors for each parameter, thus managing complexity through data fusion.
3Manufacturing precision
If real-time sensing and map creation is performed, then layout proposal accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary creation of attention level maps and goodness-of-fit maps based on initial sensor data before final layout proposal generation. These pre-computed maps serve as input parameters for the optimization algorithm, allowing accurate layout proposals to be generated quickly without performing all computations in real-time.
Solution Approach 2:
The system computes layout proposals for only the most relevant objects and spatial regions identified by the attention level map, rather than processing all objects uniformly. By focusing computational resources on high-priority areas where user attention and object compatibility indicate potential optimal placements, the system achieves high accuracy while reducing overall processing time.
Data Source
AI summary
The information processing apparatus includes a space information acquisition unit that acquires space information based on the space sensing information, an object information acquisition unit that acquires object information, a user information acquisition unit that acquires user information based on the user sensing information, an attention level map creation unit that creates an attention level map showing an attention level of the user for each local area of the space, a goodness-of-fit map creation unit that creates a goodness-of-fit map showing a goodness-of-fit of the object for each local area, and a proposing unit that proposes a disposition of the object in the space on the basis of the attention level map and the goodness-of-fit map.