Mixed Reality Object Dimension Evaluation
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Solution Overview
Problem
Individuals face challenges in evaluating the physical dimensions of objects in relation to their actual fitment in target areas, leading to evaluation errors, increased returns, and unsatisfied user experiences due to reliance on manual intuition.
Innovation Solution
An AI, MR, and IoT-based system that uses machine learning to analyze user preferences and contextual measurements, providing recommendations for object placement and orientation within a target area, considering line-of-sight, location, and historical preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intuition is used to evaluate object dimensions and fitment, then the process is simple and requires no complex tools, but evaluation accuracy is poor leading to errors and returns
Solution Approach 1:
The patent creates a digital twin or virtual representation of the physical space and objects within it. This digital model allows for accurate measurement and fitment evaluation without requiring complex physical measurement tools. The virtual copy enables precise dimensional analysis while maintaining simplicity of use through software-based evaluation.
Solution Approach 2:
The patent replaces manual physical measurement and intuition-based evaluation with automated computational methods. Machine learning algorithms and image processing techniques substitute for human manual measurement, providing accurate dimensional evaluation without requiring complex physical measurement devices or tools.
2Measurement precision
If complex measurement tools and systems are deployed to improve measurement accuracy, then evaluation precision improves, but ease of operation decreases requiring specialized training
Solution Approach 1:
The system performs automated measurements and evaluations without requiring user intervention or specialized knowledge. The machine learning model automatically identifies objects, measures dimensions, and evaluates fitment based on images or sensor data, making the process as simple as capturing visual data while maintaining high measurement precision.
Solution Approach 2:
The system pre-processes and analyzes spatial data, object dimensions, and fitment criteria before the user needs the evaluation. By performing measurements and calculations in advance through automated processing, the system delivers ready-to-use results without requiring the user to perform complex measurement tasks or interpret technical data.
3Productivity
If traditional evaluation methods are used, then the system remains simple, but time consumption increases due to repeated trials and errors
Solution Approach 1:
The system performs preliminary analysis of object dimensions, target area characteristics, and potential fitment scenarios before final evaluation. By pre-processing data and running simulations in advance, the system eliminates the need for repeated physical trials and errors, significantly reducing the time required for evaluation while maintaining simplicity.
Solution Approach 2:
The system provides automated feedback on fitment evaluation results, indicating whether objects will fit properly and suggesting adjustments if needed. This immediate feedback mechanism eliminates the need for repeated manual trials and errors by guiding users directly to successful configurations, thereby improving productivity and reducing time loss.
Data Source
AI summary
A processor may analyze a target area. The processor may identify, from the analyzing, one or more objects in the target area. The processor may evaluate each of the one or more objects in the target area. Evaluating each of the one or more objects may include measuring each of the one or more objects and determining a purpose for each of the one or more objects. The processor may generate, based on the evaluating, a placement for each of the one or more objects.