Lens Quality Estimation Using Learned Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interchangeable lenses require periodic maintenance, but determining the necessity of maintenance is challenging due to their complex and precise nature, and existing methods may not accurately reflect actual lens quality due to environmental factors like temperature and humidity.
Innovation Solution
An information processing apparatus that uses a learning model to estimate lens performance by acquiring and processing use state and performance data, including environmental conditions, to evaluate lens quality accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform durability information is applied to all interchangeable lenses of the same model, then the evaluation process is simplified, but the accuracy of quality assessment deteriorates due to environmental variations
Solution Approach 1:
The patent segments the evaluation process into two distinct phases: a learning phase where the system collects and analyzes actual usage data from multiple lenses under various environmental conditions, and an evaluation phase where the trained model assesses individual lenses. This segmentation allows the system to first build accurate environmental correlations through data collection, then apply this knowledge to individual assessments, thereby maintaining both operational simplicity and assessment accuracy.
2Measurement precision
If AI technology is applied to analyze complex lens elements, then the ability to evaluate lens performance is improved, but the complexity of the evaluation system increases
Solution Approach 1:
The patent implements a self-service mechanism where the evaluation system automatically collects usage data, trains its own assessment model, and performs quality evaluations without requiring external expert intervention. The system autonomously learns from accumulated data and continuously improves its evaluation capabilities, thereby achieving high measurement precision while keeping the operational complexity manageable through automation.
Solution Approach 2:
The system incorporates feedback mechanisms where actual lens performance data and environmental conditions are continuously collected and fed back into the learning model. This feedback loop allows the system to refine its evaluation criteria based on real-world performance, improving accuracy over time while the automated feedback processing keeps system complexity controlled through algorithmic rather than manual processes.
3Measurement precision
If detailed usage data and environmental information are collected for each lens, then the accuracy of maintenance timing determination is improved, but the data processing workload increases
Solution Approach 1:
The patent performs preliminary action by collecting and processing usage data from multiple lenses during a learning phase before actual individual lens evaluations are needed. This advance data collection and model training creates a ready-to-use assessment framework, so that when individual lens evaluations are required, the system can quickly apply the pre-trained model without performing lengthy data processing, thereby reducing the time loss during actual maintenance determination.
Data Source
AI summary
An information processing apparatus that can estimate, for example, performance of the lens by using a learning model and easily evaluate a quality of the lens. The information processing apparatus comprises: a first acquisition unit configured to acquire first information in which a use state of a lens unit having at least one optical element after shipment is recorded; a second acquisition unit configured to acquire second information in which lens performance or standard of the lens unit or a lens unit different from the lens unit is recorded in advance; and a first processing unit configured to use the first information and the second information as input data of a predetermined learning model, and generate a learned model in which parameters in the learning model are adjusted.