Workorder Evidence Validation via Object Detection
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Solution Overview
Problem
Large organizations face challenges in efficiently validating workorder completion evidence, as manual processing is time-consuming and prone to missing falsified evidence, especially when dealing with numerous contractors and thousands of workorders daily, leading to potential incorrect payment approvals and safety issues.
Innovation Solution
A computer-implemented method and system that classifies workorder evidence using image analysis, involving object detection algorithms and deep learning models to validate images against stored metadata and previous images, detecting duplicates and falsified evidence, thereby automating the validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of workorder evidence is used, then flexibility and judgment can be applied, but processing time increases and falsified evidence is difficult to detect
Solution Approach 1:
The patent replaces manual mechanical processing of evidence with an automated computer vision system. The system uses deep learning models (such as CNNs) to automatically analyze images, detect objects, verify work completion, and identify falsified evidence, eliminating the need for manual review while improving both speed and detection accuracy.
Solution Approach 2:
The system enables self-service validation where the workorder evidence automatically verifies itself against stored metadata and previous images. The automated system independently performs validation without requiring human intervention, comparing images against workorder requirements and detecting duplicates or falsifications through algorithmic analysis.
2Productivity
If manual review of evidence is performed, then contextual understanding can be applied, but the volume of workorders that can be processed is limited
Solution Approach 1:
The patent replaces manual review processes with an automated computer-based system that processes large volumes of workorders. The system uses image processing algorithms, object detection models, and database comparisons to automatically validate evidence, enabling high-volume processing that would be impossible manually while managing complexity through structured computational approaches.
Solution Approach 2:
The validation process is segmented into distinct automated stages: image extraction, object detection, metadata comparison, duplicate detection, and validation decision. Each stage handles a specific aspect of verification independently, allowing the system to process large volumes of workorders through modular computational tasks rather than requiring complex manual analysis.
3Speed
If automated image analysis is implemented, then processing speed increases, but detection of subtle falsifications becomes more challenging
Solution Approach 1:
The patent employs advanced deep learning models and computer vision algorithms that can rapidly analyze images while maintaining high detection precision. The system uses trained neural networks to recognize patterns, anomalies, and subtle inconsistencies in images, enabling both fast processing and accurate detection of falsified evidence through sophisticated computational analysis.
Solution Approach 2:
The system incorporates feedback mechanisms where detected objects and features are continuously compared against workorder metadata and previous validation results. This feedback loop allows the system to refine its detection accuracy by comparing current images against established patterns and workorder requirements, maintaining precision while processing at high speed through iterative validation.
Data Source
AI summary
The present disclosure relates to computer implemented methods and systems for processing of workorder evidence. The workorder has associated objects of interest and metadata describing at least one workorder attribute. Workorder evidence to be processed comprises an image. The image is validated to assess whether the image is associated with the workorder. Validation comprises one or both of comparing image attributes to workorder attributes to detect a match, and comparing the image to previous images to ensure that the image is not a match for a previous image. The system or method detects whether an object of interest is depicted in the image using an object detection algorithm. The workorder evidence is classified depending on whether the image is detected as valid and the object or interest is detected in the image.