Promissory Image Classification via Object Detection and ML
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Solution Overview
Problem
Current image classification technologies are unable to identify fiduciary promises conveyed through images, which are implicit or explicit visual concepts, and manual review of large volumes of images is impractical and inefficient.
Innovation Solution
A system combining object detection and machine learning, utilizing a convolutional neural network to detect objects in images and a machine learning classifier to determine if these objects represent a fiduciary promise, flagging images for manual review while achieving high recall and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of images is performed to identify fiduciary promises, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system segments the image analysis task into multiple stages: initial automated classification using machine learning models, followed by selective manual review only for images that meet specific confidence thresholds or exhibit ambiguous features. This segmentation allows the majority of clear cases to be processed automatically while preserving human judgment for edge cases.
Solution Approach 2:
The patent introduces an intermediary automated classification system that acts as a mediator between the image set and human reviewers. This intermediary pre-screens images, flags potentially problematic ones, and prioritizes them for review, thereby reducing the overall volume of manual work while maintaining high identification accuracy through human expertise when needed.
2Productivity
If automated image classification is implemented to identify fiduciary promises, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system merges multiple machine learning classification models with different strengths (e.g., image recognition, text analysis, contextual understanding) into a unified automated classification pipeline. This ensemble approach leverages the complementary capabilities of each model to improve overall classification accuracy while maintaining high processing speed through parallel computation.
Solution Approach 2:
The patent implements feedback mechanisms where classification results are continuously evaluated against ground truth data, and model parameters are adjusted based on performance metrics. Misclassified images are fed back into the training process to improve future classification accuracy, creating a self-improving system that maintains high precision while operating at automated speeds.
3Reliability
If comprehensive image analysis is performed to detect all potential fiduciary promises, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary filtering and preprocessing actions before comprehensive analysis, such as removing obviously non-promissory images, extracting only relevant visual features, and organizing images by risk category. This preliminary action reduces the complexity of subsequent analysis while ensuring that no potential fiduciary promises are missed in the filtering process.
Solution Approach 2:
The patent employs dynamic analysis strategies where the depth and scope of image analysis are adjusted based on initial assessment results, image characteristics, and risk profiles. High-risk images receive more comprehensive analysis while low-risk images undergo lighter processing, optimizing the balance between reliability and system complexity through adaptive resource allocation.
Data Source
AI summary
Systems, methods and products for classifying images according to a visual concept where, in one embodiment, a system includes an object detector and a visual concept classifier, the object detector being configured to detect objects depicted in an image and generate a corresponding object data set identifying the objects and containing information associated with each of the objects, the visual concept classifier being configured to examine the object data set generated by the object detector, detect combinations of the information in the object data set that are high-precision indicators of the designated visual concept being contained in the image, generate a classification for the object data set with respect to the designated visual concept, and associate the classification with the image, wherein the classification identifies the image as either containing the designated visual concept or not containing the designated visual concept.