Concurrent Currency Identification With Confidence-Based Verification
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Solution Overview
Problem
Machine learning models for identifying multiple media of exchange in images are computationally intensive and require multiple image transmissions, which can be inefficient and resource-heavy, particularly for visually impaired individuals who rely on others for currency valuation.
Innovation Solution
A system that captures an image, generates a feature vector, and uses a machine learning model to determine a quantity and confidence score, compares the score to evaluation criteria, and initiates a communication channel for verification, while reducing image transmissions and improving accuracy through automated training data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate images are required for each medium of exchange to be identified by machine learning models, then object identification accuracy can be maintained, but network transmission overhead and computational resources increase significantly
Solution Approach 1:
The patent combines multiple separate images containing different media of exchange into a single composite image for processing. The machine learning model processes this single image to identify all media of exchange simultaneously, eliminating the need for multiple separate image transmissions while maintaining identification accuracy through confidence score evaluation for each detected object
2Loss of energy
If machine learning models process multiple media of exchange concurrently in a single image, then network transmissions are reduced, but computational complexity increases
Solution Approach 1:
The patent replaces complex computational processing with a confidence score-based filtering mechanism. The machine learning model processes all objects in the single image and generates confidence scores for each detection. Objects with confidence scores above a threshold are accepted, while those below trigger a secondary verification process, significantly reducing computational complexity compared to processing multiple separate images
3Measurement precision
If machine learning models require multiple image transmissions for accurate identification, then measurement precision can be maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by generating confidence scores for all detected objects in the single image before final verification. High-confidence detections are immediately accepted, while only low-confidence detections require additional verification steps. This preliminary filtering approach maintains accuracy while significantly reducing overall processing time compared to transmitting multiple images
4Reliability
If visual verification by other users is implemented for currency identification, then reliability can be enhanced, but device complexity and processing time increase
Solution Approach 1:
The patent implements a feedback mechanism where confidence scores from the machine learning model determine whether visual verification is needed. Objects with low confidence scores trigger a feedback loop that presents the image to other users for verification. The verification results are then fed back to confirm or correct the initial detection, enhancing reliability while avoiding unnecessary verification for high-confidence detections
Data Source
AI summary
The techniques may include capturing an image that includes a depiction of the media of exchange. In addition, the techniques may include communicating image information representing the image to a remote computing device that is configured to execute a machine learning model that receives as input the image information and outputs a quantity that corresponds to the media of exchange. The techniques may include receiving a quantity that corresponds to the media of exchange and a confidence score that includes a probability that the media of exchange in the image corresponds to the quantity. Moreover, the techniques may include comparing the confidence score with respect to one or more evaluation criteria. Also, the techniques may include in response to the comparing, presenting, via a user interface, an option for establishing a communication channel. Further, the techniques may include in response to receiving input from the user interface, establishing the communication channel.