Automated Image Labeling for Single-Image Currency Detection
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Solution Overview
Problem
Existing machine learning models for object identification, particularly for detecting media of exchange in images, are computationally intensive and require multiple images for each medium of exchange, leading to inefficiencies in processing and storage.
Innovation Solution
The techniques involve capturing a single image that includes multiple media of exchange, communicating image information to a remote computing device running a machine learning model, and using the model to output a quantity and confidence score for the media of exchange, which can then be used to establish a communication channel for verification if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate images are required for each medium of exchange in traditional object identification models, then the model can accurately identify each medium of exchange, but the computational intensity and processing time increase significantly
Solution Approach 1:
The patent merges multiple separate image processing tasks into a single image analysis process. The machine learning model processes one image containing multiple media of exchange simultaneously, rather than requiring separate images for each medium. This combining approach maintains identification accuracy while dramatically improving processing efficiency and reducing computational overhead.
Solution Approach 2:
The machine learning model is designed with multi-functionality to handle various types of media of exchange (different currencies, denominations, and formats) within a single processing framework. The model can identify and quantify multiple different media of exchange types in one image, eliminating the need for separate specialized models or image sets for each medium.
2Reliability
If multiple separate images are stored for each medium of exchange, then complete documentation is maintained, but storage requirements increase
Solution Approach 1:
The system combines multiple documentation requirements into a single image file. Instead of storing separate image files for each medium of exchange, the patent stores one image that captures all media of exchange present, significantly reducing storage space while maintaining complete documentation through the single comprehensive image and its associated metadata.
3Loss of information
If multiple images are transmitted over the network for each medium of exchange, then all necessary data is sent, but network bandwidth consumption increases
Solution Approach 1:
The patent merges multiple data transmission operations into a single network transmission. Instead of sending separate images for each medium of exchange, the system transmits one image containing all media of exchange along with associated metadata. This approach maintains data completeness while reducing network bandwidth consumption and transmission time.
4Measurement precision
If traditional object identification models process multiple separate images, then each medium of exchange is thoroughly analyzed, but the computational intensity increases
Solution Approach 1:
The patent combines multiple separate analysis operations into a single computational process. The machine learning model performs thorough analysis of all media of exchange in one image simultaneously, rather than processing multiple separate images through separate computational pipelines. This merging reduces computational energy consumption while maintaining analysis accuracy through the unified processing approach.
Data Source
AI summary
The techniques may include detecting a repository value change for an account. In addition, the techniques may include receiving image information representing an image from a computing device that is associated with the account, where the image includes a depiction of the media of exchange. The techniques may include generating a feature vector for the image, where the feature vector is generated based at least in part on the image information, and where the feature vector may include numeric properties of the image. Moreover, the techniques may include associating the feature vector and the repository value change, thereby generating a labeled training datum. Also, the techniques may include adding the labeled training datum to a set of labeled training data. Further, the techniques may include using the set of labeled training data to train a machine learning model that is configured to receive as input the image information and output a quantity that corresponds to the media of exchange.