Client-Side Image Processing for Mobile Check Deposit Quality
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Solution Overview
Problem
Existing mobile check deposit solutions face challenges with inconsistent image quality due to low light conditions and shaky hands, fraud detection inaccuracies, and reliance on backend servers for processing, leading to delays and resource consumption.
Innovation Solution
A computing system that performs client-side validation and processing using machine-learning models on mobile devices to enhance image quality, detect fraud, and validate user identity, reducing server load and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image processing and fraud detection are performed on backend servers, then processing power and resources are centralized, but processing delays increase and server resource consumption increases
Solution Approach 1:
The patent divides the image processing system into two segments: a client-side machine learning model that performs initial image quality assessment and feature extraction on mobile devices, and a backend server that performs final fraud detection and validation. This segmentation allows preliminary processing to occur locally without network delays, while maintaining centralized security validation.
Solution Approach 2:
The client-side machine learning model performs preliminary image processing, quality assessment, and feature extraction before images are transmitted to the backend server. This preliminary action filters out low-quality images and extracts relevant features in advance, reducing the computational burden and processing time required by the backend server.
2Measurement precision
If detailed instructions are provided to users for check deposition, then processing accuracy may improve, but user convenience decreases
Solution Approach 1:
The client-side machine learning model provides real-time feedback to users about image quality metrics such as lighting conditions, focus, and composition. The system guides users to retake images that do not meet quality thresholds, creating a closed-loop feedback system that improves image consistency without requiring users to memorize detailed instructions.
Solution Approach 2:
The machine learning model automatically assesses image quality and provides corrective guidance, enabling users to self-correct poor images without needing expert knowledge of proper check deposition techniques. The system adapts to user actions and provides context-specific feedback, making the process intuitive rather than instruction-heavy.
3Productivity
If remote deposit capture is implemented, then latency and travel time are reduced, but verification of user identity and check authenticity becomes more challenging
Solution Approach 1:
The client-side machine learning model acts as an intermediary verification layer between the user and the backend server. It performs initial validation of check authenticity features and user device characteristics before images reach the server, providing an additional security checkpoint that maintains verification reliability while enabling remote processing.
4Ease of operation
If mobile devices are used for check capture, then accessibility and convenience improve, but image quality consistency deteriorates due to varying environmental conditions and device capabilities
Solution Approach 1:
The machine learning model dynamically adjusts processing parameters based on the specific mobile device being used and its camera capabilities. It adapts to varying lighting conditions, sensor qualities, and hardware characteristics by modifying image processing parameters in real-time, thereby maintaining consistent output quality across diverse device types and environmental conditions.
Data Source
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AI summary
Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining a video feed generated at a camera device. The method may include executing an object recognition engine of a machine-learning architecture using the video feed as an input, the object recognition engine trained to detect a type of document in image data of the video feed. The method may include generating a halt instruction for halting the video feed in response to detecting a document of the type of document in the image data of the video feed. The method may include selecting one or more frames of a plurality of frames of the video feed having the image data satisfy a quality selection threshold for the document according to one or more image characteristics.