Mobile Document Image Validation Using Native ML Processing

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Solution Overview

Problem

Existing mobile operations software programs face issues with slow processing times and crashes due to reliance on third-party libraries for image capture and validation of documents like checks, struggling with brightness, contrast, and rectangular format, and lacking effective validation of document security features and user identity.

Innovation Solution

Implementing a mobile application that leverages native image processing software (e.g., Apple VisionKit®) for client-side image processing and validation, using machine-learning models to identify documents and attributes, and validate dimensions, brightness, and contrast, without relying on third-party libraries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If third-party libraries are used for image capture and validation, then functionality is provided, but processing speed decreases and system stability worsens

Engineering Contradiction:

Improveprocessing speedVSAvoidsystem stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent extracts the image processing functionality from third-party libraries and implements it using native device capabilities and machine learning models. This removes the problematic intermediate layer that caused performance degradation and instability, allowing direct access to hardware resources while maintaining reliability through native integration.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent replaces the mechanical software layer of third-party libraries with a more efficient system combining native image processing APIs and machine learning models. This substitution eliminates the overhead and compatibility issues inherent in third-party software while leveraging the full capabilities of the device's native processing resources.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If third-party libraries are used for image processing, then image capture functionality is provided, but processing time increases

Engineering Contradiction:

Improveprocessing timeVSAvoidprocessing efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The patent performs preliminary actions by pre-loading and initializing machine learning models on the device before image capture. This preparation ensures that when images are captured, processing can begin immediately without the overhead of initializing third-party libraries, significantly reducing processing time while improving overall efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the inefficient third-party library processing mechanism with a streamlined system using native image processing APIs combined with pre-initialized machine learning models. This substitution eliminates the performance overhead of external libraries while maintaining comprehensive image processing capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If detailed instructions are provided to users, then validation accuracy improves, but user convenience decreases

Engineering Contradiction:

Improvevalidation accuracyVSAvoiduser convenience

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent implements self-service functionality where the machine learning models automatically perform validation of document authenticity, orientation, and quality metrics without requiring user intervention. The system independently assesses image quality, detects document features, and provides feedback, maintaining high validation accuracy while preserving user convenience by eliminating manual instruction following.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent incorporates feedback mechanisms where the machine learning model provides real-time guidance to users based on captured image quality. Instead of requiring users to follow detailed instructions, the system analyzes the captured image and provides targeted feedback on specific improvements needed, achieving high validation accuracy while maintaining ease of operation through intuitive, context-aware guidance.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260051194A1Machine-learning models for image processing

Publication Date: 2026.02.19 CITIBANK N A

AI summary

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.