Document Image Processing for Confidence-Based Transaction Matching

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Solution Overview

Problem

Existing transaction processing systems struggle with handling varied sets of transactions without prior knowledge of the context, leading to inefficiencies and potential errors due to the need for manual document analysis and pre-processing.

Innovation Solution

A system utilizing an image processing engine with machine learning models to analyze document images, identify attributes, determine transactions, and associate them with accounts, based on confidence scores, enabling automated and efficient transaction processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual transaction processing is used to handle varied transactions without prior knowledge, then adaptability to different transaction types is improved, but processing efficiency and productivity deteriorate due to time-consuming manual analysis

Engineering Contradiction:

Improveability to handle varied transaction typesVSAvoidtransaction processing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a universal document processing system that can handle multiple transaction types (payments, purchases, refunds, etc.) through a single automated platform. The system uses machine learning models trained on diverse document formats to universally process different transaction types without requiring separate manual processing procedures for each type, thereby maintaining adaptability while dramatically improving productivity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent replaces manual mechanical processing (human analysts physically examining documents) with automated image processing and machine learning algorithms. The system captures document images, extracts text and data using OCR and NLP, and automatically processes transactions through software algorithms, eliminating the time-consuming manual analysis step while maintaining the ability to handle varied transaction types

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual document analysis is performed to ensure accurate transaction processing, then measurement precision and reliability are improved, but processing time and productivity worsen

Engineering Contradiction:

Improvedocument attribute identification accuracyVSAvoiddocument processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual document analysis with automated image processing engines that use OCR (Optical Character Recognition) and NLP (Natural Language Processing) algorithms to extract and validate document attributes. These automated systems achieve high precision in identifying document types, transaction amounts, and other critical data points while processing documents in seconds rather than minutes or hours, thereby improving productivity without sacrificing accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements feedback mechanisms where the system continuously learns from processed documents and adjusts its processing algorithms. The machine learning models are trained on historical document data and refine their accuracy over time, ensuring that automated attribute identification maintains high precision. The system also includes validation feedback loops that verify extracted data against business rules and flag anomalies for review, maintaining reliability while enabling rapid automated processing

Inventive Principle:

Principle #23Feedback

3Reliability

If pre-analysis of documents is required to process transactions in ordered manner, then transaction processing reliability is improved, but system complexity and ease of operation worsen

Engineering Contradiction:

Improvetransaction processing accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent performs preliminary analysis of documents automatically upon receipt, extracting and validating all necessary transaction data before the main processing workflow begins. The system pre-identifies document types, pre-extracts key attributes, and pre-validates data completeness using machine learning models. This preliminary automated analysis ensures reliability by catching errors early while keeping the system simple, as the preprocessing is handled automatically without requiring complex manual intervention or sophisticated orchestration layers

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12505413B2Enhanced image transaction processing solution and architecture

Publication Date: 2025.12.23 FIDELITY INFORMATION SERVICES LLC

AI summary

Systems and methods, and computer readable media for image transaction processing are disclosed. The method receives an input of images of documents. The method may then analyze the input using an image processing engine to determine attributes associated with the images of the documents and identify an account linked to the attributes and a transaction associated with the account. The method may also evaluate confidence level of association links between the transaction and the account based on confidence scores of the attributes that may identify a type of the attribute. The method may use the transaction and account to split the images of documents into sets of images of documents with each set of images with confidence level of an association link between the transaction and the account associated with them being greater than a threshold value.