Automated Vendor Identity Classification from Transaction Receipts
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Solution Overview
Problem
Current methods for determining a vendor's identity from tax receipts are time-consuming, labor-intensive, and inefficient, especially when dealing with large volumes of transactions, and often require manual entry, which can lead to errors and increased risk during tax inspections.
Innovation Solution
A method and system that classify digital images of transaction evidence by extracting descriptive data items, searching for informative data, determining correlated amounts, and applying expense type classification rules to automatically identify the vendor's identity, using techniques like Optical Character Recognition (OCR) and Term Frequency-Inverse Document Frequency (TFIDF) analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry of vendor identity is used, then accuracy of vendor identification can be maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical entry of vendor identity with an automated computer-based system that extracts vendor information from electronic documents using optical character recognition (OCR) and text analysis algorithms. This substitution eliminates manual labor while maintaining identification accuracy through automated pattern recognition and validation rules.
Solution Approach 2:
The system enables self-service by automatically extracting and validating vendor identity information from electronic documents without requiring human intervention. The automated extraction process reads document data, identifies vendor fields, and populates vendor identity information autonomously, freeing employees from manual data entry tasks.
2Ease of operation
If manual entry of vendor identity is used, then flexibility in handling different document formats is maintained, but labor intensity and error risk increase
Solution Approach 1:
The patent implements a universal automated extraction system that can handle multiple electronic document formats (PDF, images, scanned documents) through a single platform. The system uses format-agnostic OCR technology and adaptive parsing rules to extract vendor identity information from diverse document types, eliminating the need for separate manual processing procedures for each format.
Solution Approach 2:
The system replaces manual document handling with automated optical character recognition and text extraction algorithms that can process various document formats without human intervention. This substitution reduces labor intensity while maintaining operational flexibility through programmable adaptability to different document structures.
3Productivity
If automated vendor identification is implemented, then time consumption and labor intensity are reduced, but system complexity increases
Solution Approach 1:
The patent segments the automated vendor identification process into distinct functional modules: document ingestion, OCR text extraction, vendor field identification, data validation, and result output. This modular segmentation manages system complexity by organizing functions into independent, maintainable components while maintaining high processing speed through parallel operation of modules.
Solution Approach 2:
The system introduces an intermediary layer of automated extraction software that bridges the gap between raw electronic documents and vendor identity data. This intermediary automatically translates unstructured document content into structured vendor information, reducing system complexity by handling the transformation process autonomously without requiring complex manual procedures.
4Measurement precision
If large volumes of transactions are processed manually, then accuracy can be maintained through careful review, but time consumption becomes prohibitive
Solution Approach 1:
The patent replaces manual data entry and review processes with automated extraction and validation systems that can process large volumes of transactions simultaneously. The system maintains data accuracy through automated validation rules, cross-referencing, and error detection algorithms that operate at machine speed, enabling high processing volumes without sacrificing precision.
Solution Approach 2:
The system enables continuous automated processing of transaction documents without the interruptions inherent in manual review processes. The automated extraction and validation system operates continuously, processing documents in sequence or parallel, maintaining both high productivity and data accuracy through uninterrupted automated operation with built-in quality checks.
Data Source
AI summary
A system and method for classifying digital images is presented. The method includes extracting a plurality of descriptive data items of a transaction evidence from a digital image indicating a plurality of purchased items; searching in data source for informative data based on the extracted plurality of descriptive data items, wherein the informative data includes a price; determining a correlated amount for each of at least one of the plurality of descriptive data items, wherein the correlated amount determined for one of the descriptive data items defines a paid price for the descriptive data item; determining, based on at least one expense type classification rule, a primary expense type of the transaction evidence, wherein the at least one expense type classification rule is applied to the plurality of descriptive data items and each of the correlated amount; and classifying the digital image based on the primary expense type.