Document Source Identification With Deterministic and Probabilistic Matching
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Solution Overview
Problem
Businesses face inefficiencies and errors in categorizing and extracting data from customer documents received via various mediums, and existing systems require manual oversight to associate extracted data with existing customers, especially for large customer bases.
Innovation Solution
A document source identification system using machine learning to categorize and extract data from documents, applying deterministic and probabilistic searches to link documents to existing user accounts, with features like deep learning for document type identification and optical character recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual categorization and data extraction from customer documents is performed, then accuracy can be maintained through human oversight, but productivity decreases due to tedious and time-consuming manual processes
Solution Approach 1:
The system enables self-service automation where the computerized system independently performs document categorization, data extraction, and customer identification without requiring manual human intervention. The system automatically processes documents through multiple stages including categorization, data extraction, normalization, deterministic searching, and probabilistic matching, thereby maintaining accuracy while significantly improving productivity.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computerized systems. Specifically, it substitutes human manual categorization and data extraction with machine learning models and optical character recognition systems, replacing manual customer identification with automated deterministic and probabilistic searching algorithms, thereby eliminating the trade-off between manual accuracy and automated speed.
2Productivity
If computerized systems are used to identify and sort customer document types, then productivity increases through automated processing, but reliability decreases due to limitations in efficiency and accuracy without manual oversight
Solution Approach 1:
The system segments the document processing workflow into distinct automated stages: document categorization using machine learning, data extraction using optical character recognition, data normalization, deterministic identification searching, and probabilistic identification searching. Each stage is independently optimized and automated, collectively achieving both high productivity and high reliability without requiring manual intervention at any point.
Solution Approach 2:
The system introduces intermediate processing steps between document receipt and customer identification, including machine learning-based categorization, optical character recognition for data extraction, and data normalization. These intermediaries enhance the reliability of automated processing by adding layers of intelligent analysis and validation, thereby maintaining accuracy while preserving automated productivity.
3Measurement precision
If deterministic ID search is performed on all user account data entries, then complete accuracy in matching documents to customers can be achieved, but processing time increases significantly
Solution Approach 1:
The system performs preliminary filtering of user account data entries based on extracted document data before conducting the deterministic identification search. By pre-processing and narrowing down the search space to only relevant user accounts, the system maintains complete matching accuracy through deterministic searching while significantly reducing processing time by avoiding unnecessary comparisons with all user accounts.
4Reliability
If manual oversight is required to associate sorted documents with existing customers, then accuracy in customer identification can be maintained, but device complexity increases due to additional manual processes
Solution Approach 1:
The system implements a universal automated framework that handles multiple document types, extraction methods, and matching strategies within a single integrated system. The multi-functional system performs categorization, extraction, normalization, deterministic searching, and probabilistic matching automatically, eliminating the need for separate manual oversight processes and reducing overall system complexity while maintaining high identification accuracy.
Data Source
AI summary
A document source identification system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to cause the system to receive uploaded document(s) having at least one extractable data entry. The system may categorize the document, and extract at least one data entry from the document. The system may normalize each extracted data entry and execute a deterministic ID search to determine that the normalized data entry matches zero, one, or more than one account data entries associated with user accounts. Responsive to an exact match, the system may link the uploaded document to a user account associated with the matching data entry. Responsive to zero or multiple matches, the system may execute a probabilistic ID search identifying a highest ranked user account data entry and link the document to a user account associated with the highest ranked user account data entry.