Machine Learning Document Input for Billing Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face inefficiencies in processing and submitting payments from varied billing statements, as they need to manually cross-reference and enter information from different vendor formats, which is time-consuming and prone to errors.
Innovation Solution
A machine-assisted document input system that receives an image of a billing statement, generates a transcript, identifies the vendor, retrieves a vendor-specific or vendor-agnostic machine learning model, associates text groups with billing fields, and extracts relevant data using pattern matching algorithms and regular expressions, then transmits the extracted data to a user device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cross-referencing and data entry is used to process billing statements, then users can accurately extract information from varied vendor formats, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the mechanical manual process of cross-referencing and data entry with an automated optical character recognition (OCR) system combined with machine learning models. The system automatically captures billing statement images, extracts text through OCR, identifies vendor formats using trained models, and populates payment portal fields without human intervention, thereby eliminating the time-consuming manual workflow while maintaining extraction accuracy through intelligent algorithms.
Solution Approach 2:
The system enables self-service by allowing users to simply capture an image of their billing statement and automatically receive extracted data populated in the payment portal. The machine learning models autonomously perform vendor identification, field mapping, and data extraction without requiring users to manually cross-reference documents or understand complex processing steps, making the service effortless and instantaneous.
2Quantity of substance
If manual data entry from varied vendor formats is required, then all billing information can be captured, but error rates increase due to format variations
Solution Approach 1:
The system adapts to different vendor formats by dynamically changing processing parameters based on detected document characteristics. The machine learning models identify vendor-specific formatting patterns and adjust field mapping strategies accordingly, allowing the system to maintain high accuracy across diverse billing statement formats from different vendors without manual intervention.
Solution Approach 2:
The system performs preliminary vendor identification and format classification before data extraction begins. By pre-training machine learning models on various vendor formats and performing initial document analysis, the system prepares the appropriate extraction strategy in advance, ensuring accurate and complete data capture from any vendor format before the actual extraction process starts.
3Device complexity
If a universal data extraction method is used for all vendors, then the system remains simple, but it cannot adapt to vendor-specific format variations
Solution Approach 1:
The system achieves universality through a modular architecture where a core OCR engine and machine learning framework serve all vendors, while vendor-specific adaptations are handled through trained models rather than separate processing systems. This allows a single unified system to handle multiple vendor formats by selecting and applying the appropriate trained model for each vendor, maintaining system simplicity while achieving broad adaptability.
Solution Approach 2:
The system segments the data extraction process into distinct functional modules: OCR text recognition, vendor identification, format classification, field mapping, and data extraction. Each module can be independently trained and optimized for specific vendor formats, allowing the overall system to remain simple while adapting to diverse formats through specialized segments that work together in a standardized workflow.
Data Source
AI summary
Systems and methods for machine-assisted document input are disclosed. In one embodiment, a method may include a data extraction application executed by a computer processor: receiving an image of a document/email; generating a transcript of the document/email, wherein the transcript comprises a plurality of text groups from the document/email and a location for each text group in the document/email; identifying a vendor associated with the document/email based on contents of one of the text groups and/or one of the locations of the one of one of the text groups; retrieving a vendor-specific machine learning model for the vendor; associating each of the plurality of locations in the document/email with a billing field using the vendor-specific machine learning model; extracting each of the text groups into one of the billing fields based on the association; and transmitting the billing fields with the extracted data to a user electronic device.