AI Document Docketing System for Automated Data Entry
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Solution Overview
Problem
Manual processing of large volumes of documents received through physical mail or email into electronic formats for industries like law firms is inefficient, leading to significant data entry obstacles.
Innovation Solution
A system and method utilizing machine learning and artificial intelligence for automatic docketing and data entry, which includes a processing server and client terminal configuration to generate graphical interfaces, associate documents with schemas, extract data variables, and execute database operations to automate the data entry process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scanning and data entry is used to process documents, then flexibility in handling various document formats is maintained, but productivity and time efficiency significantly deteriorate
Solution Approach 1:
The system enables automatic self-processing of documents through AI-powered extraction and docketing. The automated system independently performs document classification, data extraction, and database entry without requiring manual intervention, thereby significantly improving productivity while the modular architecture keeps complexity manageable
Solution Approach 2:
The patent replaces manual mechanical processes (physical scanning, manual data typing) with automated digital processes using AI and machine learning algorithms. The system automatically extracts data from documents and enters it into databases, eliminating the need for manual labor while maintaining high processing speeds
2Loss of time
If manual data entry is performed, then accuracy can be controlled by human review, but time consumption and labor requirements significantly increase
Solution Approach 1:
The system incorporates feedback mechanisms where extracted data is validated against predefined schemas and patterns. The AI models continuously learn from corrections and feedback loops, improving accuracy over time while maintaining rapid processing speeds without requiring manual verification of each data point
Solution Approach 2:
Manual data entry operations are replaced with automated optical character recognition (OCR) and data extraction algorithms that consistently achieve high accuracy. The system uses pattern recognition and validation rules to ensure data quality while eliminating the time-consuming manual process
3Productivity
If automated AI-based processing is implemented, then productivity and data entry speed significantly improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The system is divided into modular components including document intake modules, AI extraction modules, validation modules, and database integration modules. Each component can be independently developed, tested, and maintained, making the overall complex system easier to implement and manage through standardized interfaces
Solution Approach 2:
The system employs universal AI models and schemas that can handle multiple document types and formats through a single integrated platform. The flexible architecture allows the same system to process various document kinds without requiring separate specialized systems, simplifying implementation while maintaining high throughput
Data Source
AI summary
An automated data entry system comprising target database servers for storing target database(s), a processing server configured to execute a client-application and configured to store a plurality of data objects including a document schema and a target database schema corresponding to the target database(s), and a client terminal connected to the processing server, configured to: generate a first graphical interface for connection to a target database, and to execute a second graphical interface for connection to the client-application. The processing server may also receive an electronic data file representing an unprocessed document, to associate the unprocessed document with a document schema and extract a data variable based on the document schema, and to generate a database operation comprising the data variable configured according to a target database schema. The client terminal is configured to receive the database operation and execute the database operation against a target database.