Conversation Model Generation from Documents
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Solution Overview
Problem
Current systems for converting paper forms into digital formats are tedious, costly, and prone to errors, as they require manual data entry and lack efficient automation for generating conversation models from documents.
Innovation Solution
A system that uses optical character recognition and natural language processing to generate conversation models from document images, allowing for the creation of questions and instructions, and improving models through user interactions and related document analysis, enabling automated chatbot systems to engage users and recommend related document completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual data entry is used to convert paper forms into digital formats, then data can be entered into digital systems, but the process becomes tedious, costly, and error-prone
Solution Approach 1:
The system enables self-service by automatically extracting data from document images using optical character recognition and natural language processing, eliminating the need for manual data entry. The conversation model autonomously fills forms by engaging users in natural dialogue, allowing the system to serve itself without human intervention in the data entry process.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with an automated system combining optical character recognition, natural language processing, and conversation models. This substitution transforms the physical act of typing data into an automated information extraction and synthesis process, dramatically improving both efficiency and accuracy.
2Productivity
If automated systems are implemented to reduce manual work, then efficiency improves, but system complexity increases
Solution Approach 1:
The conversation model serves multiple functions: it extracts information from document images, engages users in natural dialogue, validates responses, and fills forms automatically. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, improving productivity without proportionally increasing complexity.
Solution Approach 2:
The conversation model acts as an intermediary layer between document images and form filling operations. It translates unstructured document data and user responses into structured form inputs, simplifying the overall system architecture by providing a standardized interface that handles the complexity of data extraction and validation internally.
3Extent of automation
If conversation models are generated from documents, then automated user interaction is enabled, but the initial system setup and model generation require complex processing
Solution Approach 1:
The system performs preliminary action by pre-generating conversation models from document images before actual user interactions begin. The optical character recognition and natural language processing extract relevant information and structure it into conversation models in advance, so that when users interact with the system, the automation is already in place and ready to operate.
Solution Approach 2:
The system creates simplified copies of document information in the form of conversation models. Instead of processing complex document structures during each user interaction, it generates lightweight conversational representations that capture the essential information and logic, making automated user interaction feasible without requiring the full document processing complexity during runtime.
Data Source
AI summary
Methods and systems for generating conversation models from documents are described herein. A system may receive a document and generate a conversation model that may be deployed by a chatbot or other automated agent (e.g., voice assistant, messenger bot, etc.). The chatbot may use the conversation model to engage in a conversation with a user and obtain information from the user to complete the document. The system may generate questions to ask the user based on text in the document that indicates a request for information. Additionally, the system may provide instructions to a user via a chatbot. The instructions may be generated based on text in the document that explains how to fill out the document.