Dynamic Document Interface for Machine-Learning Extraction Review
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Solution Overview
Problem
Conventional software applications for creating electronic documents, such as invoices, are time-consuming, repetitive, and inefficient, particularly for businesses that frequently create similar documents, due to the dynamic nature of the data and the inability of existing systems to automate document creation effectively.
Innovation Solution
A dynamic user interface that displays an automatically created electronic document alongside its source document, allowing users to interact with visual indicators that explain the extraction process, provides confidence levels, and offers alternative items, leveraging machine learning and matching algorithms to enhance automation and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional software applications are used to create electronic documents manually, then users can control and review each document, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically extracts data from source documents and generates electronic documents without requiring manual input. The machine learning model processes source documents, extracts relevant information, and autonomously creates the output documents, allowing the system to serve itself rather than requiring continuous user intervention for each document creation task.
Solution Approach 2:
The patent replaces manual mechanical operations (typing, copying, pasting data) with automated machine learning-based information extraction and document generation. The system uses AI models to understand source documents, extract data, and generate structured electronic documents automatically, eliminating the need for manual data entry and processing.
2Productivity
If automation is introduced to speed up document creation, then productivity improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that bridges the source documents and the generated electronic documents. This intermediary automatically extracts information from unstructured source documents and transforms them into structured data, simplifying the overall system architecture by encapsulating the complexity within the AI model while presenting a simple user interface.
3Loss of time
If machine learning techniques are used to extract data automatically, then time consumption is reduced, but the need for user feedback and verification increases
Solution Approach 1:
The system incorporates feedback mechanisms where users can review the extracted data and generated documents, providing feedback on accuracy. This feedback loop allows the machine learning model to be refined and improved over time, ensuring increasing accuracy while maintaining user control and verification capabilities.
Data Source
AI summary
Aspects of the present disclosure relate to dynamic interaction with an automatically created electronic document. Embodiments include displaying, in a user interface associated with a computing application, a representation of an electronic document that was automatically created based on a source electronic document. Embodiments include displaying, in the user interface, a representation of the source electronic document in a same screen as the representation of the electronic document. Embodiments include detecting, via the user interface, a user action with respect to an item in the electronic document. Embodiments include displaying, in the user interface, based on the user action, in the same screen as the representation of the electronic document and the representation of the source electronic document, a visual indication that the item was automatically determined based on a particular portion of the source electronic document.