LLM-Generated Knowledge Graphs for Accurate Form Processing
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Solution Overview
Problem
Complex electronic forms often result in computer errors due to incorrect algorithm analysis, data formatting issues, and incomplete form fields, making automated processing challenging.
Innovation Solution
A method involving a knowledge graph data structure and a large language model is used to generate and process electronic forms, converting them into a computer-readable format that also allows for visual representation, enabling quick error identification by both computers and humans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing algorithms are used to complete electronic forms, then processing speed and efficiency are improved, but computer errors increase due to incorrect algorithm analysis and data formatting problems
Solution Approach 1:
The system incorporates a verification mechanism that compares algorithm-generated form data against the original form structure and logical constraints. This feedback loop detects formatting errors and logical inconsistencies, allowing the system to correct issues before final submission, thereby maintaining high processing speed while reducing error rates.
Solution Approach 2:
A data validation layer is introduced as an intermediary between the automated processing algorithm and the final electronic form output. This intermediary component checks data formatting, ensures logical consistency, and verifies completeness, acting as a buffer that prevents errors from propagating while allowing efficient automated processing to continue.
2Productivity
If complex electronic forms are processed automatically, then processing efficiency is improved, but data formatting problems occur between different electronic forms
Solution Approach 1:
The system dynamically adjusts data formatting parameters based on the specific form type and required output format. By changing formatting parameters (such as data types, delimiters, and structure) according to the target form's requirements, the system maintains high processing efficiency while ensuring precise data formatting compatibility across different electronic form types.
3Productivity
If automated form processing is implemented, then processing speed is improved, but failure to complete mandatory form fields occurs
Solution Approach 1:
The system performs preliminary validation of mandatory fields before final form generation. By checking required fields in advance and pre-filling or flagging them for attention, the system ensures complete and accurate form completion while maintaining efficient automated processing speeds, preventing failures at the submission stage.
Data Source
AI summary
A method including receiving an object notation data structure including key-value pairs. Each key represents a field of an electronic form. Each value includes at least a first sub-value and a second sub-value for the field. The first sub-value represents a name of the field. The second sub-value represents a range of allowed values for the field. The method also includes applying a large language model to the object notation data structure to generate an output data structure. The output data structure includes a text string defining fields of the electronic form as nodes and further defining relationships among the key-value pairs as edges between the nodes. The method also includes applying an object notation model to the output data structure to convert the output data structure into a knowledge graph data structure including the nodes connected by the edges. The knowledge graph data structure is returned.