AI Form Content Generation Using Iterative Prompt Extraction
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Solution Overview
Problem
Creating software application content for forms, such as tax or loan applications, is a complex and labor-intensive task prone to human error, and existing machine learning technologies struggle to handle the intricacies of extracting information from dense forms.
Innovation Solution
Utilizing machine learning models to dynamically generate prompts and embeddings from forms and related documents, with user feedback loops for continuous improvement, to create accurate and user-friendly software application content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create software application content from forms, then accuracy and understanding of complex forms can be maintained, but the process becomes extremely labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical processes of reading, understanding, and transcribing form data with an automated machine learning system. The ML model automatically extracts information from forms and generates software application content, eliminating the need for human experts to manually process each form while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the source forms and the target software application content. This intermediary automatically processes the transformation, understanding complex form structures and translating them into software content without requiring direct human intervention in each conversion task.
2Extent of automation
If existing machine learning technologies are used to generate content, then automation can be achieved, but the systems struggle to handle the complexities of extracting information from dense forms
Solution Approach 1:
The patent performs preliminary actions by training the machine learning model on extensive form data before deployment. The model learns to recognize and extract information from various form formats and structures in advance, preparing it to handle complex extraction tasks reliably when processing actual software application content.
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model's extraction results are evaluated and used to improve future extractions. This continuous learning process allows the system to handle increasingly complex form structures while maintaining and improving extraction accuracy over time.
Data Source
AI summary
Aspects of the present disclosure relate to generating software application content related to forms. Embodiments include providing a form and a prompt comprising instructions to a first machine learning model. The first machine learning model may be used to extract first information from the form based on an embedding representation of the form. Based on the first extracted information, a second prompt may be generated and provided to the first machine learning model. Then, based on the second prompt, the first machine learning model may extract second information from the form. The first information and the second information may then be provided to a generative machine learning model that is then used to generate software application content based on the first and second information.