Ink Gesture Editing with LLM Prompts for Diverse Mark-Ups
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Solution Overview
Problem
Ink parsing technology struggles to accurately recognize diverse user-generated mark-up symbols and understand written instructions, limiting its ability to automate document editing effectively.
Innovation Solution
Integrate a large language model (LLM) with ink parsing technology to interpret and automate document editing based on ink gestures, using a prompt that includes an image of the ink strokes and a document tree, along with context information to generate suggested revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ink parsing technology is trained to recognize mark-up symbols to accommodate variation in hand-drawn symbols, then recognition capability is improved, but the system reaches a point of diminishing returns and cannot anticipate all user mark-up variations
Solution Approach 1:
The patent introduces an intermediary system that bridges the gap between limited ink parsing capabilities and diverse user mark-ups. The system uses a combination of ink parsing for basic symbol recognition and large language models for semantic understanding of handwritten instructions. This intermediary layer translates various user mark-up styles into actionable editing commands, resolving the contradiction between recognition precision and adaptability to diverse mark-up variations.
Solution Approach 2:
The patent creates a composite system combining multiple technologies: ink parsing technology for stroke recognition, optical character recognition for handwritten text, and large language models for semantic understanding. This composite approach allows the system to handle both standardized mark-up symbols and unique handwritten instructions, overcoming the limitations of any single technology alone.
2Extent of automation
If ink parsing technology is used to identify strokes and recognize mark-up symbols, then basic editing tasks are automated, but the system cannot understand written instructions requiring manual review
Solution Approach 1:
The patent merges ink parsing technology with large language model capabilities to create a unified system that handles both basic mark-up recognition and complex handwritten instructions. The ink parser identifies strokes and basic symbols while the LLM interprets semantic meaning of handwritten text, combining these functions to automate editing tasks that previously required manual review, thus improving ease of operation while maintaining high automation.
3Adaptability or versatility
If the AI model is scaled to broaden training and improve parser recognition capabilities, then more mark-up variations are recognized, but the system complexity and computational resources increase
Solution Approach 1:
The patent segments the document editing system into distinct functional components: an ink parsing module for stroke recognition, an OCR module for handwritten text recognition, and a large language model module for semantic understanding and command generation. This segmentation allows each component to be optimized independently, improving adaptability to various mark-up styles without proportionally increasing overall system complexity, as each segment handles specific aspects of the processing pipeline.
Data Source
AI summary
Systems, methods, and software are disclosed herein for automating the editing of documents based on ink gestures via an LLM integration in various implementations. In an implementation, a computing device receives an ink gesture made by a user with respect to a document displayed in the user interface of an application. The ink gesture includes ink strokes made on a portion of the document. The computing device generates a prompt for an LLM to obtain suggested revisions to the document, wherein the prompt includes an image of the ink strokes made on the portion of the document. The prompt also includes a document tree indicative of a structure of the document. The computing device surfaces a suggested revision generated by the LLM in response to the prompt in the user interface.