Notes Auto-Curating System for Handwritten Intent Classification
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Solution Overview
Problem
Current notetaking applications on information handling systems fail to provide an intuitive and user-friendly solution for capturing user intent, particularly in recording important points or action items during events, as they struggle to separate and track intent-designated notes among a multitude of handwritten notes, leading to missed important information.
Innovation Solution
A notes auto-curating system utilizing machine learning and crowd-sourced information for intent-classification of digitized handwritten notes, employing symbol and handwriting recognition, along with symbol parsing, to categorize notes as actionable items or important notes, and generate curated lists for easy retrieval and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current notetaking applications store all handwritten notes together, then all notes are captured, but important points and action items cannot be separated from general notes
Solution Approach 1:
The system segments handwritten notes into different categories (important points, action items, general notes) using machine learning classification. This segmentation allows users to separate and track important information from general notes, resolving the contradiction between capturing all notes and enabling easy separation of important information.
Solution Approach 2:
The patent introduces an intermediary layer (machine learning model and symbol parsing system) that automatically analyzes and classifies notes. This intermediary processes the raw handwritten notes and organizes them into structured categories, making important information retrievable without manual user effort.
2Reliability
If manual review of digitized notes is used to find intent-designated notes, then all notes can be examined, but significant time is lost navigating through general notes
Solution Approach 1:
The system performs preliminary classification of notes automatically as they are captured. By pre-organizing notes into categories using machine learning and symbol recognition before the user needs to retrieve them, the system eliminates the time-consuming manual review process while maintaining high retrieval accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual note review with an automated machine learning system. The ML model automatically classifies and organizes notes based on their content and associated symbols, substituting human manual sorting with intelligent automated processing that is both faster and more accurate.
3Extent of automation
If symbol recognition and parsing is implemented to classify notes, then intent-designated notes can be automatically categorized, but system complexity increases
Solution Approach 1:
The system employs a multi-functional processing pipeline that handles multiple tasks (handwriting recognition, symbol detection, parsing, and classification) within a unified machine learning framework. This universal approach consolidates what could be separate complex systems into an integrated solution, achieving high automation while managing complexity through shared processing components.
Data Source
AI summary
A notetaking information handling system device comprising a processor, a memory device, and a touchscreen display device displaying a graphical user interface (GUI) of a software application for accepting digitized handwritten notes and receiving a digitized handwritten note in a first notetaking instance. The processor to record metadata relating to the first notetaking instance and execute code instructions of a notes auto-curating system to recognize and parse the digitized handwritten note and determine from the digitized handwritten note a smart symbol or a keyword within the note content of the digitized handwritten note. The notes auto-curating system to process digitized handwritten note with a trained machine learning system to correlate the digitized handwritten note with an important notes category or an action items category, and to generate a corresponding to-do list or important notes list with the digitized handwritten note with additional similarly-categorized digitized handwritten notes received.