AI Freeform User Interface for Handwriting and Symbol Queries
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Solution Overview
Problem
Existing graphical user interfaces (GUIs) lack the ability to effectively process freeform inputs such as handwritten text and symbolic information, limiting users' ability to express complex queries and interactions.
Innovation Solution
A system utilizing artificial intelligence (AI) that combines handwriting recognition and machine learning to analyze and interpret handwritten text and symbolic inputs, transforming them into actionable queries that can interact with backend services, enabling richer data interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional navigation-based and text-based input methods are used, then the system structure remains simple, but the user interaction capability and expressiveness are limited
Solution Approach 1:
The patent introduces an AI engine as an intermediary component that bridges the gap between freeform user inputs (handwriting, drawings, symbols) and the electronic device's processing system. The AI engine translates natural, expressive inputs into actionable commands, enabling enhanced user interaction capability without requiring complex direct processing of diverse input types in the device structure
Solution Approach 2:
The system implements a universal input interface that accepts multiple types of freeform inputs (handwriting, drawings, symbolic representations) through a single integrated mechanism. This multi-functional approach allows users to express complex queries and intentions through various natural methods while maintaining a unified system structure that processes all input types through the AI engine
2Ease of operation
If freeform input methods like handwriting and drawing are enabled, then the expressiveness and naturalness of user input improve, but the input interpretation complexity increases
Solution Approach 1:
The AI engine is designed to autonomously interpret and understand freeform inputs without requiring manual structuring or explicit formatting from users. The system self-services the complex task of analyzing handwriting, drawings, and symbolic representations, automatically extracting meaning and intent while maintaining ease of operation for users who naturally express themselves
3Loss of information
If AI-based freeform input processing is implemented, then the ability to convey complex queries improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of freeform inputs by the AI engine to extract key features, patterns, and semantic meaning before main processing occurs. This preliminary action includes recognizing handwriting patterns, identifying drawing elements, and understanding symbolic representations in advance, which reduces the computational burden and processing time for subsequent query execution
Data Source
AI summary
In an example embodiment, first user input including handwriting input and non-alphanumeric symbolic input is detected. The non-alphanumeric symbolic input is input into a first machine learning model trained to output a set of possible actions corresponding to the non-alphanumeric symbolic input and a probability score assigned to each action in the set of possible actions. A combination of the action having the highest probability score and textual input from the handwriting input is input into a second machine learning model trained to select a service from a plurality of services based on the textual input and the selected action by referencing a service model corresponding to each service in the plurality of services. The combination of the textual input and the selected action is transformed into a native request for the selected service based on the service model for the selected service.