Neural Network Suggested Edits for Paste Context
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Solution Overview
Problem
Conventional systems require users to manually adapt pasted text to match the context of the body text, which is time-consuming and degrades the user experience.
Innovation Solution
A method using a trained neural network to generate suggested modifications for pasted text by processing the text segment and its context, allowing for automatic or user-confirmed adaptations based on confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual text adaptation is used to match pasted text to context, then text accuracy is improved, but user time and productivity deteriorate
Solution Approach 1:
The system performs automatic text adaptation itself without requiring user intervention. The neural network model automatically analyzes the pasted text segment, identifies contextual elements in the surrounding text, and generates suggested modifications autonomously, allowing the system to serve itself in the text adaptation task.
Solution Approach 2:
The patent replaces the manual mechanical process of text adaptation with an automated computational system. Instead of users manually analyzing and modifying text, a neural network model processes the text segment and context to generate suggested modifications, substituting human cognitive and manual work with machine learning-based automation.
2Adaptability or versatility
If manual text adaptation is required, then text context compatibility is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically performs text adaptation to match context without requiring user actions. The neural network model independently analyzes the relationship between the pasted text segment and surrounding context, then generates appropriate modifications, making the system self-sufficient in achieving context compatibility.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between the pasted text and the target context. This intermediary automatically analyzes both the text segment and surrounding context, then generates suggested modifications that bridge the gap between the two, facilitating seamless integration without user intervention.
3Productivity
If automated suggested modifications are generated using neural network, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex manual text adaptation processes with a neural network model that automatically generates suggested modifications. This substitution of human cognitive processes with machine learning algorithms improves productivity by eliminating manual intervention while managing complexity through specialized AI infrastructure.
Solution Approach 2:
The neural network model serves multiple functions: it analyzes the pasted text segment, identifies contextual elements in surrounding text, determines appropriate modifications, and generates suggested edits. This multi-functional approach consolidates multiple operations into a single automated system, improving productivity while managing complexity through functional integration.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic generation of suggested edits for paste events. In one aspect, a method comprises receiving information comprising a text segment to be inserted into a body of text displayed in a user interface, identifying a context, where the context is based at least in part on text surrounding the inserted text segment in the body of text, generation an input that comprises the text segment and the context, processing the input using a trained neural network to generate a suggested modification to the inserted text segment, to the context, or both, and presenting the suggested modification to a user in the user interface.