Generative Neural Network Document Modification With Asynchronous Editing
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Solution Overview
Problem
Users face challenges in articulating creative goals to generative neural network models, managing data and documents across fragmented ecosystems, and maintaining consistency in outputs due to lack of memory across prompts or sessions.
Innovation Solution
A system utilizing a generative neural network model with an intermediary that provides an improved graphical user interface for document modification, allowing asynchronous editing and management of textual contexts, enabling users to interact with the model without waiting for generation times, and managing data and document consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user interacts with a generative neural network model through a standalone service, then the model can provide creative output, but the user must work across a fragmented ecosystem of tools and devote energy to managing data and documents
Solution Approach 1:
The patent combines the generative neural network model with the document editing application into a unified system. The model is integrated directly into the application, allowing users to access creative capabilities without switching between separate tools or managing fragmented ecosystems. This merging eliminates the need for users to coordinate multiple standalone services while maintaining full creative functionality.
2Adaptability or versatility
If a user requests output from a generative neural network model, then the model can generate creative content, but the user must wait during the generation time
Solution Approach 1:
The patent implements asynchronous generation that allows the user interface to remain responsive and continue accepting user inputs while the neural network model generates content in the background. Instead of blocking the user interface during generation, the system maintains continuous useful action by decoupling the user interaction thread from the content generation thread, enabling users to perform other tasks or review previously generated content without idle waiting.
3Ease of operation
If a user edits a document synchronously while waiting for model output, then the user can manage document content, but the editing time is extended due to waiting for generation
Solution Approach 1:
The patent initiates the content generation process as soon as the user requests it, without requiring the user to wait or complete other tasks first. The system performs the generation action preliminarily and independently in the background, allowing the user to continue editing or preparing other parts of the document simultaneously. This preliminary action eliminates the sequential dependency where editing must wait for generation to complete.
4Device complexity
If the generative neural network model processes requests sequentially, then the system can maintain simplicity, but the productivity is reduced due to waiting time
Solution Approach 1:
The patent implements dynamic processing where the system can handle multiple generation requests concurrently rather than strictly sequentially. The asynchronous architecture allows the system to dynamically manage multiple tasks at different stages of processing, improving throughput without significantly increasing system complexity. Users can submit multiple requests and the system processes them in parallel, dramatically increasing productivity while maintaining manageable system architecture.
Data Source
AI summary
A method of modifying a document is described. A first user input is received from a user via an editing window of a graphical user interface. The first user input represents a request to a generative neural network model for drafting assistance with the document. The editing window displays content of the document during an editing session of the document for the user. An output generated by the generative neural network model based on the first user input and the content of the document is asynchronously obtained while maintaining the editing session. The content of the document is modified during the editing session to include the output generated by the generative neural network model. The modified content is caused to be displayed within the editing window during the editing session.