18570912. AI-ASSISTED CONTEXT-AWARE PIPELINE CREATION simplified abstract (Intel Corporation)
Contents
AI-ASSISTED CONTEXT-AWARE PIPELINE CREATION
Organization Name
Inventor(s)
Richard Chuang of Chandler AZ (US)
AI-ASSISTED CONTEXT-AWARE PIPELINE CREATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18570912 titled 'AI-ASSISTED CONTEXT-AWARE PIPELINE CREATION
- Simplified Explanation:**
This patent application describes AI-assisted pipeline copilot techniques, where an artificial intelligence system helps users optimize their workflow by recommending next task components for their pipeline based on key words and connections.
- Key Features and Innovation:**
- Workflow method using AI-assisted pipeline copilot
- Neural network model to recommend next task components
- Inference of connections between recommended tasks and existing pipeline
- User interface for providing recommendations to users
- Potential Applications:**
- Project management
- Data analysis
- Software development
- Problems Solved:**
- Streamlining workflow processes
- Improving efficiency in task management
- Enhancing decision-making in pipeline development
- Benefits:**
- Increased productivity
- Better resource allocation
- Enhanced project outcomes
- Commercial Applications:**
Optimizing workflow processes in various industries such as software development, data analysis, and project management can lead to increased efficiency and improved project outcomes, making this technology valuable for businesses looking to streamline their operations.
- Questions about AI-assisted pipeline copilot:**
1. How does the AI-assisted pipeline copilot system determine the recommended next task components? 2. What are the potential challenges in implementing AI-assisted pipeline copilot techniques in different industries?
Original Abstract Submitted
AI-assisted pipeline copilot techniques are described herein. In one example, a workflow method using an AI-assisted pipeline copilot involves receiving pipeline information from a user for an artificial intelligence (AI) pipeline and identifying key words in the pipeline information. A recommended next task component to add to the AI pipeline is then determined using a neural network model based on: a mapping of the key words to AI pipeline stages and one or more previous task components added to the AI pipeline. Connections between the recommended next task and the existing pipeline can also be inferred with a second neural network model. The recommended next task components and connections can then be provided to the user (e.g., with a graphical user interface).
