AI-Assisted Pipeline Copilot for Workflow Construction
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Solution Overview
Problem
The complexity of real-world AI and analytic solutions poses a high learning cost for developers, making it difficult for them to transition between different development environments and platforms, even with code conversion tools, as they need to understand various layers of the software stack, APIs, and data flow.
Innovation Solution
An AI-assisted pipeline copilot provides a graphical interface for developers to construct AI applications and workflows by connecting key AI analytics, data, and IO functions, using neural network models to recommend next task components and connections based on user-provided pipeline information, reducing the need for deep knowledge of libraries and APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If developers use traditional development approaches requiring deep knowledge of software stack, APIs and data flow, then they can build complex AI solutions, but the learning cost is high and platform transition is difficult
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the developer and the complex software stack. The AI assistant handles the complexity of APIs, data flow, and software stack details, allowing developers to work at a higher abstraction level without needing deep knowledge of underlying systems.
Solution Approach 2:
The patent replaces the mechanical process of manually understanding and configuring complex software stacks with an AI-based system that automatically generates code and configurations. This substitution eliminates the need for developers to manually navigate complex documentation and API references.
2Adaptability or versatility
If developers switch between different development platforms, then they can access diverse tools and capabilities, but the transition time is significant due to learning curves
Solution Approach 1:
The patent creates a universal development interface that works across multiple platforms. The AI assistant platform-agnostic approach allows developers to work with a consistent set of tools and workflows regardless of the underlying platform, making the development process portable and reducing relearning requirements.
Solution Approach 2:
The patent uses code generation and template-based approaches to copy proven development patterns across different platforms. Instead of learning each platform's unique syntax and conventions, developers can leverage generated code that adapts to different platforms while maintaining consistent development practices.
3Productivity
If developers manually construct AI pipelines with complete knowledge of components and connections, then they can optimize performance, but the development time and expertise required are excessive
Solution Approach 1:
The patent implements a self-service system where the AI assistant automatically generates pipeline configurations, selects appropriate components, and establishes connections based on high-level user specifications. The system serves itself by autonomously handling the complex tasks of pipeline construction without requiring manual intervention for each configuration detail.
Solution Approach 2:
The patent performs preliminary actions by pre-generating code templates, pre-configuring common pipeline patterns, and pre-establishing best practices. This preliminary preparation allows developers to quickly assemble pipelines by selecting from pre-configured options rather than building everything from scratch.
Data Source
AI summary
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).