Visual-Language ML Pipeline Generation for Non-Programmers
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Solution Overview
Problem
Designing and implementing complex, machine learning-based data processing pipelines requires significant engineering effort and expertise, which poses a barrier to entry for industries like agriculture where human-based phenotyping is prevalent.
Innovation Solution
A method for automatically generating machine learning-based processing pipelines using natural language and visual annotations, enabling individuals without programming expertise to create ML-based state machines by drawing annotations on digital images and describing operations, leveraging multi-modal transformer models to generate target computer code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming and engineering expertise are used to design ML-based processing pipelines, then the pipeline can be implemented with high precision and reliability, but the barrier to entry increases and accessibility decreases
Solution Approach 1:
The patent introduces a multi-modal transformer model as an intermediary between the user's intuitive inputs (natural language and visual annotations) and the complex ML pipeline implementation. This mediator translates simple user intentions into executable code, eliminating the need for users to directly engage with programming complexity while maintaining reliable pipeline generation.
Solution Approach 2:
The patent replaces the mechanical process of manual programming with an automated AI-based code generation system. Instead of requiring users to write and debug code, the system uses natural language processing and visual recognition to automatically generate the necessary ML pipeline code, substituting human programming effort with intelligent automation.
2Adaptability or versatility
If complex ML-based processing pipelines are implemented, then advanced phenotyping capabilities are achieved, but the engineering effort and expertise required increase significantly
Solution Approach 1:
The patent segments the complex ML pipeline into modular components that can be independently generated and combined. The system handles different phenotyping tasks (object detection, classification, measurement) as separate functional units, making the overall complex system manageable and easier to implement through automated generation rather than manual assembly.
Solution Approach 2:
The system enables self-service pipeline generation where the multi-modal transformer model automatically creates the entire ML processing pipeline based on user inputs. This eliminates the need for users to engage in complex engineering efforts, as the system serves itself by generating optimized code structures, data flow graphs, and processing logic without human intervention.
3Manufacturing precision
If traditional programming approaches are used, then precise control over data processing is achieved, but the time required for pipeline creation increases
Solution Approach 1:
The patent employs preliminary action by pre-training the multi-modal transformer model on extensive datasets of ML pipelines, code patterns, and phenotyping tasks. This pre-learning enables the system to quickly generate accurate and precise data processing pipelines without requiring users to spend time learning programming concepts or reviewing detailed code structures.
Data Source
AI summary
Implementations are disclosed for automatically generating computer code that implements a machine learning-based processing pipeline based on multiple different modalities of input. In various implementations, one or more annotations created on a demonstration digital image to annotate one or more visual features depicted in the demonstration digital image may be processed to generate annotation embedding(s). Natural language input describing one or more operations to be performed based on the one or more annotations also may be processed to generate one or more logic embeddings. The annotation embedding(s) and the logic embedding(s) may be processed using a language model to generate, and store in non-transitory computer-readable memory, target computer code. The target computer code may implement a machine learning-based processing pipeline that performs the one or more operations based on the one or more annotations.