AI Industrial Process Generation from Multimedia Process Inputs
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Solution Overview
Problem
Current industrial process design and generation are time-consuming and inefficient, relying on human imagination and lacking the use of existing process libraries, which limits the ability to generate ideal processes or alternatives.
Innovation Solution
A computer-implemented system using AI and ML models that receives multimedia inputs, analyzes semantics, and generates industrial processes by combining process specifications and descriptions from a dataset or library, outputting them in various formats such as 3D CAD representations or videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual industrial process design is performed by industrial engineers, then the process can be customized to specific needs, but the design time is excessive and productivity is low
Solution Approach 1:
The system creates digital copies of physical industrial processes through multimedia inputs (images, videos, audio), converting real-world processes into structured digital representations that can be stored, analyzed, and reused. This copying mechanism eliminates the need to manually redesign processes from scratch.
Solution Approach 2:
The patent replaces manual mechanical process design with an automated AI-based system. Machine learning models and natural language processing algorithms substitute for human industrial engineers, automatically generating process specifications and descriptions from multimedia inputs without requiring manual programming or detailed engineering knowledge.
2Adaptability or versatility
If existing process libraries are not utilized, then unique custom processes can be created, but the design process becomes time-consuming and repetitive
Solution Approach 1:
The system performs preliminary action by pre-processing multimedia inputs into structured process specifications and descriptions before actual process generation. The AI models pre-analyze images, videos, and audio to extract process parameters, layouts, and operational details, preparing data that can be quickly assembled into complete process designs.
Solution Approach 2:
The system achieves universality by handling multiple types of multimedia inputs (images, videos, audio recordings) and generating various process documentation formats simultaneously. A single AI system performs multiple functions: semantic analysis, process specification extraction, description generation, and format conversion, eliminating the need for separate tools for each task.
3Ease of manufacture
If human imagination is relied upon for process design, then creative solutions can be generated, but the process lacks consistency and ideal optimization
Solution Approach 1:
The system implements feedback loops where generated process specifications and descriptions are continuously refined based on AI model analysis. The system can compare generated processes against stored process libraries, identify improvements, and iteratively optimize process designs to achieve ideal configurations while maintaining consistency.
Solution Approach 2:
The AI system systematically varies and optimizes process parameters based on analyzed multimedia inputs and stored process data. Machine learning models adjust process specifications, timing, resource allocation, and operational parameters to achieve optimal process configurations, replacing subjective human judgment with data-driven parameter optimization.
4Extent of automation
If computer programming skills are required for process analysis, then digital process representation is achievable, but the accessibility to industrial engineering becomes limited
Solution Approach 1:
The system performs self-service by automatically analyzing multimedia inputs and generating process specifications without requiring user programming skills. The AI models autonomously extract process parameters, create digital representations, and generate analysis results, eliminating the need for users to write or understand computer code for process digitalization.
Solution Approach 2:
The patent introduces an intermediary AI system that translates between multimedia inputs and structured process data. This intermediary layer handles the complexity of digital process analysis, converting images, videos, and audio into standardized process specifications that can be used by industrial engineers without requiring them to master programming or complex data processing techniques.
Data Source
AI summary
A system and method for generating industrial process in a computing environment is disclosed. The system receives multimedia input from users, analyzes multimedia input, to determine semantics associated with multimedia inputs, using at least one of natural language and symbolic processing techniques, and deep learning technique. Furthermore, the system determines process specifications and descriptions in industrial process, using at least one of a dataset and library comprising representations of industrial processes in plurality of configurations, using machine learning (ML) models. Additionally, the system combines a plurality of types of process specifications and descriptions. Further, the system generates industrial processes corresponding to the multimedia inputs, based on combining the plurality of types of process specifications and descriptions. Furthermore, the system outputs the generated industrial processes, on at least one of display of a user device, and external devices.