System and method for generating industrial processes in a computing environment

A computer-implemented method for generating one or more a machine learning based (ML-based) industrial processes in a computing environment, the computer-implemented method comprising:

• receiving, by one or more hardware processors, one or more multimedia inputs from one or more users:
• analyzing, by the one or more hardware processors, the received one or more multimedia inputs to determine semantics associated with the one or more multimedia inputs, using at least one of:
• natural language processing techniques, symbolic processing techniques, and deep learning techniques, wherein the semantics comprises at least one of:
• contexts, nuances, linguistics, and modalities;
• determining, by the one or more hardware processors, one or more process specifications and one or more descriptions in one or more industrial processes, using machine learning (ML) models wherein the ML models comprise transformer models, autoencoders, generative adversarial networks, and autoregressive models, wherein the one or more process specifications comprises at least one of:
• a work cell layout for an assembly operation, a first standard operating procedure for the assembly operation, and a computer language description of the one or more industrial processes, wherein the one or more descriptions comprises a natural language description of the one or more industrial processes;
• training, by the one or more hardware processors, the ML models on a library of the one or more process specifications and the one or more descriptions:
• performing, by the one or more hardware processors, a generative modeling of the ML models using at least one of:
• optimization algorithms, machine learning model architectures, and an objective function;
• tuning weights and parameters, by the one or more hardware processors of the ML models to return output corresponding to the one or more multimedia inputs, during the training of the ML models;
• combining, by the one or more hardware processors, the one or more process specifications and the one or more descriptions;
• generating, by the one or more hardware processors, the one or more ML-based industrial processes corresponding to the one or more multimedia inputs, based on combining the plurality of types of the one or more process specifications and the one or more descriptions;
• and
• outputting, by the one or more hardware processors, the generated one or more ML-based industrial processes, on one of a display of a user device, and one or more external devices and wherein the outputting of the generated one or more ML-based industrial processes comprises outputting a description of the one or more ML-based industrial processes with a three-dimensional (3D) workstation layout and second standard operating procedures, and wherein the outputting of the description of the one or more ML-based industrial processes is configured to output the description as one of:
• augmented reality (AR) and virtual reality (VR) representations.

A non-transitory computer-readable storage medium having programmable instructions stored therein, that when executed by one or more hardware processors, cause the one or more hardware processors to:

• receive one or more multimedia inputs from one or more users:
• analyze the received one or more multimedia inputs to determine semantics associated with the one or more multimedia inputs, using at least one of:
• natural language processing techniques, symbolic processing techniques, and deep learning techniques, wherein the semantics comprises at least one of:
• contexts, nuances, linguistics, and modalities;
• determine one or more process specifications and one or more descriptions in one or more industrial process processes, using machine learning (ML) models wherein the ML models comprise transformer models, autoencoders, generative adversarial networks, and autoregressive models, wherein the one or more process specifications comprises at least one of:
• a work cell layout for an assembly operation, a first standard operating procedure for the assembly operation, and a computer language description of the one or more industrial processes, wherein the one or more descriptions comprises a natural language description of the one or more industrial processes;
• a machine learning (ML) model training subsystem configured to train the ML models on a library of the one or more process specifications and the one or more descriptions:
• a generative modeling subsystem configured to perform a generative modeling of the ML models using at least one of:
• optimization algorithms, machine learning model architectures, and an objective function;
• an output tuning subsystem configured to tune weights and parameters of the ML models to return output corresponding to the one or more multimedia inputs, during the training of the ML models;
• combine the one or more process specifications and the one or more descriptions;
• generate one or more machine learning based (ML-based) industrial processes corresponding to the one or more multimedia inputs, based on combining the one or more process specifications and the one or more descriptions:
• and
• output the generated one or more ML-based industrial processes, on one of:
• a display of a user device and one or more external devices and wherein the outputting of the generated one or more ML-based industrial processes, comprises outputting a description of the one or more ML-based industrial processes with a three-dimensional (3D) workstation layout and second standard operating procedures, and wherein the outputting of the description of the one or more ML-based industrial processes is configured to output the description as one of:
• augmented reality (AR) and virtual reality (VR) representations.