This invention relates to a process, system and computer code to sequence processes to automate based on return on investment or ROI. The process and system divides considers the mix of human and robotic steps to optimize cost, quality and cycle-time of the process; classifying a process based on an entity and corresponding divisional partition, such as one of a group, department or stakeholder, and (2) generating key criteria; categorizing the ROI; applying constraints such as one of (a) cost, (b) quality or cycle-time; comparing one of (a) the human entered data, (b) the robot entered data, (c) the bot acquired data, with respect to one (i) cost, (ii) quality or (iii) cycle-time; queuing one of (a) a human task, (b) a robot task, or (c) a bot constructed task; storing one of (a) tracking process changes, (b process details and constraints in the event of a change.

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

A method of fabrication for a component to be installed in a facility, the facility being represented by an electronic design model, the method including extracting, at a computing device and from the model, details of the component and controlling a plurality of stations with the computing device to fabricate and track the component.

In example embodiments, a multi-stage PFS diagram generation technique is used to iteratively define the layout of a PFS diagram from a subset of engineering data in a 3D model of an industrial process. The multi-stage PFS diagram generation technique may repeatedly call an automatic layout generator, which each time solves for one unknown quality of the PFS diagram (e.g., relative positions of components in the PFS diagram, positions on components in the PFS diagram, sizes of the components in the PFS diagram). The PFS diagram may be adapted based on user preferences, for example to define the subset of engineering data, or to constrain aspects of its layout. Updated PFS diagrams may be generated by selecting different user preferences.

Provided is an analysis apparatus comprising: a variation model storage unit configured to store a plurality of variation models indicating variation in characteristics of a plant corresponding to an operating condition of the plant; a model extraction unit configured to acquire structure information indicating a structure model of an analysis target plant and to extract the variation model corresponding to the structure model; and an analysis unit configured to analyze the analysis target plant, based on the structure model of the analysis target plant and the variation model extracted by the model extraction unit.

A method for generating an orthotic device is disclosed. The method includes receiving data from a client device of a patient, the data comprising patient information and image data representative of a body part of the patient. The method further includes generating, based on the image data, three-dimensional model data representative of the body part.

A method includes identifying at least one of historical data associated with historical substrate lots processed by substrate processing tools in a substrate processing facility or simulated data for simulated substrate lots processed by simulated substrate processing tools. The method further includes generating features from the at least one of the historical data for the historical substrate lots or the simulated data for the simulated substrate lots. The method further includes training a machine learning model with data input comprising the features to generate a trained machine learning model. The trained machine learning model is capable of generating one or more outputs indicative of one or more corrective actions to be performed in the substrate processing facility.

A method for generating an orthotic device is disclosed. The method includes receiving data from a client device of a patient, the data comprising patient information and image data representative of a body part of the patient. The method further includes generating, based on the image data, three-dimensional model data representative of the body part.

Methods, systems, and media for optimization of fabrication processes are provided. In some implementations, a method of automatically optimizing fabrication processes comprises: (a) providing a first set of process parameter values associated with a first experiment to a model representing a fabrication process; (b) characterizing a statistical uncertainty of predictions made by the model; (c) using an acquisition function to select a second set of process parameter values, wherein the acquisition function identifies the second set of process parameters based on both: (i) a difference between predicted wafer characteristics and a target specification; and (ii) the statistical uncertainty; (d) receiving results of the fabrication process performed using the second set of process parameter values; and (e) determining whether the performance of the fabrication process generates a post-processed wafer having wafer characteristics that meet the target specification.