Robot Fleet Management and Additive Manufacturing for Value Chain Networks

Abstract

The value chain network automation system includes a supply chain robot fleet dataset including attributes of a set of states and capabilities of a set of robotic systems in a supply chain for a set of goods, a demand intelligence robotic process automation dataset including attributes of a set of states of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods, and a coordination system that provides a set of robotic task instructions for the supply chain robot fleet based on processing of the supply chain robot fleet dataset and the demand intelligence robotic process automation dataset to coordinate demand and supply for the set of goods.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 127,983, filed December 18, 2020, and U.S. Provisional Patent Application No. 63 / 185,348, filed May 6, 2021. This application also claims priority to Indian Patent Application No. IN202111029964, filed July 3, 2021, and Indian Patent Application No. IN202111036187, filed August 10, 2021, the entire disclosures of which are incorporated by reference.

[0002] The present disclosure relates to information technology methods and systems for managing value chain network entities, including supply chain and demand management entities. The present disclosure also relates to the field of enterprise management platforms, more particularly including data management, artificial intelligence, network connectivity, digital twins, additive manufacturing, robotics as a service, and energy management. [Background technology]

[0003] Historically, many of the various categories of goods purchased and used by home consumers, businesses, and other customers were primarily supplied in a relatively linear fashion, with manufacturers and other suppliers of finished goods, parts, and other items delivering the goods to shipping companies, freight forwarders, etc., which then delivered the goods to warehouses for temporary storage, to retail stores where the customers made their purchases, or directly to the customers' locations. To stimulate and meet customer demand, manufacturers and retailers engaged in various sales and marketing activities, including product design, shelf and advertising placement, and pricing.

[0004] Orders for products were fulfilled by manufacturers through a supply chain such as that depicted in FIG. 1 , with suppliers 122 in various supply environments 160 operating production facilities 134 or acting as resellers or distributors for others, making products 130 available at origin locations 102 in response to orders. As products 130 moved through the supply chain, they were transported and stored via various transportation facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112, and delivery systems 114, such as trucks and other vehicles, trains, etc. In many cases, maritime facilities and infrastructure, such as ships, barges, docks, and ports, provided transportation over waterways between origin locations 102 and one or more destination locations 104.

[0005] Organizations have access to a nearly limitless amount of data. With the advent of smart connected devices, wearable technology, the Internet of Things (IoT), and more, the amount of data available to organizations planning, supervising, managing, and operating their value chain networks has dramatically increased and is likely to continue to do so. For example, a manufacturing facility, warehouse, campus, or other operating environment may have hundreds or even thousands of IoT sensors providing metrics such as vibration data measuring the vibration signatures of critical machinery, temperature throughout the facility, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many other metrics. Furthermore, as wearable technology becomes more prevalent, wearables may be able to capture worker behavior, health indicators, physiological status, activity status, movements, and other characteristics. Additionally, as organizations adopt CRM systems, ERP systems, operational systems, information technology systems, advanced analytics, and other systems that leverage information and information technology, they gain increasingly broader access to other large datasets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, etc. (including datasets generated by or for the organization and third-party datasets).

[0006] While the existence of more data and new types of data provides many opportunities for organizations to achieve competitive advantage, it also presents challenges, such as complexity and volume, which can overwhelm users and lead to missed insight opportunities. There is a need for methods and systems that enable companies to not only capture data, but also transform data into insights, and then transform insights into informed decisions and timely execution of efficient operations.

[0007] (Additive Manufacturing) Additive manufacturing, which encompasses techniques such as 3D printing, vapor deposition, polymer (or other material) coating, epitaxial growth, and / or crystal growth approaches, can be used alone or in combination with other techniques, such as subtractive or assembly techniques, to produce three-dimensional products from a design, through a process of forming successive layers of the product, arriving at a finished part or system at any intermediate or subsequent steps. The design may be in the form of a data source, such as an electronic 3D model created with a computer-aided design package or a 3D scanner. 3D printing or other additive processes form an initial layer of material and then add successive layers of material, where each new layer of material is added on top of the previously formed layer, until the entire designed three-dimensional product is completed. Throughout this disclosure, references to 3D printing or other specific additive manufacturing techniques should be understood to encompass alternative embodiments, including other additive manufacturing techniques, unless the context specifically dictates otherwise.

[0008] Many additive processes are currently available. These processes may differ in the way successive layers are deposited to create a 3D product. They may also differ in the materials used to form the product. Metals (including alloys unless otherwise specified in the context, including specialty metals such as shape memory materials) are increasingly popular 3D printing materials. Common examples include titanium, stainless steel, aluminum, tool steel, Inconel, and cobalt chrome. Metals can also be melted or softened to create layers. Examples of metal 3D printing methods include selective laser melting (SLM), selective laser sintering (SLS), direct metal laser sintering (DMLS), and / or fused deposition modeling (FDM). Other methods include: (a) metal extrusion, in which polymer filaments or rods are enriched with metal powder and extruded through a nozzle (as in FDM) to form a "green" part that can be post-processed (debinding and sintering) to create a fully metal part; (b) metal binder jetting, in which a printhead is used to apply a liquid binder to a powder layer; and (c) nanoparticle jetting, in which metal nanoparticles are jetted in ultra-thin layers from an inkjet nozzle.

[0009] Regardless of the design data source or method, additive manufacturing, including metal 3D printing, is prone to inefficiencies, process variability, product inconsistencies, and unreliability throughout the entire process, from design and manufacturing to delivery to the end customer. This can result in issues such as final 3D printed products not meeting customer expectations or product specifications, low-quality 3D printed products, or part failures. These issues can also increase operating costs for 3D printing service providers due to material waste, reduced throughput due to machine downtime and unproductive print time, and associated supply chain risks and inefficiencies. For example, 3D printed products often deform during or after production due to printing procedures or suboptimal printing parameters. Common issues resulting from inefficient manufacturing supply chains include fraud, delivery delays, contractual liability, and product recalls.

[0010] There is a need for smarter product design, manufacturing, supply chain, and demand management methods and systems to ensure that final metal 3D printed products meet customer expectations and manufacturer specifications for quality, cost, and delivery. Additionally, there is a need for methods and systems to improve the monitoring, management, and optimization of additive manufacturing capacity by various stakeholders.

[0011] Traditional machine vision systems consist of a combination of optics, lighting, sensors, and software, and aim to replicate the functions of the human eye. Such systems create an image of an object by capturing and analyzing light reflected from the object. Optical lenses capture the image and present it to an image sensor, such as a charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) device. These devices contain a matrix or linear array of precisely spaced, tiny, light-sensitive elements fabricated on a silicon chip using integrated circuit technology. The sensor device converts the light beams that stream through the camera lens into analog electrical signals corresponding to the light's intensity. In this way, the image of the object is broken down into an array of individual picture elements (pixels). An analog-to-digital converter is used to convert the pixel analog voltages into digital values. When the voltage level of each pixel is assigned a value of either 0 or 1 based on a threshold, it is called a binary system. In contrast, a grayscale system assigns up to 256 different values ​​to each pixel, depending on its intensity. This allows for the differentiation of various shades of gray in addition to black and white. A grayscale image can be considered to have one channel, represented by a two-dimensional matrix of pixels, each with a pixel value ranging from 0 to 255. A color image, on the other hand, represents the brightness and color of each pixel in the image using three primary colors: red (R), green (G), and blue (B). Thus, a color image has red, green, and blue (RGB) channels, each representing the RGB components of the image. The raw data captured by the image sensor is sent to an image processing system for analysis. The image processing system then processes the raw data to extract information useful for analyzing the image and make decisions regarding such analysis. The image processing system may include preprocessing functions to improve image quality. For example, such processing may include image scaling, noise reduction, color adjustment, brightness adjustment, white balance adjustment, sharpness adjustment, contrast adjustment, etc. The image may also be analyzed using machine learning or other algorithms to identify one or more objects in the image and determine the location and orientation of such objects.

[0012] Vision technology has made great strides in recent years, largely related to the processing of image data captured by vision sensors. This can be attributed to the use of big data, advanced machine learning algorithms such as convolutional neural networks (CNNs), and graphics processing units (GPUs). However, traditional vision technologies have significant limitations, particularly when it comes to acquiring raw data about objects and scenes. For example, the optical lenses in traditional vision systems rely on simple focusing techniques to extract information in a linear, obtuse manner. Attempting to focus an object on an image ultimately results in the loss of significant information and other optical properties. Therefore, there is a need to incorporate previously lost or inferred information to generate images that are not "perfect" to the human eye but "rich" to algorithms. Additionally, there is a need for richer object recognition complex vision applications where traditional vision techniques have proven inadequate, including object recognition in dynamic environments, such as when the object or vision system is moving, as in mobile and robotic use cases; three-dimensional (3D) object recognition by acquiring depth data; prediction of object attributes such as depth, orientation, and motion; small object recognition; facial feature recognition; object recognition in power-constrained or network-constrained environments; and other use cases where traditional machine vision systems and methods are not suitable.

[0013] Furthermore, automation is revolutionizing the value chain of almost every item, and robotics is at its core. While the role of physical robots in manufacturing has been expanding for years, typical implementations have focused on stationary robots performing predefined tasks in predefined positions, such as painting or welding on an assembly line. While this limited role has led to and continues to lead to significant improvements in quality, cost, and productivity, it has not fully leveraged emerging technologies such as engineering, materials science, software process automation, artificial intelligence, additive manufacturing, data-driven analytics, digital twins, blockchain, and smart contracts. These technologies, when integrated with advances in robotics (including hardware and software robotics), can create an innovative array of highly capable, autonomous robots with interactive capabilities. Emerging and future robot classes and capabilities offer opportunities for an ever-expanding range of robot use cases and management platforms that can automatically configure, organize, deploy, and control robots and robot fleets to securely deliver reliable services, including contract services that access robot fleet capabilities on "Robotics as a Service" platforms. Summary of the Invention

[0014] The robotic fleet management platform includes a set of data stores storing a governance library that defines a set of governance criteria, including at least one set of security criteria, legal criteria, ethical criteria, regulatory criteria, quality criteria, or engineering criteria, that apply to decisions made by one or more respective intelligence services. The robotic fleet management platform includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively execute a governance-enabling intelligence layer that receives and responds to intelligence requests received from respective intelligence service clients. The intelligence layer includes a set of artificial intelligence services, including at least one of machine learning services, rule-based intelligence services, digital twin services, robotic process automation services, or machine vision services. The intelligence layer includes an intelligence layer controller that coordinates the execution of each intelligence service on behalf of each intelligence service client and the execution of a set of analyses corresponding to each intelligence service based in part on a set of governance criteria. In response to intelligence requests, the intelligence layer returns a decision collectively determined by the artificial intelligence services, where the decision is determined based on the set of intelligence service data sources and the set of analyses.

[0015] In other features, the intelligence layer controller is configured to receive an intelligence request from an intelligence service client indicating a requested decision, determine zero or more governance criteria implied by a type of the requested decision, determine zero or more predefined analyses implied by the type of the requested decision or the governance criteria implied by the type of decision, and provide the zero or more governance criteria and the zero or more predefined analyses to the artificial intelligence service, where the zero or more governance criteria are selected from governance criteria defined in a governance library.

[0016] In other features, the intelligence layer controller is further configured to iteratively determine and provide additional governance criteria and predefined analyses to the artificial intelligence service in response to decisions made by the artificial intelligence service until the requested decision is provided by the artificial intelligence service. In other features, the intelligence layer is further configured to determine a set of intelligence layer data sources based on a type of decision requested. In other features, the requesting intelligence service client provides the set of intelligence layer data sources with the request. In other features, the decisions provided by the intelligence layer define respective actions to be taken by each intelligence service client. In other features, the respective actions include actions requiring human intervention.

[0017] In other features, each action comprises a non-adaptive predefined action. In other features, each action comprises a domain-specific action responsive to each request. In other features, the intelligence service client comprises a security system requesting a classification of a potential security risk. In other features, the intelligence service client comprises a resource provisioning system requesting a recommendation of resources to support a robotic fleet. In other features, the intelligence service client comprises a logistics system requesting a logistics-based recommendation for one or more robotic fleets. In other features, the intelligence service client comprises a job configuration system requesting proposed job configurations given a job request. In other features, the intelligence service client comprises a fleet configuration system requesting proposed fleet configurations given a set of tasks to be completed by the robotic fleet. In other features, the intelligence service client comprises a robotic operating unit deployed by a robotic fleet management platform.

[0018] A robot fleet management platform for configuring robotic fleet resources includes one or more processors configured to execute a set of computer-readable instructions. The one or more processors collectively execute a job analysis system that applies a set of filters to job content received in association with a job request to identify portions suitable for robotic automation. A task definition system establishes a set of robot tasks, each defining at least a robot type and a task objective, where the set of robot tasks is based at least in part on portions of the job request that are suitable for robotic automation and that satisfy a first fleet objective of the set of fleet objectives. A fleet configuration proxy service processes the set of robot tasks and additional job content associated with the job request to generate a fleet resource configuration data structure for the job request, the fleet resource configuration data structure defining a set of task associations and a set of robot adaptation instructions. Each task association associates at least one robot operating unit with a respective robot task of the set of robot tasks, and the set of robot adaptation instructions defines a method by which one or more robot operating units of the robot fleet are adapted to perform respective tasks assigned to the robots. The fleet intelligence layer activates a set of intelligence services to generate at least one recommended robot task and associated context information that facilitates robot selection and task sequencing in a robot task workflow. The job workflow system generates a workflow that defines an execution order of the robot tasks based on the fleet resource configuration data structure and the set of robot tasks. The workflow simulation system is configured to simulate job performance based on the workflow and the job execution simulation environment.The workflow simulation system applies the workflow to generate simulation results in a job execution simulation environment including a digital model of the robotic operating units assigned to the robot fleet and a digital model of the task definition, and the simulation results are used to iteratively redefine one or more of the set of tasks, the fleet resource configuration data structure, or the workflow until the simulation results satisfy a second fleet objective of the set of fleet objectives corresponding to the job request. The job execution plan generator generates a job execution plan based on the set of tasks, the fleet resource configuration data structure, and the workflow in response to the simulation results satisfying the set of fleet objectives.

[0019] In other features, the task definition system interacts with the intelligence layer to suggest alternative tasks that satisfy a second fleet objective. In other features, the task definition system interacts with the intelligence layer to optimize at least one of the robot type and the task objective based on the first fleet objective. In other features, the first fleet objective includes fleet resource utilization criteria. In other features, the task definition system receives from a fleet configuration proxy service a specific robot type to use in performing the robotic tasks. In other features, the task definition system configures a set of robotic tasks based on the specific robot type provided by the fleet configuration proxy service. In other features, the task definition system generates a data structure for each task in the set of tasks, for use by the workflow simulation system, the data structure including a digital twin for at least one of the tasks and a reference to at least one robot operating unit for performing the task. In other features, the task definition system generates, for each task in the set of tasks, a data structure identifying at least one of a robot type and a robot operating unit for performing the task and a configuration data structure for configuring the robot to perform the task. In other features, the task definition system generates a data structure for each task in the set of tasks and stores the data structures in a library of robot tasks indexed by information indicative of the job request and an identifier of the robot type and / or robot operating unit. In other features, the task definition system matches the requirements of the constraints specified in the job request with the capabilities of the robot in identifying a type of robot to satisfy the task objective. In other features, the task definition system generates multiple robot tasks for multiple different robot types to achieve the task objective.

[0020] In other features, the task definition system interacts with a fleet configuration proxy service to query a library of robotic tasks for candidate robotic tasks that satisfy the task objective and select a robotic task from the candidate robotic tasks based on at least one fleet objective. In other features, the at least one fleet objective is compatibility with available robot operating units. In other features, the task definition system interacts with a fleet intelligence layer to query a library of robotic tasks for candidate robotic tasks that satisfy the task objective and select a robotic task from the candidate robotic tasks based on the suitability of the candidate robotic tasks for achieving the task objective. In other features, the task definition system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining the set of tasks. In other features, the job workflow system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining a workflow of the robotic tasks. In other features, the job workflow system generates a workflow of the robotic tasks based on a dependency of a second task on a first task to satisfy an objective of the second task. In other features, a job workflow simulation system operates digital twins of tasks in a set of tasks to determine an optimized workflow order of the tasks.

[0021] A robot fleet management platform for configuring robotic fleet resources includes a set of one or more processors executing a set of computer-readable instructions. The set of one or more processors collectively constitute a job configuration system that receives a job request and determines a set of robotic tasks to be performed by the robotic fleet based on job content associated with the job request and at least one fleet objective in a set of fleet objectives. A fleet configuration proxy service applies the fleet configuration service to the set of robotic tasks and the job content to generate a fleet resource configuration data structure for the job request. A fleet intelligence layer operates a set of intelligence services to generate at least one recommended robot task and associated context information that facilitates robot selection and task sequencing in a workflow of the robotic tasks. A job workflow system generates a workflow that defines an execution order for the robotic tasks based on the fleet resource configuration data structure and the set of robotic tasks. The workflow simulation system is configured to simulate performance of the job based on the workflow and the job execution simulation environment, and generate simulation results that are used to recursively redefine one or more of the set of tasks, the fleet resource configuration data structure, or the workflow until the simulation results satisfy a second fleet objective of the set of fleet objectives corresponding to the job request. The job execution plan generator generates a job execution plan based on the set of tasks, the fleet resource configuration data structure, and the workflow in response to the simulation results satisfying the set of fleet objectives.

[0022] In other features, the job configuration system includes a job analysis system that applies content and structure filters to job content received in association with the job request to identify portions thereof suitable for robotic automation. In other features, the job configuration system includes a task definition system that establishes a set of robotic tasks, each defining at least a robot type and a task objective, the set of robotic tasks being based at least in part on portions of the job request that are suitable for robotic automation and that satisfy a first fleet objective of the set of fleet objectives. In other features, the fleet resource configuration data structure defines a set of task associations and a set of robot adaptation instructions. Each of the task associations associates at least one robotic operating unit with a respective robotic task of the set of robotic tasks, and the set of robot adaptation instructions defines how one or more robotic operating units of the robot fleet are adapted to perform respective tasks to which the robots are assigned. In other features, the workflow simulation system applies a workflow in a job execution simulation environment that includes digital models of the robotic operating units assigned to the robot fleet and the digital model of the task definition to generate simulation results. In other features, the job configuration system interacts with the intelligence layer to suggest alternative tasks that satisfy a second fleet objective. In other features, the job configuration system interacts with the intelligence layer to optimize at least one of the robot type and task objective based on at least one of the set of fleet objectives. In other features, the first fleet objective includes a fleet resource utilization criterion.

[0023] In other features, the job configuration system receives from a fleet configuration proxy service a specific robot type to use when performing the robotic task. In other features, the job configuration system configures a set of robotic tasks based on the specific robot type provided by the fleet configuration proxy service. In other features, the job configuration system generates a data structure for each task in the set of tasks, for use by the workflow simulation system, the data structure including a reference to a digital twin for at least one of the tasks and at least one robot operating unit for performing the task. In other features, the job configuration system generates, for each task in the set of tasks, a data structure identifying at least one of a type of robot and a robot operating unit for performing the task, and a configuration data structure for configuring the robot to perform the task. In other features, the job configuration system generates the data structure for each task in the set of tasks and stores the data structure in a library of robotic tasks indexed by information indicative of the job request and an identifier of at least one of the robot type and the robot operating unit. In other features, the job configuration system matches requirements and robot capabilities against constraints specified in the job request when identifying a type of robot to fulfill the task objective. In other features, the job configuration system generates a plurality of robot tasks for a plurality of different robot types to achieve the task objective. In other features, the job configuration system interacts with a fleet configuration proxy service to query a library of robot tasks for candidate robot tasks that satisfy the task objective and select a robot task from the candidate robot tasks based on at least one fleet objective.

[0024] In other features, at least one fleet objective is compatibility with available robot operating units. In other features, the job configuration system interacts with the fleet intelligence layer to query a library of robotic tasks for candidate robotic tasks that satisfy the task objective and select a robotic task from the candidate robotic tasks based on the suitability of the candidate robotic tasks for achieving the task objective. In other features, the job configuration system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining the set of tasks. In other features, the job workflow system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining a workflow of the robotic tasks. In other features, the job workflow system generates a workflow of the robotic tasks based on a second task dependency on a first task to satisfy a second task objective. In other features, the job workflow simulation system manipulates digital twins of tasks in the set of tasks to determine an optimized workflow order of the tasks.

[0025] The robot fleet management platform includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively executes: receiving a job request including descriptive information for a job deliverable and request-specific constraints for providing the job deliverable; applying content and structural filters to the received content associated with the job request to identify portions thereof suitable for robotic automation; establishing a set of robotic tasks, each defining at least a robot type and a task objective, based at least in part on portions of the job request suitable for robotic automation and that satisfy a first fleet objective; and applying a fleet configuration service to the job content and the set of robotic tasks to generate a fleet resource configuration data structure for the job request that associates at least one robotic operating unit with each task in the task set and, based on the at least one robotic operating unit, with robot adaptation instructions for performing the associated task. Also performed is using the fleet intelligence layer to recommend robot tasks and associated context information that facilitates robot selection and task sequencing in a workflow of the robot tasks. Also performed is generating a workflow of the robot tasks based on the fleet resource configuration data structure and the set of robot tasks. Also performed is simulating a digital model of a robot operating unit executing the digital model of the task definition, thereby validating the generated workflow and providing job execution simulation results for recursively establishing the set of robot tasks. Also performed is generating at least a first portion of an execution plan for the robot fleet resources configured in the fleet resource configuration data structure.

[0026] In other features, the robotic fleet management platform includes using a fleet intelligence layer to suggest alternative tasks that satisfy a second fleet objective. In other features, the robotic fleet management platform includes using the intelligence layer to optimize at least one of a robot type and a task objective based on the first fleet objective. In other features, the first fleet objective includes a fleet resource utilization criterion. In other features, the task definition system receives a specific robot type to use when performing the robotic task from a fleet configuration proxy service. In other features, establishing the set of robotic tasks is based on the specific robot type provided by the fleet configuration proxy service. In other features, establishing the set of robotic tasks includes generating, for use by the workflow simulation system, a data structure for each task in the set of tasks that includes a reference to a digital twin for at least one of the tasks and at least one robotic operating unit for performing the task. In other features, establishing the set of robotic tasks includes generating, for each task in the set of tasks, a data structure identifying at least one of a type of robot and a robotic operating unit for performing the task, and a configuration data structure for configuring the robot to perform the task. In other features, establishing the set of robotic tasks includes generating a data structure for each task in the set of tasks and storing the data structure in a library of robotic tasks indexed by information indicative of the job request and an identifier of at least one of a robot type and a robot operating unit.

[0027] In other features, establishing the set of robotic tasks includes matching requirements of constraints specified in the job request with robot capabilities when identifying a type of robot to satisfy the task objective. In other features, establishing the set of robotic tasks includes generating a plurality of robot tasks for a plurality of different robot types to achieve the task objective. In other features, establishing the set of robotic tasks includes querying a library of robotic tasks for candidate robotic tasks that satisfy the task objective and interacting with a fleet configuration proxy service to select a robotic task from the candidate robotic tasks based on at least one fleet objective. In other features, the at least one fleet objective is compatibility with available robot operating units. In other features, establishing the set of robotic tasks includes querying a library of robotic tasks for candidate robotic tasks that satisfy the task objective and interacting with a fleet intelligence layer to select a robotic task from the candidate robotic tasks based on the suitability of the candidate robotic tasks for achieving the task objective. In other features, establishing the set of robotic tasks includes referencing descriptive information from a sensor detection package indicating a preferred sequence of sensing tasks when defining the set of tasks. In other features, generating the workflow of the robotic tasks includes referencing descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks in defining the workflow of the robotic tasks. In other features, generating the workflow of the robotic tasks is based on a dependency of a second task on a first task to satisfy an objective of the second task. In other features, simulating the digital model of the robotic operating unit includes operating a digital twin of tasks in the set of tasks to determine an optimized workflow order of the tasks.

[0028] The robot fleet platform is for preparing job requests to facilitate configuration of a robot fleet operated by the robot fleet platform. The system includes one or more processors executing a set of computer-readable instructions. The one or more processors collectively execute a job request ingestion system configured to receive job content related to at least one of picking, packing, moving, storing, warehousing, transporting, or delivering a set of items in a supply chain, the job content including an electronic job request and associated data. A job content parsing system is configured to apply filters to the received job content to identify candidate portions for robotic automation. A fleet intelligence layer operates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information that facilitates robot selection and task sequencing in a robotic task workflow. A demand intelligence layer provides real-time information related to parameters of demand for a set of items in a supply chain. The job requirements system generates a job request instance-specific set of job requirements based on the portion of the job content indicative of robotic automation, the real-time information from the demand intelligence layer, and the at least one recommended robot task and associated context information, and the set of job requirements is stored in a non-transitory computer-readable memory accessible by at least one processor of the set of processors.

[0029] In other features, the job content analysis system retrieves a set of content and structural filters from a job configuration library that facilitates mapping between job content indicators and target terms indicative of robotic automation. In other features, the job content analysis system augments a set of default content and structural filters with filter criteria from the job configuration library that facilitates mapping between job content indicators and target terms indicative of robotic automation. In other features, the content filters indicate terms in the job content that distinguish robotic automation content from other content in the job content. In other features, the terms are retrieved from a job configuration library that facilitates mapping between job content indicators and terms indicative of robotic automation. In other features, the fleet intelligence layer facilitates sending portions of job content identified as suitable for robotic automation to a machine learning service of a set of intelligence services to improve analysis of the job content. In other features, the machine learning service is trained with a training dataset including human-generated feedback on job content analysis results for a plurality of job requests, a robotic automation knowledge base, a desired job-specific knowledge base, a technical dictionary, and content received from job experts. In other features, the job analysis system is configured to detect physical location information in the job content that facilitates automatically determining at least one of transportation options, operational constraints, permit requirements, transport restrictions, fleet assets local to the physical location of the job request, and logistics constraints.

[0030] In other features, the physical location information includes one or more of an address, a region, GPS data, an aerial photograph, a marked location on a map image, map coordinates, latitude, longitude, altitude, a route, and a depth relative to sea level. In other features, the job analysis system is configured to detect power information for at least one location in the job content, including a plurality of voltages, frequencies, currents, an availability schedule, a schedule of grid-provided electricity costs, a cost per kWh, a power demand profile, a maximum heat density, and proximity to at least one location. In other features, the job analysis system is configured to detect digital data representative of a layout of a portion of a job site present or referenced in the job content to facilitate generation of at least one job request instance-specific requirement related to the job site layout. In other features, the job analysis system is configured to detect at least one of information describing the operating environment, deliverables, an interface through which information about the job request is communicated with the job requester, wireless communication network accessibility, budget constraints for performing the task, and scheduling of resources for access and operation at the job site. In other features, the job request ingestion system is configured to scan the received job content for external links to related data. In other features, the job request ingestion system is configured to retrieve relevant data for use by the robotic fleet platform based on the external links. In other features, the job request ingestion system is configured to determine and forward to the job content analysis system portions of the received job content that include references to activities suitable for execution by the robotic fleet resources.

[0031] In another feature, the job request ingestion system is configured to process the received content with a job configuration indicator filter that automatically routes job configuration indicators in the job content to a job configuration library search service for classifying the job configuration indicators as one of a current job configuration, a previous job configuration, or an unknown job configuration. In another feature, the job content analysis system is configured to identify structure and content elements in the received content that facilitate identification of candidate robot tasks. In another feature, the job content analysis system is configured to identify structural elements in the received content that indicate at least one of a task, a subtask, a task order, a task dependency, and a task requirement to facilitate selection of a fleet robotic operating unit. In another feature, the job content analysis system is configured to identify content terms that indicate at least one robot minimum capability. In another feature, the job content analysis system is configured with a robot type filter that, when applied to the job request content, identifies terms that indicate a type of robot for performing the task. In another feature, the job request ingestion system includes a job request ingestion interface for receiving an electronic job request. In another feature, applying the content and structure filter includes scanning the received content for data indicative of robot activity. In other features, applying content and structure filters in the job content analysis system includes processing the received content with a robot type filter that, when applied to the job request content, identifies terms indicative of a type of robot for performing the task.

[0032] In other features, the job analysis system utilizes a content filter to detect qualified job data. In other features, the robotic fleet platform includes a qualified data query generation system configured to generate a query related to at least one element of qualified data in the job content for disambiguation thereof. In other features, the query related to the at least one element of qualified data is presented in a user interface. In other features, the query related to the at least one element of qualified data is provided to a fleet intelligence layer for processing with at least one intelligence service of the set of intelligence services to provide at least one disambiguation item of data related to the at least one element of qualified data through the fleet intelligence layer. In other features, the robotic fleet platform includes a qualified data resolution system configured to evaluate at least one qualified data element in the job content for similarity to disambiguated data from a plurality of job requests and, based on results of the evaluation, adjust the at least one qualified data element based on the similar disambiguated data elements. In other features, adjusting the at least one qualified data element includes replacing qualified data values ​​in the qualified data element with corresponding data values ​​from the disambiguated data element. In other features, the content filter is configured to identify qualified data including at least one of missing data, ambiguous data, and qualitative references. In other features, the fleet intelligence layer facilitates processing of the qualified data by a machine learning service of the set of intelligence services to improve analysis of the qualified data. In other features, the content filter is configured to identify qualified data and associated context to facilitate resolution of at least one of missing data, ambiguous data, and qualitative references in the qualified data.

[0033] The value chain network automation system includes a supply chain robot fleet dataset including a set of state and capability attributes of a set of robotic systems in a supply chain for a set of goods, a demand intelligence robotic process automation dataset including a set of state attributes of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods, and a coordination system that provides a set of robotic task instructions to the supply chain robot fleet based on processing of the supply chain robot fleet dataset and the demand intelligence robotic process automation dataset to coordinate supply and demand for the set of goods.

[0034] Further Summary of the Invention According to some embodiments of the present disclosure, methods and systems are provided herein for an information technology system that may include a cloud-based management platform with a microservices architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities, and a set of applications to enable management of a set of value chain network entities from an enterprise's point of origin to a customer's point of use.

[0035] Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, subsystems, goods, services, software, hardware, and other elements (sometimes collectively referred to as "platforms" or "systems," which terms should be understood to encompass any of the above unless the context requires otherwise) that individually or collectively improve the utilization of additive manufacturing capabilities in a network of value chain entities within a value chain network (such terms encompass many examples and embodiments disclosed herein and in documents incorporated herein by reference).

[0036] In embodiments, such methods and systems enable feedback and monitoring by customers and various other stakeholders throughout the modeling, printing, and supply chain processes, resulting in optimization of 3D printing parameters, achieving greater fidelity and accuracy in printing, and enhancing efficiency and traceability of the design process, manufacturing, supply chain demand management systems, products, product use cases, and the like.

[0037] Aspects provided herein include an information technology system having an artificial intelligence system for learning with a training set of results, parameters, and data collected from a set of distributed manufacturing network entities within a distributed manufacturing network and / or value chain network to optimize digital production processes and workflows.

[0038] Aspects provided herein include an information technology system for a distributed manufacturing network, the information technology system including: an additive manufacturing management platform configured to manage processes and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, supply chain, delivery, point of sale, and point of use stages; an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from distributed manufacturing network entities of the distributed manufacturing network to optimize the digital production processes and workflows; and a distributed ledger system integrated with a digital thread configured to provide a unified view of workflow and transaction information for entities in the distributed manufacturing network.

[0039] In an embodiment, the information technology system includes a control system configured to adjust, in real time, data and one or more parameters collected from the distributed manufacturing network entities.

[0040] In embodiments, the information technology system includes a digital twin system configured to build a digital twin of one or more distributed manufacturing network entities, where the digital twin provides a substantially real-time representation of the distributed manufacturing network entities through data from one or more sensors located within, on, or near the distributed manufacturing network entities. In embodiments, the digital twin may represent various parameters and attributes of the manufacturing entity (whether additive, subtractive, biological, chemical, or other entity), such as the types of materials it can handle, the current level of available source materials, processing / output rates, operating capabilities, biological manufacturing capabilities, vacuum processing capabilities, energy production and consumption information (e.g., for heating, laser processing, etc.), pricing parameters, etc. In embodiments, a platform, such as one using an artificial intelligence system, may run simulations on the digital twin or its predicted outputs to predict possible future states of the distributed manufacturing network entity and / or one or more of its outputs.

[0041] In an embodiment, the distributed manufacturing network entities include a set of printed parts, products, processes, additive manufacturing units such as 3D printers, other types of manufacturing units, actors (e.g., suppliers, manufacturers, financiers, users, customers, etc.), packagers, infrastructure, vehicles, and manufacturing nodes.

[0042] Aspects provided herein include a distributed manufacturing network including an additive manufacturing management platform comprising an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from a set of distributed manufacturing network entities to optimize manufacturing, supply chain, demand management, service, maintenance, and other processes and workflows, and a distributed ledger integrated with a digital thread of the distributed manufacturing network entities.

[0043] In embodiments, the distributed network entity is a part manufactured using additive manufacturing, and the digital thread comprises information related to the complete lifecycle of the part, from design, modeling, production, validation, use, and maintenance to disposal. In embodiments, the digital thread may include an instruction set for manufacturing the item, including additive manufacturing instructions, such as design specifications and / or operational parameters, by which one or more additive manufacturing units may be configured and operated to manufacture the item. In embodiments, the digital thread may include multiple alternative such instruction sets, such as those configured to facilitate manufacturing of the item through alternative forms of additive manufacturing and / or hybrids or combinations thereof with other additive manufacturing types and / or other manufacturing types. In embodiments, the instruction sets are embodied in a set of digital twins.

[0044] Aspects provided herein include an autonomous additive manufacturing platform including: a plurality of sensors disposed in, on, and / or near a product or part and configured to collect sensor data related to the product or part, wherein the sensor data is substantially real-time sensor data; an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, wherein the adaptive intelligence system is configured to input the sensor data to one or more machine learning models, wherein the sensor data is used as training data for the machine learning models, and the machine learning models are configured to convert the sensor data into simulation data; a digital twin system configured to create a product twin or a part twin based on the simulation data, wherein the product twin or part twin provides a substantially real-time representation of the product or part and allows for simulation of possible future states of the product or part via the simulation data; and an artificial intelligence system configured to run the simulation on the digital twin system; wherein the one or more models are utilized by the artificial intelligence system to perform classifications, predictions, recommendations, and / or to generate or facilitate decisions or instructions regarding the product and part (e.g., decisions or instructions governing design, configuration, material selection, shape selection, production type, job scheduling, etc.).

[0045] In an embodiment, the model trained by the machine learning system is utilized by the artificial intelligence system to run simulations on the part twin to predict expansion or contraction of the part, such as based on a physical model of expansion or contraction for the material simulated by the simulation.

[0046] In an embodiment, the model trained by the machine learning system is utilized by an artificial intelligence system to run simulations on the part twin to predict part warpage.

[0047] In an embodiment, the model trained by the machine learning system is utilized by the artificial intelligence system to run simulations on the part twin to calculate the changes required in the additive manufacturing process to compensate for shrinkage and warpage of the part, such as material selection, shape selection, interface selection, thermal management element selection or configuration, etc.

[0048] In embodiments, models trained by machine learning systems and / or other AI systems may perform simulations and generate or facilitate decisions or instructions based at least in part on expected use conditions, such as based on a customer's geographic location, indoor or outdoor use specifications, a set of weather and / or climate models, etc. For example, additive manufacturing of parts having the same intended use may be configured to use different materials, structural elements, or other elements based on whether the parts are intended to be used outdoors in very cold climates or indoors or in very hot environments. Thus, methods and systems are provided for application-aware, environment-aware, and customer-type-aware automated configuration of manufacturing instructions for parts or products involving automated manufacturing entities, such as additive manufacturing entities.

[0049] In embodiments, the model trained by the machine learning system is utilized by the artificial intelligence system to run simulations on the part twin to test the compatibility of the 3D printed part with other parts, with the system in which the part will be used, with infrastructure elements of the environment of use, with ambient conditions of the environment, with available tools, and / or with 3D printers or other additive manufacturing systems or other manufacturing systems available to produce the part.

[0050] In embodiments, the model trained by the machine learning system is utilized by an artificial intelligence system to run simulations on the part twin to predict deformations or defects in the 3D printed part. In embodiments, the model can also determine a set or sequence of process control parameter adjustments to implement corrective actions, such as adjusting layer dimensions or thicknesses, to correct the defects. In embodiments, the system can send a warning or error signal to an operator or user, or automatically abort the printing process.

[0051] In embodiments, the artificial intelligence system includes or is integrated with a machine vision system that uses a variable-focus, liquid-lens-based camera for image capture and defect detection. In embodiments, the artificial intelligence system operates on images captured with variable focal lengths, variable lighting settings, etc., to facilitate improvements in AI-based object recognition, boundary detection, item classification, material recognition, or other aspects related to the design, manufacture, or use of parts or other components. In embodiments, output from the integrated AI and variable-focus lens system is integrated with or incorporated into a digital twin representing a set of items, such as parts, captured by the system using the variable-focus lens.

[0052] In an embodiment, the model learned by the machine learning system is utilized by an artificial intelligence system to run simulations on the part twin to optimize the building process to minimize the occurrence of deformation.

[0053] In embodiments, the model trained by the machine learning system is utilized by the artificial intelligence system to run simulations on the product twin to predict the cost and / or price of the product or its components. The cost prediction may utilize inputs from marketplaces, outputs from search engines, cost models (such as enterprise procurement system models), costs presented in smart contracts, costs presented on websites, and other inputs, such as those related to additive manufacturing input material costs, additive manufacturing processing time costs, etc. The cost prediction may use inputs related to process costs, including energy costs, labor costs, etc. The price prediction can be based on similar inputs, such as public information from various sources indicating the current or historical market price of the product. The cost or price prediction may use inputs from the smart contract, such as smart contract parameters indicating current cost and pricing information provided in third-party contracts for materials, parts, etc.

[0054] Aspects provided herein include an information technology system for a distributed manufacturing network, the information technology system including: an additive manufacturing management platform comprising an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from a set of distributed manufacturing network entities and run simulations on digital twins of the distributed manufacturing network entities to make classification, prediction, and optimization-related decisions for the distributed manufacturing network entities; and a distributed ledger system integrated with a digital thread configured to provide a unified view of workflow and transaction information to entities in the distributed manufacturing network.

[0055] In an embodiment, digital manufacturing network entities include a set of printed parts, products, processes, additive manufacturing units such as 3D printers, other types of manufacturing units, actors (e.g., suppliers, manufacturers, financiers, users, customers, etc.), packagers, infrastructure, vehicles, and manufacturing nodes.

[0056] In embodiments, the artificial intelligence system runs simulations on one or more of the part twin, the product twin, and the printer twin to generate a 3D printing quote. In embodiments, the set of additive manufacturing quotes may be embodied in a smart contract, optionally linked to a blockchain, such that additive manufacturing operations may be contracted via the smart contract.

[0057] In embodiments, the artificial intelligence system runs simulations on one or more of the part twin, product twin, printer twin, or other twin to generate a set of printing or other additive manufacturing-related recommendations for a user of the platform. The recommendations may include recommendations regarding material type, printer or other additive manufacturing facility type, technology type, manufacturing service provider or source, manufacturing location, timing of scheduling an additive manufacturing job or steps therein, design parameters (e.g., among a set of possible designs), etc. In embodiments, the recommendations relate to material selection for printing. In embodiments, the recommendations relate to 3D printing technology selection.

[0058] In an embodiment, the artificial intelligence system runs simulations on one or more of the part twin, the product twin, and the printer twin to generate printing-related recommendations for users of the platform.

[0059] In an embodiment, the artificial intelligence system runs simulations on one or more of the part twin, the product twin, and the printer twin to predict delivery times for 3D printing jobs.

[0060] In an embodiment, the artificial intelligence system runs simulations on one or more of a part twin, a product twin, a printer twin, and a manufacturing node twin to predict cost overruns in the manufacturing process.

[0061] In an embodiment, the artificial intelligence system runs simulations on one or more of the part twin, product twin, printer twin, and manufacturing node twin to optimize production sequences of parts and products based on estimated prices, delivery times, sales margins, order sizes, or similar characteristics.

[0062] In an embodiment, the artificial intelligence system runs simulations on one or more of a part twin, a product twin, a printer twin, and a manufacturing node twin to optimize cycle time for manufacturing.

[0063] In an embodiment, the artificial intelligence system performs simulations on one or more of a part twin, a product twin, a printer twin, a customer twin, and a manufacturing node twin to forecast and manage product demand from one or more customers.

[0064] In an embodiment, an artificial intelligence system runs simulations on one or more of the twins to predict and manage the supply of a set of items from a digital manufacturing network.

[0065] In an embodiment, the artificial intelligence system runs simulations on one or more of the twins to optimize the production capacity of the distributed manufacturing network.

[0066] In an embodiment, the distributed manufacturing entity includes the following: an enterprise resource planning (ERP) system, a manufacturing execution system (MES), a product lifecycle management (PLM) system, a maintenance management system (MMS), a quality management system (QMS), a certification system, a compliance system, a robot / cobot (Cobot) system, and an SCCG system, which link to, use, get input from, or integrate with a set of other systems.

[0067] Aspects provided herein include a computer-implemented method for facilitating the production and delivery of 3D printed products to customers using one or more manufacturing nodes in a distributed manufacturing network, the method including: receiving one or more product requirements from a customer; tokenizing and storing the product requirements in a distributed ledger system; determining one or more manufacturing nodes, printers, processes, and materials based on the product requirements; generating a quote including a price and a delivery timeline; and, upon acceptance of the quote by the customer, producing and delivering the 3D printed product to the customer. In an embodiment, the quote is automatically generated and configured within a smart contract for additive manufacturing.

[0068] In an embodiment, determining includes matching customer orders with manufacturing nodes or 3D printers based on factors such as printer capabilities, location of the customer and manufacturing nodes, available capacity at each node, price and timeline requirements, and customer satisfaction scores.

[0069] In various embodiments, including entity matching, design selection, production type selection, material selection, recommendations, scheduling, etc., location-based decisions may include geofencing and other distance-based information, route-based information (e.g., considering traffic congestion and other factors that may affect delivery times), and other location-related information regarding distribution points, transportation facilities, points of sale, and / or points of use, such as infrastructure information, resource availability information, weather information, climate information, and many others. Location-based decisions may, for example, factor ambient temperature and other conditions at a location (or a combination of location and intended use) into selection of materials of manufacture, construction (e.g., considering potential expansion or contraction in extremes of high or low temperature), etc.

[0070] In an embodiment, the method further includes ranking the one or more manufacturing nodes based on a customer satisfaction score for meeting customer requirements.

[0071] In embodiments, the method may facilitate management of production workflow within and between one or more manufacturing nodes, thereby facilitating collaboration among the manufacturing nodes through the sharing of resources, capabilities, and intelligence. In embodiments, manufacturing nodes may collaborate for forecasting and prediction of material supply and product demand. In embodiments, manufacturing nodes may collaborate for design and product development. In embodiments, manufacturing nodes may collaborate for the manufacture and assembly of one or more parts of a product. In embodiments, manufacturing nodes may collaborate for the distribution and delivery of manufactured products.

[0072] In embodiments, the method may provide "manufacturing as a service" by leveraging unused capacity of one or more manufacturing nodes or 3D printers by opening up that capacity to one or more users seeking to manufacture 3D printed parts. In embodiments, the manufacturing as a service may be provided via smart contracts, optionally using blockchain and / or distributed ledgers. In embodiments, the manufacturing as a service may be governed and managed by an artificial intelligence system for configuring offerings, scheduling jobs, setting prices, setting other contractual terms, etc., for a set of additive manufacturing entities.

[0073] Aspects provided herein include a distributed manufacturing network that includes a distributed ledger system integrated with a digital thread of a set of distributed manufacturing network entities for storing information about events, activities, and transactions related to the distributed manufacturing network entities, and an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

[0074] In an embodiment, the distributed ledger system includes a distributed application downloadable by entities in the distributed manufacturing network.

[0075] In an embodiment, the distributed ledger system includes a user interface configured to provide a set of unified views of a workflow to a set of entities in a distributed manufacturing network.

[0076] In an embodiment, the distributed ledger system includes a user interface configured to provide tracking and reporting on the status and movement of a product from order, through manufacturing and assembly, to final delivery to the customer.

[0077] In an embodiment, the distributed ledger system includes a user interface configured to provide unified data collection from the metering systems.

[0078] In an embodiment, the distributed ledger system includes a system for digital rights management of entities in a distributed manufacturing network. In an embodiment, the distributed ledger system stores digital fingerprint information for documents / files and other information including creation and modification.

[0079] In embodiments, the distributed ledger system uses tokens, such as cryptocurrency tokens, to incentivize value creation and transfer value between entities in the distributed manufacturing network. For example, a unit of token may represent a defined amount of a certain type of manufacturing capacity, a defined amount of a certain type of material, a defined amount of available time, or other measurable amount of distributed manufacturing capacity. In embodiments, the tokens may constitute a mechanism for the exchange of value governed by a set of smart contracts.

[0080] In an embodiment, the distributed ledger system includes a system for attesting to the experience of a manufacturing node.

[0081] In an embodiment, the distributed ledger system includes a system for obtaining end-to-end traceability of a part.

[0082] In embodiments, the distributed ledger system includes a system for tracking all transactions, modifications, quality checks, and authentications of the distributed ledger.

[0083] In an embodiment, the distributed ledger system includes a system for verifying the capabilities of a manufacturing node.

[0084] In embodiments, the distributed ledger system includes or supports smart contracts for automating and managing workflow in a distributed manufacturing network.

[0085] In embodiments, the distributed ledger system includes or supports smart contracts for fulfilling purchase orders that cover the scope of work, estimates, timelines, and payment terms.

[0086] In embodiments, the distributed ledger system includes or supports smart contracts for processing payments by customers upon delivery of the product.

[0087] In embodiments, the distributed ledger system includes or supports smart contracts for processing insurance claims for defective products.

[0088] In embodiments, the distributed ledger system includes or supports smart contracts for processing warranty claims.

[0089] In embodiments, the distributed ledger system includes or supports smart contracts for automated execution and payment of maintenance.

[0090] Aspects provided herein include a distributed manufacturing network information technology system including a cloud-based additive manufacturing management platform having a user interface, connectivity facilities, data storage facilities, and monitoring facilities; a set of applications to enable the additive manufacturing management platform to manage distributed manufacturing network entities; and an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

[0091] In embodiments, the connectivity facility may include a network connection, an interface, a port, an application programming interface (API), a broker, a service, a connector, a wired or wireless communication link, a human accessible interface, a software interface, a microservice, a SaaS interface, a PaaS interface, an IaaS interface, a cloud function, or the like.

[0092] In embodiments, the artificial intelligence system provides optimization and process control throughout the manufacturing lifecycle, from product conception and design, through production and distribution, to sales, use, service, and maintenance.

[0093] In an embodiment, the artificial intelligence system provides generative design and topology optimization to determine at least one product design suitable for manufacture, suitable to meet customer needs, suitable to meet manufacturer specifications, or the like.

[0094] In an embodiment, the artificial intelligence system provides optimization of the build preparation process.

[0095] In an embodiment, the artificial intelligence system optimizes the part orientation process to achieve superior production results.

[0096] In an embodiment, an artificial intelligence system is provided to optimize toolpath generation.

[0097] In embodiments, the artificial intelligence system provides optimized dynamic 2D, 2.5D, and 3D nesting to maximize the number of printed parts while minimizing raw material waste.

[0098] In an embodiment, the user interface includes a dashboard that provides tracking and tracing of the production history of one or more 3D printed parts.

[0099] In an embodiment, the user interface includes a dashboard that provides batch traceability to identify parts from the same batch.

[0100] In an embodiment, the user interface includes a digital twin interface for resolving queries from users of the network related to parts or products.

[0101] In an embodiment, the user interface includes a virtual reality (VR) interface configured to enable a user to construct a 3D model in VR.

[0102] In an embodiment, the application is selected from the group consisting of a production management application, a production reporting application, a production analysis application, and a value chain management application.

[0103] In an embodiment, the application is an order tracking application configured to track product orders through their movement in a distributed manufacturing network.

[0104] In an embodiment, the application is a workflow management application configured to manage a complete 3D printing production workflow.

[0105] In an embodiment, the application is an alert and notification application configured to generate alerts, notifications, and reports regarding one or more events in the distributed manufacturing network to users or customers of the network. In an embodiment, the alert and notification application is configured to send alerts related to printing errors or failures to a user's computing device.

[0106] In an embodiment, the application is a payment gateway application configured to manage the entire billing, payment, and invoicing process for customers ordering products using a distributed manufacturing network.

[0107] In an embodiment, the artificial intelligence system is configured to automatically classify and cluster parts, such as those that can be additively manufactured, based on similarities in attributes, including physical attributes, shape, functional attributes, material attributes, performance attributes, economic attributes, etc.

[0108] In an embodiment, the artificial intelligence system is configured to analyze usage patterns associated with one or more users and learn user preferences regarding materials, directions, and / or printing strategies.

[0109] In an embodiment, the artificial intelligence system is configured to minimize material waste generation during the additive manufacturing process.

[0110] In an embodiment, the artificial intelligence system is configured to optimize material utilization during the additive manufacturing process, including providing an instruction set that takes into account waste generation and material recapture or recycling.

[0111] In an embodiment, the artificial intelligence system is configured to optimize a combination of material usage, energy usage, and other resource usage during an additive manufacturing process, such as by factoring energy and labor costs into the optimization of the instruction set.

[0112] In an embodiment, an artificial intelligence system is configured to manage real-time dynamics that affect inventory levels for smart inventory and material management in a distributed manufacturing network.

[0113] In an embodiment, the artificial intelligence system is configured to build, maintain, and provide a library of parts with pre-set parameters, searchable by material, property, function, device compatibility, form fit, interface compatibility, part type, part class, industry, and compliance.

[0114] In embodiments, the artificial intelligence system uses algorithms including artificial neural networks, decision trees, logistic regression models, stochastic gradient descent models, fuzzy classifiers, support vector machines, Bayesian networks, hierarchical clustering algorithms, k-means algorithms, genetic algorithms, deep learning systems, supervised learning systems, semi-supervised learning systems, deep convolutional neural networks, deep recurrent neural networks, or any combination thereof. In embodiments, the artificial intelligence system (in any of the embodiments described herein) may use any of the artificial intelligence types described herein or in the documents incorporated herein by reference. In embodiments, the artificial intelligence system (in any embodiment described herein) may utilize training datasets that may include, among other things, one or more of the following: a set of expert actions or operations on information; process and / or workflow data; a set of models of various types; a set of results (such as from additive manufacturing processes, from utilization of additive manufacturing output, from workflows and operations, and / or from related economic activities including sales and service activities); sensor datasets; information from public sources (such as search engine results, news feeds, website information, social media information, traffic data, weather data, climate data, demographic data, geospatial data, etc.); information from enterprise or other databases and information technology systems; information from crowdsourcing; Internet of Things information; and / or other data sources or inputs.

[0115] In an embodiment, the distributed manufacturing network information technology system is configured to provide a 3D printed product that conforms to a body part or anatomical structure of a user, and the 3D printed product is a wearable selected from the group consisting of eyewear, footwear, earwear, and headgear.

[0116] Aspects provided herein include an information technology system for supporting additive manufacturing and value chain workflows, comprising a cloud-based metal additive manufacturing management platform including an artificial intelligence system configured to learn with a training set of results, parameters, and data collected from one or more additive manufacturing nodes to optimize additive manufacturing and value chain processes and workflows; and a distributed ledger system configured to store data related to the manufacturing nodes.

[0117] In an embodiment, the artificial intelligence system learns from a training set of results, parameters, and data collected from one or more additive manufacturing nodes to optimize process and material selection for additive manufacturing.

[0118] In an embodiment, the artificial intelligence system learns from a training set of results, parameters, and data collected from one or more additive manufacturing nodes to optimize the formulation of feedstocks for additive manufacturing.

[0119] In an embodiment, an artificial intelligence system learns from a training set of results, parameters, and data collected from one or more additive manufacturing nodes to optimize part designs for additive manufacturing.

[0120] In an embodiment, the artificial intelligence system learns from a training set of results, parameters, and data collected from one or more additive manufacturing nodes to predict and manage risks associated with the production or delivery of parts or products to customers by one or more manufacturing nodes.

[0121] In an embodiment, the artificial intelligence system learns from a training set of results, parameters, and data collected from one or more additive manufacturing nodes and provides personalized marketing and customer service regarding parts or products manufactured by the one or more manufacturing nodes and delivered to customers.

[0122] Liquid Lens Summary

[0123] Provided herein are methods, systems, components, processes, modules, blocks, circuits, subsystems, articles, services, software, hardware, and other elements (sometimes collectively referred to as a "platform" or a "system," which terms should be understood to encompass any of the above unless the context indicates otherwise) that individually or collectively improve the vision capabilities in a value chain network or network of value chain entities in a VCN (this term encompasses many examples and embodiments disclosed herein and in documents incorporated herein by reference) to enhance the vision capabilities of the VCN network.

[0124] Aspects provided herein include a dynamic vision system having an artificial intelligence system for learning with a training set of results, parameters, and data collected from a variable focus liquid lens optical assembly to recognize objects.

[0125] Embodiments provided herein include a dynamic vision system including: a variable focus liquid lens optical assembly; a control system configured to adjust in real time one or more optical parameters and data collected from the optical assembly; and a processing system that dynamically learns with a training set of results, parameters, and data collected from the optical assembly to train a machine learning model to recognize objects and / or environments.

[0126] In embodiments, the variable focus liquid lens can be continuously adjusted by a control system based on environmental factors and feedback from a processing system to generate an object concept, which in embodiments includes contextual intelligence about the object and its environment, providing superior object recognition by a dynamic vision system.

[0127] In an embodiment, the processing system can receive an adjustable data stream in real time or near real time from the variable focus liquid lens optical assembly to generate situational awareness or to generate out-of-focus images of an object to capture rich metadata and contextual intelligence about the object and its environment.

[0128] In an embodiment, the control system and processing system may be integrated with the variable focus liquid lens optical assembly.

[0129] In an embodiment, the optical parameters adjusted by the control system include focal length, liquid material, specularity, color, environment, lens shape, or other types of parameters, which in turn affect spherical aberration, field curvature, coma, chromatic aberration, distortion, vignetting, ghosting, flare, diffraction, and / or some other characteristic.

[0130] In an embodiment, the processing system can be trained with a set of results, parameters, and data from the liquid lens optical assembly to derive a configuration of the liquid lens optical assembly, which may include the liquid lens material, geometry, shape, optical properties, performance, and design.

[0131] Aspects provided herein include a robotic vision system including an optical assembly including one or more sensors, a variable focus liquid lens, and a photon capture board; and a processing system configured to dynamically learn with a training set of results, parameters, and data collected from the optical assembly to train an artificial intelligence model to recognize objects. In embodiments, the robotic vision system further includes a control system configured to adjust one or more optical parameters and data collected from the optical assembly in real time.

[0132] In an embodiment, an artificial intelligence model is trained to make classification, prediction, or optimization-related decisions about objects.

[0133] In an embodiment, an artificial intelligence model can determine the position, orientation, and movement of an object.

[0134] In an embodiment, the artificial intelligence model may be a neural network.

[0135] In embodiments, the artificial intelligence model can build a three-dimensional representation of an object in a single step or multiple steps, without the intermediate step of processing it into a two-dimensional image.

[0136] In embodiments, the one or more sensors may include a camera, LIDAR, RADAR, SONAR, thermal imaging sensor, hyperspectral imaging sensor, illuminance sensor, force sensor, torque sensor, speed sensor, acceleration sensor, position sensor, proximity sensor, gyro sensor, sound sensor, motion sensor, location sensor, load sensor, temperature sensor, touch sensor, depth sensor, ultrasonic range sensor, infrared sensor, chemical sensor, magnetic sensor, inertial sensor, gas sensor, humidity sensor, pressure sensor, viscosity sensor, flow sensor, object sensor, tactile sensor, or other type of sensor.

[0137] In an embodiment, the processing system can use conditional probabilities to temporally combine the outputs from two or more sensors to create a combined view of the object that is richer and includes information about the object's position, orientation, and motion.

[0138] Embodiments provided herein include a vision system for dynamically learning an object concept for an object of interest, the vision system including: a variable focus liquid lens assembly; a control system configured to adjust one or more optical parameters of the variable focus liquid lens assembly in real time; one or more vision sensors configured to capture a real time pixel array based on data received from the variable focus liquid lens assembly in response to adjustments by the control system, the pixel array representing the object concept; and an adaptive intelligence system configured to process the object concept to construct a three-dimensional representation of the object; the adaptive intelligence system including a machine learning system configured to input the object concept into one or more machine learning models, the object concept being used as training data for the machine learning models; and an artificial intelligence system configured to make classifications, predictions, and other decisions about the object, including determining the position, orientation, and movement of the object.

[0139] Aspects provided herein include a method for recognizing an object, the method including receiving, at a sensor, a real-time adjustable data stream representing visual and contextual information about an object of interest; generating, by an image processing system, an object concept including contextual intelligence about the object and its environment; adjusting, by a control system, optical parameters of a conformable liquid lens; modifying, by a machine learning system, the object concept in response to the adjustment of the optical parameters of the conformable liquid lens; and determining, by an artificial intelligence system, object attributes including object classification, depth, position, orientation, and movement; wherein the object concept is constantly modified in response to the adjustment of the optical parameters of the conformable liquid lens and is used as input to train a machine learning model that dynamically learns with a training set of results, parameters, and data collected from the conformable liquid lens.

[0140] Here's a summary of Robo

[0141] This disclosure relates to a fleet management platform capable of organizing, deploying, and controlling special-purpose, multipurpose, and other classes of robots. Such a platform, capable of securely delivering reliable contract services, is one key to unlocking the value-creation potential of autonomous robots. This value proposition can be further amplified when highly configurable robots are designed with state-of-the-art capabilities and incorporate advanced artificial intelligence; when the platform has the intelligence and computational power to integrate data from various sources, such as deployed robots, value chain network (VCN) entities across a wide range of supply chain activities (e.g., picking, packing, moving, storing, warehousing, transporting, and delivering) and demand-related activities (e.g., marketing, sales, advertising, forecasting, pricing, location, deployment, and design), ERP systems, and smart contracts; and when the platform learns from and manages performance based on operational results.

[0142] A more complete understanding of the present disclosure will be obtained from the following description and accompanying drawings, and the appended claims. All documents referenced herein are incorporated by reference. [Brief explanation of the drawings]

[0143] The accompanying drawings, which are included to provide a better understanding of the present disclosure, illustrate embodiments of the disclosure and, together with the description, serve to explain many aspects of the disclosure.

[0144] [Figure 1] FIG. 1 is a block diagram illustrating the prior art relationships of various entities and facilities in a supply chain.

[0145] [Figure 2] FIG. 2 is a block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0146] [Figure 3] FIG. 3 is another block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0147] [Figure 4] FIG. 4 is a block diagram illustrating the components and interrelationships of the systems and processes of the digital products network of FIGS. 2 and 3 according to the present disclosure.

[0148] [Figure 5] FIG. 5 is a block diagram illustrating the components and interrelationships of the systems and processes of a value chain network technology stack according to the present disclosure.

[0149] [Figure 6]FIG. 6 is a block diagram illustrating a platform and relationships for orchestrating the control of various entities in a value chain network according to the present disclosure.

[0150] [Figure 7] FIG. 7 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0151] [Figure 8] FIG. 8 is a block diagram illustrating components and relationships of value chain entities managed by an embodiment of a value chain network management platform according to the present disclosure.

[0152] [Figure 9] FIG. 9 is a block diagram illustrating the network relationships of entities in a value chain network according to the present disclosure.

[0153] [Figure 10] FIG. 10 is a block diagram illustrating a set of applications supported by a unified data processing layer in a value chain network management platform according to the present disclosure.

[0154] [Figure 11] FIG. 11 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0155] [Figure 12] FIG. 12 is a block diagram illustrating components and relationships of the data storage layer in an embodiment of a value chain network management platform according to the present disclosure.

[0156] [Figure 13]FIG. 13 is a block diagram illustrating the components and relationships of the adaptive intelligent system layer in an embodiment of the value chain network management platform according to the present disclosure.

[0157] [Figure 14] FIG. 14 is a block diagram depicting providing an adaptive intelligence system for collaborative intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0158] [Figure 15] FIG. 15 is a block diagram illustrating providing a hybrid adaptive intelligence system for collaborative intelligence for a set of supply and demand applications or a category of goods in accordance with the present disclosure.

[0159] [Figure 16] FIG. 16 is a block diagram depicting providing an adaptive intelligence system for predictive intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0160] [Figure 17] FIG. 17 is a block diagram depicting providing an adaptive intelligence system for classification intelligence for a set of supply and demand applications for categories of goods in accordance with the present disclosure.

[0161] [Figure 18] FIG. 18 is a block diagram depicting providing an adaptive intelligence system for generating automatic control signals for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0162] [Figure 19]FIG. 19 is a block diagram illustrating training an artificial intelligence / machine learning system to generate intelligent routing recommendations for a selected value chain network in accordance with the present disclosure.

[0163] [Figure 20] FIG. 20 is a block diagram illustrating a semi-sensory problem recognition system for recognizing pain points / problem states in a value chain network according to the present disclosure.

[0164] [Figure 21] FIG. 21 is a block diagram illustrating a collection of artificial intelligence systems operating on value chain information to enable automated adjustment of an enterprise's value chain activities in accordance with the present disclosure.

[0165] [Figure 22] FIG. 22 is a block diagram illustrating the components and relationships involved in integrating a set of digital twins in one embodiment of a value chain network management platform according to the present disclosure.

[0166] [Figure 23] FIG. 23 is a block diagram illustrating a set of digital twins involved in an embodiment of a value chain network management platform according to the present disclosure.

[0167] [Figure 24] FIG. 24 is a block diagram illustrating components and relationships of an entity discovery and management system in an embodiment of a value chain network management platform according to the present disclosure.

[0168] [Figure 25] FIG. 25 is a block diagram illustrating components and relationships of a robotic process automation system in an embodiment of a value chain network management platform according to the present disclosure.

[0169] [Figure 26] FIG. 26 is a block diagram illustrating the components and relationships of a collection of opportunity miners in one embodiment of a value chain network management platform according to the present disclosure.

[0170] [Figure 27] FIG. 27 is a block diagram illustrating the components and relationships of a set of edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0171] [Figure 28] FIG. 28 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform according to the present disclosure.

[0172] [Figure 29] FIG. 29 is a block diagram illustrating additional details of components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0173] [Figure 30] FIG. 30 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0174] [Figure 31] FIG. 31 is a block diagram illustrating the components and relationships of a unified database in one embodiment of a value chain network management platform according to the present disclosure.

[0175] [Figure 32] FIG. 32 is a block diagram illustrating components and relationships of a set of unified data collection systems in an embodiment of a value chain network management platform according to the present disclosure.

[0176] [Figure 33] FIG. 33 is a block diagram illustrating the components and relationships of a set of Internet of Things monitoring systems in an embodiment of the value chain network management platform of the present disclosure.

[0177] [Figure 34] FIG. 34 is a block diagram illustrating the components and relationships of a machine vision system and a digital twin in an embodiment of a value chain network management platform according to the present disclosure.

[0178] [Figure 35] FIG. 35 is a block diagram illustrating components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0179] [Figure 36] FIG. 36 is a block diagram illustrating additional details of components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0180] [Figure 37] FIG. 37 is a block diagram illustrating the components and relationships of a set of unified adaptive intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0181] [Figure 38] FIG. 38 is a schematic diagram of a system configured to train an artificial system utilized by a value chain system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0182] [Figure 39]FIG. 39 is a schematic diagram of a system configured to train an artificial system utilized by a container fleet management system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0183] [Figure 40] FIG. 40 is a schematic diagram of a system configured to train an artificial system utilized by a logistics design system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0184] [Figure 41] FIG. 41 is a schematic diagram of a system configured to train an artificial system utilized by a packaging design system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0185] [Figure 42] FIG. 42 is a schematic diagram of a system configured to train an artificial system utilized by a waste mitigation system using real-world outcome data and a digital twin system, according to some embodiments of the present disclosure.

[0186] [Figure 43] FIG. 43 is a schematic diagram illustrating an example of portions of an information technology system for value chain artificial intelligence utilizing digital twins according to some embodiments of the present disclosure.

[0187] [Figure 44] FIG. 44 is a block diagram illustrating the components and relationships of a set of intelligent project management equipment in an embodiment of a value chain network management platform according to the present disclosure.

[0188] [Figure 45]FIG. 45 is a block diagram illustrating the components and relationships of an intelligent task recommendation system in an embodiment of a value chain network management platform according to the present disclosure.

[0189] [Figure 46] FIG. 46 is a block diagram illustrating components and relationships of a routing system between nodes of a value chain network in an embodiment of a value chain network management platform according to the present disclosure.

[0190] [Figure 47] FIG. 47 is a block diagram illustrating the components and relationships of a dashboard for managing a set of digital twins in an embodiment of a value chain network management platform.

[0191] [Figure 48] Figure 48 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform using a microservices architecture.

[0192] [Figure 49] FIG. 49 is a block diagram illustrating the components and relationships of an Internet of Things data collection architecture and sensor recommendation system in an embodiment of a value chain network management platform.

[0193] [Figure 50] FIG. 50 is a block diagram illustrating components and relationships of a social data collection architecture in an embodiment of a value chain network management platform.

[0194] [Figure 51] FIG. 51 is a block diagram illustrating components and relationships of a crowdsourcing data collection architecture in an embodiment of a value chain network management platform.

[0195] [Figure 52] FIG. 52 is a perspective view depicting an embodiment of a set of value chain network digital twins representing virtual models of a set of value chain network entities according to the present disclosure.

[0196] [Figure 53] FIG. 53 is a perspective view illustrating an embodiment of a warehouse digital twin kit system according to the present disclosure.

[0197] [Figure 54] FIG. 54 is a perspective view illustrating an embodiment of a stress test performed on a value chain network in accordance with the present disclosure.

[0198] [Figure 55] FIG. 55 is a perspective view illustrating an embodiment of a method used by a machine to detect failures and predict any future failures of the machine in accordance with the present disclosure.

[0199] [Figure 56] FIG. 56 is a perspective view illustrating an embodiment of a deployment of machine twins to perform predictive maintenance on a set of machines according to the present disclosure.

[0200] [Figure 57] FIG. 57 is a schematic diagram illustrating an example of a portion of a system for a value chain customer digital twin and a customer profile digital twin according to some embodiments of the present disclosure.

[0201] [Figure 58] FIG. 58 is a schematic diagram illustrating an example of an advertising application interfacing with an adaptive intelligent systems layer according to the present disclosure.

[0202] [Figure 59] FIG. 59 is a schematic diagram illustrating an example of an e-commerce application integrated with an adaptive intelligent systems layer according to the present disclosure.

[0203] [Figure 60] FIG. 60 is a schematic diagram illustrating an example of a demand management application integrated with an adaptive intelligent systems layer according to the present disclosure.

[0204] [Figure 61] FIG. 61 is a schematic diagram illustrating an example of a portion of a system for value chain smart supply part digital twin according to some embodiments of the present disclosure.

[0205] [Figure 62] FIG. 62 is a schematic diagram illustrating an example of a risk management application interfacing with an adaptive intelligent systems layer according to the present disclosure.

[0206] [Figure 63] FIG. 63 is a perspective view of maritime assets associated with a value chain network management platform including port infrastructure components according to the present disclosure.

[0207] [Figure 64] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including vessel components according to the present disclosure. [Figure 65] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including vessel components according to the present disclosure.

[0208] [Figure 66] FIG. 66 is a perspective view of a maritime asset associated with a value chain network management platform including a barge component in accordance with the present disclosure.

[0209] [Figure 67]FIG. 67 is a perspective view of maritime assets associated with a value chain network management platform, including those involved in maritime events, legal proceedings, and utilizing geofence parameters, in accordance with the present disclosure.

[0210] [Figure 68] FIG. 68 is a schematic diagram illustrating an example environment of an enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0211] [Figure 69] FIG. 69 is a schematic diagram illustrating an example set of components of an enterprise control tower and management platform according to some embodiments of the present disclosure.

[0212] [Figure 70] FIG. 70 is a schematic diagram illustrating an example of an enterprise data model according to some embodiments of the present disclosure.

[0213] [Figure 71] FIG. 71 is a diagram illustrating examples of different types of enterprise digital twins, including executive digital twins, associated with the data, processing, and application layers of an enterprise digital twin framework according to some embodiments of the present disclosure.

[0214] [Figure 72] FIG. 72 is a schematic diagram illustrating an illustrative example of an enterprise and executive control tower and management platform according to some embodiments of the present disclosure.

[0215] [Figure 73] Figure 73 is a flowchart illustrating an example set of operations for configuring and delivering an enterprise digital twin.

[0216] [Figure 74]Figure 74 illustrates an example set of operations for how to configure a digital twin of an organization.

[0217] [Figure 75] FIG. 75 illustrates an example set of operations for a method of creating an executive digital twin.

[0218] [Figure 76] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 77] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 78] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 79]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 80] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 81] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 82] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 83]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 84] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 85] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 86] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 87]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 88] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 89] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 90] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 91]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 92] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 93] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 94] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 95]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 96] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 97] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 98] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 99]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 100] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 101] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 102] 76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 103]76 through 103 are schematic diagrams of embodiments of neural net systems that enable intelligent transactions, including those involving expert systems, self-organization, machine learning, and artificial intelligence, and that can be connected, integrated, and accessed by a platform including neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure.

[0219] [Figure 104] FIG. 104 is a schematic diagram illustrating an example intelligence service system according to some embodiments of the present disclosure.

[0220] [Figure 105] FIG. 105 is a schematic diagram illustrating an example neural network having multiple layers, according to some embodiments of the present disclosure.

[0221] [Figure 106] FIG. 106 is a schematic diagram illustrating an example convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0222] [Figure 107] FIG. 107 is a schematic diagram illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.

[0223] [Figure 108] FIG. 108 is a schematic diagram illustrating an example of a reinforcement learning-based approach for performing one or more tasks by a mobile system according to some embodiments of the present disclosure.

[0224] [Figure 109] FIG. 109 is a schematic diagram illustrating an exemplary physical direction determination chip according to some embodiments of the present disclosure.

[0225] [Figure 110] FIG. 110 is a schematic diagram illustrating an exemplary network reinforcement chip according to some embodiments of the present disclosure.

[0226] [Figure 111] FIG. 111 is a schematic diagram illustrating an exemplary diagnostic chip according to some embodiments of the present disclosure.

[0227] [Figure 112] FIG. 112 is a schematic diagram illustrating an exemplary governance chip according to some embodiments of the present disclosure.

[0228] [Figure 113] FIG. 113 is a schematic diagram illustrating an example prediction, classification, and recommendation chip according to some embodiments of the present disclosure.

[0229] [Figure 114] FIG. 114 is a perspective view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure.

[0230] [Figure 115] FIG. 115 is a schematic diagram illustrating an exemplary embodiment of an autonomous additive manufacturing platform for automating and optimizing digital production workflows for metal additive manufacturing, according to some embodiments of the present disclosure.

[0231] [Figure 116] FIG. 116 is a flow diagram illustrating the optimization of different parameters of an additive manufacturing process according to some embodiments of the present disclosure.

[0232] [Figure 117] FIG. 117 is a schematic diagram illustrating a system for learning with data from an autonomous additive manufacturing platform to train an artificial learning system that uses a digital twin for classification, prediction, and decision-making, according to some embodiments of the present disclosure.

[0233] [Figure 118] FIG. 118 is a schematic diagram illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.

[0234] [Figure 119] FIG. 119 is a diagram illustrating an example implementation of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub-processes including process and material selection, hybrid part workflow, raw material formulation, part design optimization, risk prediction and management, marketing and customer service, according to some embodiments of the present disclosure.

[0235] [Figure 120] FIG. 120 is a perspective view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system, according to some embodiments of the present disclosure.

[0236] [Figure 121] FIG. 121 is a schematic diagram illustrating an example implementation of a distributed manufacturing network in which digital thread data is tokenized and stored in a distributed ledger to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network, according to some embodiments of the present disclosure.

[0237] [Figure 122] FIG. 122 is a perspective view of an example of a conventional computer vision system for creating an image of an object of interest.

[0238] [Figure 123] FIG. 123 is a schematic diagram illustrating an example implementation of a dynamic vision system for dynamically learning object concepts for objects of interest, according to some embodiments of the present disclosure.

[0239] [Figure 124]FIG. 124 is a schematic diagram illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0240] [Figure 125] FIG. 125 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.

[0241] [Figure 126] FIG. 126 is a schematic diagram illustrating an example implementation of a dynamic vision system for modeling, simulating, and optimizing various optical, mechanical, design, and lighting parameters of a dynamic vision system according to some embodiments of the present disclosure.

[0242] [Figure 127] FIG. 127 is a schematic diagram illustrating an example implementation of a dynamic vision system detailing various components, along with integration of the dynamic vision system with one or more third party systems according to some embodiments of the present disclosure.

[0243] [Figure 128] FIG. 128 is a schematic diagram illustrating an example of an environment for a fleet management platform according to some embodiments of the present disclosure.

[0244] [Figure 129] FIG. 129 is a schematic diagram illustrating example configurations of multi-purpose robots and special-purpose robots according to some embodiments of the present disclosure.

[0245] [Figure 130] FIG. 130 is a schematic diagram illustrating an example of a platform-level intelligence layer of a fleet management platform according to some embodiments of the present disclosure.

[0246] [Figure 131] FIG. 131 is a schematic diagram illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.

[0247] [Figure 132] FIG. 132 is a schematic diagram illustrating an example security framework according to some embodiments of the present disclosure.

[0248] [Figure 133] FIG. 133 is a schematic diagram illustrating an example of an environment for a fleet management platform according to some embodiments of the present disclosure.

[0249] [Figure 134] FIG. 134 is a schematic diagram illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure.

[0250] [Figure 135] FIG. 135 is a schematic diagram illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.

[0251] [Figure 136] FIG. 136 is a schematic diagram illustrating an example job analysis system and task definition system and example data flow thereof according to some embodiments of the present disclosure.

[0252] [Figure 137] FIG. 137 is a schematic diagram illustrating an example fleet configuration system and its example data flow according to some embodiments of the present disclosure.

[0253] [Figure 138] FIG. 138 is a schematic diagram illustrating an example workflow definition system and its example data flow according to some embodiments of the present disclosure.

[0254] [Figure 139] FIG. 139 is a schematic diagram illustrating an example configuration of a multipurpose robot and its components according to some embodiments of the present disclosure.

[0255] [Figure 140] FIG. 140 is a schematic diagram illustrating an example architecture of a robotic control system according to some embodiments of the present disclosure.

[0256] [Figure 141] FIG. 141 is a schematic diagram illustrating an example architecture of a robotic control system 12150 that utilizes data from multiple sensors in a vision and sensing system, according to some embodiments of the present disclosure.

[0257] [Figure 142] FIG. 142 is a schematic diagram illustrating an exemplary vision and sensing system of a robot according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0258] Over time, companies have increasingly used technology solutions to improve outcomes associated with traditional supply chains, such as those depicted in Figure 1, including software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods as they move through the supply chain, and navigation and routing systems for improving route selection efficiency. However, several major trends are forcing manufacturers, retailers, and other companies to improve their supply chain performance. First, online and e-commerce operators, particularly Amazon®, have become the largest retail channel for many categories of goods, deploying distribution and fulfillment centers112 across some regions, such as the United States, that house hundreds of thousands, and in some cases even more, of product categories (SKUs), allowing customers to receive their ordered goods the next day, or even the same day (sometimes delivered to their door by drones, robots, and / or autonomous vehicles). For retailers without widespread geographic distribution of fulfillment centers and warehouses, customer expectations for speed of delivery increase pressure on supply chain efficiency and optimization. Thus, a need remains for improved supply chain methods and systems.

[0259] Second, agile manufacturing capabilities such as 3D printing and robotic assembly, customer profiling techniques, and online ratings and reviews are driving customer expectations for product customization and personalization. Therefore, to stay ahead of the competition, manufacturers and retailers need to improve their methods and systems for understanding, predicting, and satisfying customer demand.

[0260] Historically, supply chain management and demand planning and management have been largely separate activities that converge primarily when demand is converted into orders and passed to the supply side for fulfillment in the supply chain. Increasing expectations for speed and personalization require methods and systems that enable integrated orchestration of supply and demand.

[0261] Parallel to these trends has been the emergence of the Internet of Things, with some products, particularly smart home products such as thermostats, lighting systems, and speakers, increasingly incorporating on-board network connectivity and processing power, often enabling voice-controlled intelligent agents such as Alexa® or Siri® to control devices, launch specific application functions like playing music, or even order products. In some cases, smart products 650 may even initiate orders, such as a printer ordering cartridge refills. While intelligent products 650 may participate in collaborative systems, such as an Amazon® Echo® product controlling a television or a sensor-enabled thermostat or security camera connecting to a mobile device, most intelligent products still participate in a largely isolated set of application-specific interactions. As artificial intelligence capabilities improve and more computing and networking power moves to the supply environment 670, the demand environment 672, and to network-enabled edge devices and systems that reside at all the locations, systems, and facilities along the product 650 path from the manufacturer's loading dock to the customer 662 or retailer 664 destination 612, there is a need and opportunity to dramatically improve the intelligence, control, and automation of all elements involved in supply and demand.

[0262] [Value Chain Network] Referring to FIG. 2, a block diagram illustrating the components and interrelationships of value chain network systems and processes is shown at 200. In an exemplary embodiment, "value chain network," as used herein, refers to the elements and interconnections of historically separate demand management systems and processes and supply chain management systems and processes, made possible by the development and convergence of many diverse technologies. In an exemplary embodiment, a value chain control tower 260 (e.g., sometimes referred to herein as a "value chain network management platform," "VCNP," or simply the "system," or "platform") may be connected to, in communication with, or otherwise operatively coupled to data processing facilities, including, but not limited to, big data centers (e.g., big data processing 230, as described below), and associated processing capabilities that receive data flows, data pools, data streams, and / or other data configurations and transmission modalities received from, for example, a digital product network 252, a customer directly (e.g., a direct-connect customer 250), or some other third party 220. Communications related to market orchestration activities and communications 210, analytics 232, or any other type of input may also be utilized by the value chain control tower for demand enrichment 262, synchronized planning 234, intelligent sourcing 238, dynamic fulfillment 240, or any other smart operations informed by collaborative and adaptive intelligence, as described herein.

[0263] Referring to Figure 3, another block diagram illustrating the components and interrelationships of value chain network systems and processes, as well as associated use cases, data processing, and related entities, is shown. In an exemplary embodiment, a value chain control tower 360 may coordinate market orchestration activities 310, including, but not limited to, demand curve management 352, ecosystem synchronization 348, intelligent sourcing 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In an exemplary embodiment, the value chain control tower 360 may be connected to, otherwise coupled with, or in communication with, or other operationally coupled processing facilities, which may further couple with, communicate with, or be operationally coupled to, an adaptive data pipeline 302 and external data sources 320 and a data handling stack 330 (e.g., value chain network technology), which may include an intelligent user-adaptive interface, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 may also be further connected to, in communication with, or otherwise operatively coupled to additional value chain entities, including, but not limited to, a digital product network 360, customers (e.g., designated connected customers 362), and / or other connected operations 364 and value chain network entities.

[0264] [Digital Product Networks (DPN)] Referring to FIG. 4, a block diagram illustrating the components and interrelationships of systems and processes of a digital product network at 400 is shown. In an exemplary embodiment, products (including goods and services) can create and send data, such as product-level data, to communication layers and / or edge data processing facilities in the value chain network technology stack. This data can generate enriched product-level data and be combined with third-party data for further processing, modeling, or other adaptive or collaborative intelligence activities, as described herein, including, but not limited to, generating and / or simulating product and value chain use cases, for which the data can be utilized by the product, product development process, product design, etc.

[0265] [Stack View Example] Referring to FIG. 5 , a block diagram illustrating the components and interrelationships of systems and processes of a value chain network technology stack is shown at 500, which may include, but is not limited to, a presentation layer, an intelligence layer, and serverless functionality such as a platform (e.g., a development platform and a host platform), a data facility (e.g., data-related due to IoT and big data), and a data aggregation facility. In an exemplary embodiment, the presentation layer may include, but is not limited to, a user interface and modules for exploration and discovery, and tracking of user experience and engagement. In an exemplary embodiment, the intelligence layer may include, but is not limited to, statistical and computational methods, semantic models, analytical libraries, a development environment for analysis, algorithms, logic and rules, and machine learning. In an exemplary embodiment, the platform or value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device discovery. In an exemplary embodiment, the data aggregation facility or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from heterogeneous devices. In an exemplary embodiment, the data facilities or layers may include, but are not limited to, IoT and big data access, control, and collection and replacement. In an exemplary embodiment, the value chain network technology stack may be further associated with additional data sources and / or technology enablers.

[0266] [Value Chain Orchestration from a Command Platform] FIG. 6 illustrates an interconnected value chain 668 in which a value chain network management platform 604 (sometimes referred to herein as a “value chain control tower,” “VCNP,” or simply “system,” or “platform”) orchestrates various factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, and logistics and distribution factors. The unified platform 604 for monitoring and managing supply and demand factors and status information (e.g., quality and status, planning, ordering and confirmation, and / or tracking and tracing) can be shared about and among various entities (including, for example, customers / consumers, distribution such as suppliers and distributors, and production such as producers and production facilities) so that demand factors are understood and accounted for, orders are generated and fulfilled, and products are made and moved through the supply chain. The value chain network 668 can include not only the intelligent product 650 but also all of the equipment, infrastructure, personnel, and other entities involved in planning for and fulfilling demand for it.

[0267] [Value Chain Network and Value Chain Network Management Platform] Referring to FIG. 7 , the value chain network 668 managed by the value chain management platform 604 includes a set of value chain network entities 652 such as, but not limited to, a product 650, which may be an intelligent product 650; a set of production facilities 674 involved in the production of the product 650, which may be a finished product, component, system, subsystem, material used in the article, etc.; various entities involved in the supply environment 670, such as suppliers 642, points of origin 610, activities and other supply elements 648; various entities involved in the demand environment 672, Activities and other demand drivers 644, such as customers 662 (including consumers, businesses, and intermediate customers such as value-added resellers and distributors), retailers 664 (including online retailers, mobile retailers, traditional brick-and-mortar retailers, pop-up shops, etc.), etc., located and / or operating at various destinations 612, various distribution environments 678 and distribution facilities 658 such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and maritime facilities 622 such as port infrastructure facilities 660, floating assets 620, and shipyards 638, etc. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise manages (and in some cases enables autonomous or semi-autonomous operation of) a wide range of processes, workflows, activities, events, and applications 630 (in some cases collectively referred to as simply “applications 630”) of the value chain network 668.

[0268] 7, there is shown a high-level schematic diagram of a value chain network management platform 604. The value chain network management platform 604 manages the management of one or more value chain network processes, workflows, activities, events, and / or applications 630, which may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work together to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact, or own, operate, support, or enable within, or related to, a product 650 (which may be any category of product, such as a finished product, a software product, a hardware product, a component product, a material, an item of equipment, an item of consumer packaged goods, etc.). The intelligent product 650 may be a consumer product, a food product, a beverage product, a household product, a business product, a consumable product, a pharmaceutical product, a medical device product, a technology product, an entertainment product, or any other type of set of products and / or related services that may be part of, integrated with, linked to, or operated in conjunction with the VCNP 604, and in embodiments may encompass an intelligent product 650 that enables a set of capabilities such as, without limitation, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, voice recognition, touch interfaces, remote operation, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, acoustic processing systems, data storage, data integration, and / or various Internet of Things capabilities.

[0269] In an embodiment, the management platform 604 may include a set of data processing layers 624, each configured to provide a set of capabilities that facilitate the development and deployment of intelligence, such as to drive automation, machine learning, the application of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In an embodiment, the data processing layers 624 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, conveniently referred to in some cases as data collection systems 640, to collect and organize data collected from or about the value chain entities 652, as well as data collected from or about the various data layers 624 or their services or components. In an embodiment, the data processing layer 624 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 via a value chain network-oriented data storage system layer 624, sometimes referred to herein for convenience simply as a data storage layer 624 or storage layer 624. As shown in FIG. 7 , the data processing layer 624 may also include an adaptive intelligent systems layer 614. The adaptive intelligent systems layer 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere in this disclosure. The data processing, artificial intelligence, and computation systems 634 may be related to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, etc.).Specifically, the data processing, artificial intelligence, and computation system 634, in some embodiments, may be used in applications such as the use of recurrent networks as adaptive intelligence systems operating on blockchains of transactions in a supply chain to determine patterns, use with biological systems, opportunity mining (e.g., artificial intelligence systems may be used to monitor new data sources as opportunities to automatically deploy intelligence), robotic process automation (e.g., automating intelligent agents for various workflows), edge and network intelligence (e.g., involving monitoring systems such as adaptive use of available RF spectrum, adaptive use of available fixed network spectrum, adaptive storage of data based on available storage conditions, adaptive sensing based on a type of context sensing), etc.

[0270] In embodiments, the data processing layer 624 may be depicted in a vertical stack or ribbon in the diagram and may represent many functionalities available to the platform 604, including storage, monitoring, and processing applications and resources, and combinations thereof. In embodiments, the set of capabilities of the data processing layer 624 may include a shared microservices architecture. By these examples, the set of capabilities may be deployed to provide multiple different services or applications, which may be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or resident in a particular application or process. In some examples, the set of capabilities may include one or more activities marshaled for the benefit of the platform. In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the set of platform capabilities may be deployed within at least a portion of a common architecture, such as a common architecture that supports a common data schema. In embodiments, one of the set of platform capabilities may be deployed within at least a portion of a common architecture that can support common storage. In embodiments, one of the set of platform capabilities may be deployed within at least a portion of a common architecture that can support a common monitoring system. In embodiments, one or more sets of platform capabilities may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks. In embodiments, the set of capabilities of data processing layer 624 may include examples where storage functions support scalable processing capabilities, scalable monitoring systems, digital twin systems, payment interface systems, etc.By these examples, one or more software development kits may be provided by the platform along with deployment interfaces to facilitate connection and use of the capabilities of the data processing layer 624. In a further example, an adaptive intelligence system may analyze, learn, configure, and reconfigure one or more of the capabilities of the data processing layer 624. In an embodiment, the platform 604 may include a common data storage schema that provides, for example, shipyard entity-related services and warehousing entity services. Many other applicable examples and combinations exist that are applicable to the aforementioned examples involving many of the value chain entities disclosed herein. By these examples, the platform 604 may be shown to create connectivity (e.g., capability and information provision) across many value chain entities. In many examples, there is a pairing (dual, triple, quadruple, etc.) of similar types of value chain entities that use one or more smaller sets of the capabilities of the data processing layer 624 to deploy (interact, depend, etc.) a common data schema, a common architecture, a common interface, etc. While services and capabilities may be provided to a single value chain entity, a platform can be shown to provide myriad benefits to the value chain and consumers by supporting connectivity between value chain entities and the applications used by the entities.

[0271] [Platform-managed value chain network entities] Referring to FIG. 8 , the value chain network management platform 604 is illustrated in conjunction with a set of value chain entities 652 that may be managed by, integrated with, or integrated into the platform 604, and / or that may provide inputs to or take outputs from the platform 604, for example, a wide range of value chain activities (such as supply chain activities, logistics activities, etc., demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many other activities involving various value chain network processes, workflows, activities, events, and applications 630 (collectively “applications 630” or simply “activities”)). Connectivity with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in more detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data processing layer, for the platform as a whole, and / or between value chain entities 652.

[0272] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, individuals or other entities mentioned throughout this disclosure or in the documents incorporated by reference herein, such as, but not limited to, machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loaders, cranes, lifts, carriers, trucks, loaders, unloaders, packers, pickers, and many others; robotic systems, e.g., physical robots, collaborative robots (e.g., "cobots"), drones, autonomous vehicles, software bots, and many others); products 650 (which may be any category of product, e.g., finished goods, software products, hardware products, component products, materials, items of equipment, items of consumer packaged goods, consumer products, food products, beverage products, household products, business products, consumables, pharmaceuticals, medical device products, technology products, entertainment products, or any other type of product and / or set of related services); value chain processes 722 (e.g., shipping processes, transportation processes, maritime transport, etc.); processes, inspection processes, transportation processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, etc.), border control processes, port-related processes, software processes (applications, programs, services, etc.), packing / loading processes, financial processes (e.g., insurance processes, reporting processes, trading processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others), wearable and portable devices 720 (cell phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing-integrated devices, armbands, bracelets, neck-worn devices, AR / VR devices, headphones, and many others), workers 718 (delivery workers, seafarers, barge workers, port workers, dock workers, train workers, ship workers,fulfillment center delivery workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, stevedores, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors, and (for any of the processes described herein) workers, security guards, safety personnel, and many others); Suppliers 642 (such as suppliers of goods and related services of all kinds, parts suppliers, ingredient suppliers, material suppliers, manufacturers, and many others); Customers 662 (including consumers, licensees, businesses, value-added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use categories of goods and / or related services); extensive operational facilities 712 (such as loading docks, storage and warehousing facilities 654, vaults, distribution facilities 658, and fulfillment centers 628); air travel facilities 740 (including aircraft, airports, hangars, runways, fueling depots, and the like); Maritime Facilities 622 (including port infrastructure facilities 62 2 (docks, yards, cranes, roll-on / roll-off facilities, ramps, containers, etc., container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (ships, barges, boats, etc.), origin 610 and / or destination 628 facilities and other items, transportation facilities 710 (container ships, barges, other floating assets 620, and land vehicles and other delivery systems 632 used to transport goods, such as trucks, trains, etc.), items or factors that factor into demand (e.g., demand factors 644) (market factors, etc.), vents, and many others), supply-driven items or elements (i.e., supply factors 648) (including market factors, weather, availability of parts and materials, and many others), logistics factors 750 (such as availability of transportation routes, weather, fuel prices, regulatory factors, availability of space (such as on vehicles, in containers, in packages, in warehouses, in fulfillment centers, on shelves, and many others), retailers 664 (including online retailers 730, such as in the form of e-commerce sites 730), transportation routes (such as waterways 732, roads 734, airways, railroads 738, and many others),Robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, etc.), drones 748 (including for package delivery, site mapping, monitoring or inspection, etc.), autonomous vehicles 742 (for package delivery, etc.), software platforms 752 (enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platforms as a service, infrastructure as a service, software-based data storage platforms, analytics platforms, artificial intelligence platforms, etc.), and many others. In some demonstrative embodiments, products 650 may be encompassed as intelligent products 650, or VCNP 604 may include intelligent products 650. Intelligent products 650 may be enabled with a range of capabilities such as, but not limited to, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computing, artificial intelligence, analog or digital sensors, cameras, acoustic processing systems, data storage, data integration, and / or various Internet of Things capabilities. The intelligent product 650 may include a form of information technology. The intelligent product 650 may include a processor, computer random access memory, and a communication module. The intelligent product 650 may be a passive intelligent product, similar to an RFID-type data structure that the intelligent product may be pinged or read. The product 650 may be considered a value chain network entity (e.g., under the control of a platform), surrounding infrastructure, and may be made intelligent by adding RFID so that data can be read from the intelligent product 650. The intelligent product 650 may also include sensors, IoT devices, tags,Alternatively, the intelligent product 650 may be connected to fit into a value chain network in such a way that connectivity is built around the intelligent product 650 through other components.

[0273] In an embodiment, the monitoring system layer 614 may monitor any or all of the value chain entities 652 in the value chain network 668, may exchange data with the value chain entities 652, may provide control instructions to or take instructions from any of the value chain entities 652, etc. through various functions of the data processing layer 624 described throughout this disclosure.

[0274] [Network characteristics of value chain network entities] 9 , the orchestration of a collection of deeply interconnected value chain network entities 652 in a value chain network 668 by a value chain network management platform 604 is illustrated. Each of the value chain network entities 652 may have a connection to a VCNP 604, a connection to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via the cloud, or other connection), and / or a connection to other value chain network entities 652 through the VCNP 604. The value chain network management platform 604 may configure or provide resources that enable the connectivity and manage applications 630 that utilize the connectivity, such as by using information from one set of entities 652 to inform applications 630 that include another set of entities 652, by coordinating the activities of the set of entities 652, by providing input to artificial intelligence systems of the VCNP 604 or a set of entities 652 or related thereto, or by interacting with edge computing systems deployed on or within the entities 652 or their environments.

[0275] The entities 652 may be external, allowing the VCNP 604 to interact with these entities 652. In some cases, the VCNP 604 may function as a control tower to establish oversight (e.g., establishing common oversight across multiple entities 652). In one unified platform, there may be an interface where users can view various items, such as user destinations, ports, aviation and rail assets, and orders. It is then conceivable to establish a common data schema that enables services to operate across any one of these applications. This may involve taking any of the data flowing through or about any of these entities 652 and pulling the data into a framework where other applications across supply and demand can interact with the entities 652. This may be a shared data pipeline coming from IoT systems and other external data sources, fed into a monitoring layer and stored in a common data schema at a storage layer, after which various intelligence may be trained to identify meaning across these entities 652. In an exemplary embodiment, if a supplier goes bankrupt or a determination is made that a supplier has gone bankrupt, the VCNP 604 may automatically trigger a replacement smart contract to be sent to a secondary supplier with modified terms. There can also be management of different aspects of the supply chain. For example, instantly and automatically changing pricing on the demand side in response to confirmation that one or more suppliers have gone bankrupt (e.g., from a bankruptcy announcement). Other similar examples can be used based on what occurs in its automation layer, which can be enabled by VCNP 604. Then, at this VCNP 604 interface layer, the digital twin can be used by a user to view all of these entities 652 that are not normally shown together and monitor what is happening with each of these entities 652, including identifying problem states. For example, after seeing three-quarters of bad financial reports for a supplier, the report can be flagged to keep an eye on it for future potential bankruptcy, etc.

[0276] For example, an IoT system deployed at a fulfillment center 628 may interface with an intelligent product 650 that takes customer feedback regarding the product 650, and an application 630 for the fulfillment center 628, upon receiving customer feedback via a connection to the intelligent product 650 regarding an issue with the product 650, may initiate a workflow to take corrective action on the product 650 before the product 650 leaves the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, via a connection to floating assets 620 (ships, barges, etc.), may notify a fleet of floating assets 620 that the port is near capacity, thereby initiating a negotiation process (which may include automated negotiations based on a set of rules and governed by smart contracts) for remaining capacity and redirecting some assets 620 to an alternative port or holding facility. These and many other connections between value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections between defined groups of entities 652 (such as those controlled by the same owner or operator), are encompassed hereby as applications 630 managed by VCNP 604.

[0277] [Platform-managed value chain network activities and applications] 10 , the set of applications 630 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may include, but are not limited to, supply chain management applications 812 (e.g., but not limited to, for managing the timing, quantity, logistics, shipping, delivery, and other details of orders for goods, components, and other items), asset management applications 814 (e.g., but not limited to, for managing value chain assets such as floating assets (e.g., ships, boats, barges, and floating platforms), real estate (e.g., used for warehouses, ports, shipyards, distribution centers, and other building locations), equipment, machinery, and supplies (e.g., used to handle containers, cargo, packages, goods, and other items), vehicles (e.g., forklifts, delivery trucks, autonomous vehicles, and other systems used to move items), human resources (e.g., workers), software, information technology resources, data processing resources, data financial applications 822 (e.g., for handling financial matters related to value chain entities and assets, including, but not limited to, payments, collateral, guarantees, bonds, customs, duties, levies, taxes, etc.); risk management applications 818 (e.g., for managing risk or liability with respect to shipments, goods, products, assets, people, floating assets, vehicles, items of equipment, components, information technology systems, security systems, security events, cybersecurity systems, property items, health conditions, death, fire, flood, weather, failure, negligence, business interruption, personal injury, damage to property, damage to business, breach of contract, and the like); demand management applications 824 (e.g., without limitation, demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, etc.);Applications that analyze, plan, or promote customer interest in value chain products or services by or in categories of goods that can be supplied by the facility, such as search engine optimization applications, sales management applications, advertising network applications, behavioral tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, product or service recommendation engines, and the like (including those that use or enable one or more features of, or are performed using the intelligence capabilities of, the intelligent products 650); trading applications 858 (such as, but not limited to, buying applications, selling applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, analytical applications for analyzing value chain performance, yields, return on investment, or other metrics, or the like); tax applications 858 50 (for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other factors related to taxes, duties, levies, levies, tariffs, credits, fees, or other government-imposed charges, such as, but not limited to, customs duties, value-added taxes, sales taxes, income taxes, property taxes, municipal fees, pollution taxes, renewable energy credits, pollution abatement credits, import duties, export duties, etc.); identity management application(s) 830 (for managing one or more identities of entities 652 involved in the value chain, including, but not limited to, one or more of an identity verification application, a biometric verification application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a blacklist application, a whitelist application, a content inspection-based fraud detection application, or other fraud detection application);Inventory management applications 820 (such as, but not limited to, for managing inventory at a fulfillment center, distribution center, warehouse, storage facility, store, port, ship or other floating asset, or other location); security applications, solutions, or services 834 (referred to herein as security applications, and which may include, for example, but not limited to, any of the identity management applications 830 described above, physical security systems (e.g., access control systems (such as using biometric access control, fingerprints, retinal scans, passwords, and other access controls), safes, vaults, secure storage facilities, etc.), surveillance systems (such as using cameras, sensors such as motion sensors, infrared sensors, etc.), perimeter security systems, floating security systems for floating assets, cybersecurity systems (virus detection and repair, intrusion detection and repair, spam detection and repair, phishing detection and repair, social engineering detection and repair, cyberattack detection and repair, packet inspection, traffic inspection, DNS attack repair and detection, etc.) or other security applications safety applications 840 (including, but not limited to, any application that detects, characterizes, or predicts the likelihood and extent of an accident or other damage event, such as for improving worker safety, reducing the likelihood of damage to property, reducing accident risk, reducing the likelihood of damage to goods (e.g., cargo), risk management for insured goods, loan collateral, etc., safety management based on any of the data sources, events, or entities noted in this disclosure overall or in the documents incorporated herein by reference); blockchain applications 844 (including, but not limited to, distributed ledgers or other blockchain-based applications that capture a series of transactions, such as debits or credits, purchases or sales, exchanges of in-kind considerations, smart contract events, etc.); facilities management applications 850 (including, but not limited to, information technology systems for managing and designing infrastructure, buildings, systems, real estate, personal assets, and other assets involved in supporting the value chain, such as shipyards, ports, distribution centers, warehouses, docks, stores, fulfillment centers, storage facilities, etc.);managing or controlling systems and equipment on or around a premises, such as robotic / autonomous vehicle systems, packing systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, workflow systems for human assets, etc.; regulatory applications 852 (including, but not limited to, applications for regulating any of the applications, services, transactions, activities, workflows, events, entities, or other items referred to in this specification and the documents incorporated herein by reference, such as regulations on permitted routes, permitted cargo and goods, permitted trading parties, required disclosures, privacy, pricing, marketing, the provision of goods and services, use of data (including data privacy regulations, regulations on data retention, etc.), banking, marketing, sales, financial planning, and many others); commerce applications, solutions, or services 854 (e.g., marketplaces for e-commerce sites, online sites, auction sites or marketplaces, marketplaces for goods, advertising marketplaces, reverse auctions, etc.); vendor management applications 832 (including, but not limited to, applications for managing a set of vendors or potential vendors and / or for managing the procurement of a set of goods, components, or materials that may be supplied in a value chain, with functionality such as vendor qualification, vendor ratings, requests for proposals, requests for information, bonds or other guarantees of performance, contract management, etc.); analytics applications 838 (including, but not limited to, analytics applications relating to any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein, such as big data applications, user behavior applications, forecasting applications, classification applications, dashboards, pattern recognition applications, econometrics applications, financial yield applications, return on investment applications, scenario planning applications, decision support applications, etc.);demand forecasting applications, demand planning applications, route planning applications, weather forecasting applications, and many others), pricing applications 842 (for example, but not limited to, for pricing goods, services (including those referenced throughout this disclosure and the documents incorporated herein by reference), and smart contract applications, solutions, or services (collectively referred to herein as smart contract applications 848, for example, but not limited to, any of the smart contract types referenced in this disclosure or the documents incorporated herein by reference, e.g., smart contracts for the sale of goods, smart contracts for ordering goods, smart contracts for shipping resources, smart contracts for labor, smart contracts for the delivery of goods, smart contracts for the installation of goods, smart contracts for tokens or cryptocurrency, rights based on future terms, etc. The value chain management platform 604 may include any one or more of a wide range of types of applications (such as smart contracts to attribute options, futures, or interests; smart contracts for securities, commodities, futures, options, derivatives, etc.; smart contracts for current or future resources; smart contracts configured to consider or accommodate tax, regulatory, or compliance parameters; smart contracts configured to perform arbitrage transactions, and many others). Accordingly, the value chain management platform 604 may host a wide range of heterogeneous applications 630 (such term including the above and other value chain applications, services, solutions, etc.) enabling interaction between them such that any set or larger combination or permutation of such services may be improved compared to isolated applications of the same type by virtue of shared microservices, shared data infrastructure, and shared intelligence.

[0278] 10 , the set of applications 630 provided on, integrated with, and / or managed by or for the VCNP 604 and / or comprising the set of value chain network entities 652 may include, but are not limited to, a payment application 860 (for payment calculations (including based on contextual factors such as applicable taxes, duties, etc. for the geography of the entity 652), funds transfers, payment settlements to parties, etc. for any of the applications 630 noted herein), a process management application 862 (such as a supply process, a demand process, a logistics process, a delivery process, a fulfillment process, a distribution process, an ordering process, a navigation process, and many others), etc., for managing any of the processes or workflows described throughout this disclosure, a compatibility testing application 864 (such as for assessing compatibility between value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein), etc. 6. The value chain network 630 may include, but is not limited to, applications for testing infrastructure and equipment 652, such as infrastructure testing applications 802 (e.g., for testing the capabilities of infrastructure elements supporting the product 650 or application 630, such as, but not limited to, storage capacity, lifting capacity, movement capacity, memory capacity, network capacity, environmental control capacity, software capacity, security capacity, and many others), and / or incident management applications 910 (e.g., for managing events, accidents, and other incidents that may occur in one or more environments that include the value chain network entities 652, such as, but not limited to, vehicle accidents, worker injuries, shutdown incidents, property damage incidents,product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicular traffic, maritime traffic, human worker traffic, etc., and combinations therebetween), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others).

[0279] 10 , the set of applications 630 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may include, but are not limited to, predictive maintenance applications 910 (such as for predicting, forecasting, and undertaking actions to manage breakdowns, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, etc., for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, etc.), logistics applications 912 (such as for managing logistics for picking, delivering, transferring items to material handling equipment, loading, unloading, packing, picking, shipping, driving, and other activities involved in scheduling and managing the movement of products 650 and other items between origins and destinations via various intermediate locations), reverse logistics applications 914 (such as for managing the logistics of returned products 650, waste, damaged goods, or other items that may be transported in a return logistics route), and other applications 916 (such as for managing the logistics of returned products 650, waste, damaged goods, or other items that may be transported in a return logistics route). waste reduction applications 920 (e.g., for reducing packaging waste, solid waste, energy waste, liquid waste, pollution, computational resource waste, human resource waste, or other waste involved in a value chain network entity 652 or activity); augmented reality, mixed reality, and / or virtual reality applications 930 (e.g., for visualization of one or more value chain network entities 652 or activities involved in one or more applications 630, such as, but not limited to, the movement of a product 650, the interior of a facility, the state or condition of an item of goods, one or more environmental conditions, weather conditions, packaging configurations for a container or set of containers, or many others); demand forecasting applications 940 (e.g., for forecasting demand for a product 650, a category of product, potential products, and / or factors that affect demand such as market factors, wealth factors, demographic factors, weather factors, economic factors, etc.); demand aggregation applications 942 (e.g., for forecasting demand for one or more products 650, categories, etc., including current demand for existing products and future demand for products that are not yet available,The smart products 650 may further include a customer profiling application 944 (such as for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, such as based on past purchasing data, loyalty program data, behavioral tracking data (including data captured in customer interactions with smart products 650), online clickstream data, interactions with intelligent agents, and other data sources), and / or a component supply application 948 (such as for managing a supply chain of components for a set of products 650).

[0280] With further reference to FIG. 10 , the set of applications 630 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involving the set of value chain network entities 652 may further include, without limitation, a policy management application 868 (e.g., to manage the execution of one or more workflows (which may include configuring policies on the platform 604 for each workflow), to deploy one or more policies, rules, etc. for governance of one or more value chain network entities 652 or applications 630, such as to manage compliance with regulations (including maritime, food and drug, medical, environmental, health, pharmaceutical, etc.)). The platform 604 may also be used to administer the services of the platform 604, including those related to maritime, food and drug, medical, environmental, health, safety, tax, financial reporting, commercial, and other regulations, as described throughout this disclosure or as would be understood in the art; manage the provision of resources (such as connectivity, computation, human, energy, and other resources); manage compliance with corporate policies; manage compliance with contracts (including smart contracts), where the platform 604 may automatically deploy governance capabilities to associated entities 652 and applications 630, such as via connectivity facilities 642; govern interactions with other entities (such as including policies regarding information sharing and access to resources); data access (such as privacy data, operational data, governing the product 650 configuration, including on-the-fly configuration such as during agile manufacturing, configuring or customizing at the root (such as by 3D printing one or more features or elements), or remotely configuring or customizing by downloading firmware, configuring field programmable gate arrays, installing software, etc.;Warehouse management and fulfillment applications 872 (for managing warehouses, distribution centers, fulfillment centers, etc., including product selection, configuring product storage locations, routing products within the facility by personnel or mobile robots, picking and packing schedules, determining routes and workflows, managing the operations of robots, drones, conveyors, and other facilities, scheduling product release to docks, etc., and many other functions), kit configuration and deployment applications 874 (for enabling users of the VCNP to configure kits, boxes, or other pre-integrated, pre-provisioned, and / or pre-configured systems to enable customers or workers to quickly deploy a subset of the VCNP 604 functionality for a particular value chain network entity 652 and / or application 630), and / or product testing applications 878 for testing products 650 (including testing for performance, functionality and feature activation, safety, policy or regulatory compliance, quality, quality of service, likelihood of failure, and many other factors) are provided.

[0281] With further reference to FIG. 10 , the set of applications 630 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involving the set of value chain network entities 652 may further include, but are not limited to, a maritime fleet management application 880 (for managing a set of maritime assets such as container ships, barges, boats, etc., to manage fleet assets as well as related infrastructure facilities such as docks, cranes, ports, etc., for determining optimal routes for fleet assets based on weather, market, traffic, and other conditions, to ensure policy and regulatory compliance, to ensure safety, to improve environmental factors, to improve financial metrics, and many others), a shipping management application 882 (for managing a set of shipping assets such as trucks, trains, planes, etc., for example, to optimize financial yield, improve safety, reduce energy consumption, reduce delays, mitigate environmental impacts, and many other purposes), an opportunity matching application 884 (for managing a set of shipping assets such as trucks, trains, planes, etc., for example, to optimize financial yield, improve safety, reduce energy consumption, reduce delays, mitigate environmental impacts, and many other purposes), and an opportunity matching application 885 (for managing a set of shipping assets such as trucks, trains, planes, etc., for example, optimizing financial yield, improving safety, reducing energy consumption, reducing delays, mitigating environmental impacts, and many other purposes). or matching multiple demand factors with one or more supply factors, matching the needs and capabilities of value chain network entities 652, identifying reverse logistics opportunities, identifying input opportunities to enrich analytics, artificial intelligence and / or automation, identifying cost reduction opportunities, identifying profit and / or arbitrage opportunities, and many others); workforce management applications 888 (such as for managing workers in various workforces, including workforces in or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise locations, retail stores, online / ecommerce site facilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure); distribution and delivery applications 890 (such as for planning, scheduling, routing, and otherwise managing the distribution and delivery of products 650 and other items); and / or enterprise resource planning (ERP) applications 892 (such as for planning the utilization of enterprise resources, such as labor resources, financial resources, energy resources, physical assets, digital assets, and other resources).

[0282] [Core functions and interactions of the data handling layer (adaptive intelligence, monitoring, data storage, applications)] Referring to FIG. 11 , a high-level schematic diagram of one embodiment of a value chain network management platform 604 is shown, including a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work together to enable intelligent management of a set of value chain entities 652 that occur, operate, transact, etc. within, or that may own, operate, support, realize, or otherwise be a part of, integrated with, or linked to, one or more value chain network processes, workflows, activities, events, and / or applications 630, or that are operated by the platform 604 in connection with a product 650 (which may be a finished product, software product, hardware product, component product, material, equipment, consumer packaged goods, consumer product, food product, beverage product, household product, business product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product or related service, and in embodiments may encompass intelligent products enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities). Value chain entities 652 are such entities because of or involve in a wide range of value chain activities (which may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in documents incorporated by reference herein) that are involved in various value chain network processes, workflows, activities, events and applications 630, such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many other activities).

[0283] In an embodiment, the value chain network management platform 604 may include a set of data processing layers 624, each configured to provide a set of capabilities that facilitate the development and deployment of intelligence, such as to facilitate automation, machine learning, the application of artificial intelligence, intelligent transactions, intelligent operations, remote control, analysis, monitoring, reporting, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In an embodiment, the data processing layers 624 may include a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (sometimes referred to herein simply as the data storage layer 624 for convenience), an adaptive intelligent systems layer 614, and a value chain network management platform layer 604. The value chain network management platform layer 604 may include the data processing layer 624 to provide management of the value chain network management platform layer 604 and / or management of other layers, such as the value chain network monitoring system layer 614, the value chain network entity-oriented data storage system layer 624 (e.g., the data storage layer 624), and the adaptive intelligent systems layer 614. Each of the data processing tiers 624 may include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in documents incorporated by reference. In an embodiment, each of the data processing tiers 624 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other tiers 624 or other systems (e.g., configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture).For example, the platform 604 may have (or may configure and / or provide) a set of connectivity facilities 642, such as network connections (including various configurations, types, and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, or the like, which the data processing layer 608 may use, software interfaces, microservices, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud functions, or the like, through which data or information is exchanged between the data processing layer 608 and other layers, systems, or subsystems of the platform 604, as well as other systems, such as value chain entities 652 or external systems, e.g., cloud-based or on-premise enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, and many other systems). Each of the data processing layers 624 may include a set of services (e.g., microservices) for data processing, including facilities for data extraction, transformation, and loading, data cleansing and deduplication facilities, data normalization facilities, data synchronization facilities, data security facilities, computational facilities (e.g., for performing predefined computational operations on data streams and providing output streams), compression and decompression facilities, analytical facilities (such as providing automatic generation of data visualizations), and others.

[0284] In an embodiment, each data processing layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data processing layers 624. These may include data integration functions, such as for extracting, converting, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 630, for example, converting data from one format or protocol to another as needed for one layer to consume output from another layer. In an embodiment, the data processing layers 624 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, to collect and organize data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. For example, a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity may be distributed via the monitoring system layer 614 to multiple different applications in the value chain management platform layer 604, such as one that facilitates monitoring of the worker's physiological, psychological, performance level, attention, or other state, and another that facilitates operational efficiency and / or effectiveness. In an embodiment, the monitoring system layer 614 facilitates time synchronization, normalization, etc., alignment of data collected regarding one or more value chain network entities 652.For example, one or more video streams or other sensor data collected of or about a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned to a common clock so that the relative timing of the video or other data set can be understood by a system that may process the video, such as a machine learning system that operates on images within the video, changes between images in different frames of the video, etc. In such an example, the monitoring system layer 614 may further align the set of video, camera images, sensor data, etc. with other data, such as streams of data from wearable devices, streams of data generated by value chain network systems (ships, lifts, vehicles, containers, material handling systems, packaging systems, delivery systems, drones / robots, etc.), streams of data collected by mobile data collectors, etc. Configuring the monitoring system layer 614 as a common platform or set of microservices accessed across many applications may dramatically reduce the number of interconnections an owner or other operator in a value chain network needs to have a growing set of applications monitoring a growing set of IoT devices and other systems and devices under their control.

[0285] In an embodiment, the data processing layer 624 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 via a value chain network-oriented data storage system layer 624, referred to herein in some cases for convenience simply as a data storage layer 624 or storage layer 624. For example, various data collected for the value chain entities 652, as well as data generated by other data processing layer 624, may be stored in the data storage layer 624 such that any of the various data processing layer 624 services, applications, programs, etc., can access a common data source (which may consist of a single logical data source that is distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to handle the vast amounts of data generated by or about the value chain network entities 652 as the applications 630 and value chain network uses grow and proliferate. For example, a supply chain or inventory management application in the value chain management platform layer 604, such as one for ordering replacement parts for items of machinery or equipment, may access the same data set of which parts have been replaced for a set of machinery as a predictive maintenance application used to predict whether a component on a ship or equipment at a port is likely to need a replacement part. Similarly, predictions can be used with respect to the resupply of goods.

[0286] In an embodiment, the value chain network data objects 1004 may be provided according to an object-oriented data model that defines the classes, objects, attributes, parameters, and other characteristics of the set of data objects (such as those associated with the value chain network entities 652 and applications 630) handled by the platform 604.

[0287] In embodiments, data storage system layer 624 may provide an extremely rich environment for the collection of data that can be used to extract features or inputs for expert systems, analytics systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems, such as those disclosed throughout this disclosure and the documents incorporated herein by reference. As a result, each application 630 in platform 604 and each adaptive intelligent system in adaptive intelligent systems layer 614 can benefit from data collected or generated by or for each other. In embodiments, data storage system layer 624 may facilitate the collection of data that can be used to extract features or inputs for intelligent systems, such as development frameworks from artificial intelligence. In examples, the collection of data may include pulling and / or storing event logs (naturally stored or ad hoc, as needed), performing periodic checks on on-board diagnostic data, or the like. In examples, feature pre-computation may be deployed using pre-computation, multiplexing signals, etc., using, for example, AWS Lambda, or various other cloud-based, on-demand computing capabilities. In many examples, even when aggregating hundreds or even tens of data types from relatively heterogeneous entities, there will be pairings (two-fold, three-fold, four-fold, etc.) of similar types of value chain entities that may use one or more sets of the capabilities of the data handling layer 624 to deploy connectivity and services across the value chain entities and across the applications used by the entities. In these examples, various pairings of similar types of value chain entities that use, at least in part, connectivity and services across the value chain entities and applications, may direct information from the connected data pairings to artificial intelligence services, including various neural networks and hybrid combinations thereof, as disclosed herein.In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the connected data pairings. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the effectiveness of decisions established by the artificial intelligence service. In these examples, information from the connected data pairings may be migrated to other tiers on the platform, including to support or deploy robotic process automation, predictions, forecasts, and other resources, such as those for which a shared data schema may facilitate capabilities and resources for platform 604.

[0288] A wide variety of storage media and data storage types, data architectures 1002, and formats can be used to store a wide range of data types in the storage layer 624, including, but not limited to, asset and facility data 1030, state data 1140 (such as that indicating a state, condition status, or other indicators regarding any of the value chain network entities 652, any of the applications 630 or their components or workflows, or any of the components or elements of the platform 604), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data, and many other types), event data 1034 (such as regarding any of a wide range of events, including operational data, transactional data, workflow data, maintenance data, and many other types of data, including events occurring within the value chain network 668 or with respect to one or more applications 630, process events, financial events, transactional events, etc.).claims data 664 (such as output events, input events, state change events, operational events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, recharging events, shipping events, warehouse events, movement of goods, border crossings, movement of cargo, inspection events, supply events, and many others), including claims data 664 (such as those relating to insurance claims such as business interruption insurance, product liability insurance, insurance for goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others; claims data relating to product liability, general liability, workers' compensation, injury and other liability claims; claims data relating to contracts such as supply contract performance claims, product delivery requirements, warranty claims, indemnity claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.); accounting data 730 (such as data relating to completion of contract requirements, satisfaction of bonds, payment of duties, etc.); and risk management data 732 (such as those relating to items supplied, amount, price, delivery, source, route, customs information, etc.), among many other data types associated with value chain network entities 652 and applications 630. .

[0289] In an embodiment, data processing tier 624 is configured in a topology that facilitates shared adaptive capabilities, which may be provided, managed, mediated, etc. by one or more of a set of services, components, programs, systems, or capabilities in adaptive intelligence systems tier 614, conveniently referred to herein as adaptive intelligence tier 614. Adaptive intelligence systems tier 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere throughout this disclosure. Thus, the use of various resources such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for a single device or a peered network), and available cloud infrastructure), data storage resources (such as local storage on a device, storage resources in a value chain entity or environment (including on-device storage, storage on an asset tag, local area network storage, etc.), network storage resources, cloud-based storage resources, database resources, etc.), network resources (including cellular network spectrum, wireless network resources, fixed network resources, etc.), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others), etc. may be optimized in a coordinated or shared manner on behalf of an operator, enterprise, etc. for the benefit of multiple applications, programs, workflows, etc. For example, the adaptive intelligence layer 614 may manage and provide available network resources for both supply chain management applications and demand planning applications (among many other possibilities), such that low-latency resources are used for the supply chain management application (where quick decisions may be important) and longer-latency resources are used for the demand planning application.As described in more detail throughout this disclosure and the documents incorporated herein by reference, various adaptations may be provided on behalf of various services and functions across various tiers 624, including based on application requirements, quality of service, on-time delivery, service goals, budgets, costs, pricing, risk factors, operational goals, efficiency goals, optimization parameters, return on investment, profitability, uptime / downtime, worker utilization, and many others.

[0290] The value chain management platform layer 604, sometimes referred to herein as the platform layer 604 for convenience, allows an operator to integrate multiple aspects of the value chain network environment or entities 652 into a common application environment (e.g., common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614, and / or common adaptive intelligence in the adaptive intelligence layer 614, whether shared data is shared, pooled, or similarly licensed, whether single-person, multi-person, or anonymized). Output from the applications 630 in the platform layer 604 may be provided to other data handling layers 624. These include, but are not limited to, state and status information for various objects, entities, processes, flows, etc.; object information such as identity, attribute, and parameter information for various classes of objects of various data types; event and change information for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including those related to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, etc.). Output from each application 630 may be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614. Thus, the cross-application nature of the platform layer 604 facilitates convenient orchestration of all of the necessary infrastructure elements to add intelligence to any given application, such as by feeding machine learning about cross-application results, providing automation enrichment for a given application through machine learning based on results from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604.In examples, there may be systems, components, services and other capabilities that control, automate, or optimize one or more performance characteristics of one or more value chain network entities 652, or that may generally improve any of the outputs and outcomes 1040 of the processes and applications pursued through use of the platform 604. In some examples, the outputs and outcomes 1040 from the various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like involved in the process steps that are intended to be automated. Data Storage Layer Deep Dive - Alternative Data Architectures

[0291] [Data Storage Layer Details - Alternative Data Architectures] 12 , additional details, components, subsystems, and other elements of any embodiment of the data storage layer 624 of the platform 604 are illustrated. A variety of data architectures may be used, such as traditional relational and object-oriented data architectures, a blockchain architecture 1180, an asset tag data storage architecture 1178, a local storage architecture 1190, a network storage architecture 1174, a multi-tenant architecture 1132, a distributed data architecture 1002, a value chain network (VCN) data object architecture 1004, a cluster-based architecture 1128, an event database architecture 1034, a state database architecture 1140, a graph database architecture 1124, a self-organizing architecture 1134, and other data architectures 1002.

[0292] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adapters 1110 to facilitate data storage, search access, query management, loading, extraction, normalization, and / or transformation to enable the use of various other data storage architectures 1002, such as to enable extraction from a database of one format and loading into a data system using a different protocol or data structure.

[0293] In embodiments, the value chain network-oriented data storage systems layer 624 may include, but is not limited to, physical storage systems, virtual storage systems, local storage systems (e.g., part of local storage architecture 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of network storage architecture 1174, such as using NVME, storage-attached networks, other network storage systems), and many others.

[0294] In an embodiment, the storage layer 624 may store data in one or more knowledge graphs (such as directed acyclic graphs, data maps, data hierarchies, data clusters with links and nodes, self-organizing maps, etc.) in the graph database architecture 1124. In an exemplary embodiment, a knowledge graph may be a powerful example of when graph databases and graph database architectures may be used. In some examples, a knowledge graph may be used to graph a workflow. For linear workflows, directed acyclic graphs may be used. For contingent workflows, cyclic graphs may be used. A graph database (e.g., the graph database architecture vpc608) may include a knowledge graph, or a knowledge graph may be an example of a graph database. In an exemplary embodiment, a knowledge graph may include ontologies and connections (e.g., relationships) between the ontologies in the knowledge graph. In an exemplary embodiment, a knowledge graph may be used to capture the knowledge domain manifestations of human experts so that there may be identification of opportunities to design and build robotic process automation or other intelligence that can replicate this knowledge set. The platform can be used to recognize that certain experts use this de facto knowledge base (from the knowledge graph) combined with capabilities replicable by artificial intelligence, which may vary depending on the type of expert involved. For example, artificial intelligence such as convolutional neural networks might be used for spatiotemporal aspects, such as those used for diagnosing problems or packing boxes in a warehouse. Meanwhile, the platform can use a different type of knowledge graph for a self-organizing map of experts whose primary task is to segment customers into customer segmentation groups. In some examples, the knowledge graph may be constructed from various data, such as job credentials, job listings, and analytical output deliverables. In embodiments, the data storage layer 624 may store data in a digital thread, ledger, or the like, such as for maintaining a serial or other record of entities 652 over time, including any of the entities described herein.In embodiments, the data storage layer 624 may use and enable asset tags 1178, which may include data structures associated with assets and accessed and managed, such as through the use of access controls, such that data storage and retrieval is optionally linked to local processes, but also optionally open to remote retrieval and storage options. In embodiments, the storage layer 624 may include one or more blockchains 1180, such as those that store identity data, transaction data, past interaction data, etc., with access controls that may be role-based or based on credentials associated with value chain entities 652, services, or one or more applications 630. Data stored by data storage system 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as relating to any of the value chain assets and facilities described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664, and many other types of data that may relate to, be generated by, or generate any of the value chain entities and activities described herein and in the documents incorporated by reference. Adaptive Intelligent Systems and Monitoring Layer

[0295] 13, which illustrates additional details, components, subsystems, and other elements of optional embodiments of platform 604. Management platform 604, in various optional embodiments, may include a set of applications 630 by which an operator or owner of a value chain network entity, or other user, may manage, monitor, control, analyze, or otherwise interact with one or more elements of value chain network entity 652, such as any of the elements noted above and associated throughout this disclosure.

[0296] In an embodiment, the adaptive intelligent systems layer 614 may include a set of systems, components, services, and other capabilities that collectively facilitate the collaborative development and deployment of intelligent systems, for example, that can enhance one or more of the applications 630 in the application platform layer 604, improve the performance of one or more of the components, or the overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors), improve other capabilities within the adaptive intelligent systems layer 614, optimize the performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, or the like) of one or more components of the value chain network-oriented data storage system 624, the control, automation, or one or more performance characteristics of one or more value chain network entities 652, or generally improve any of the process and application outputs and results 1040 pursued through use of the platform 604.

[0297] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adapters 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a state and event manager 1450, an opportunity miner 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction management systems 1900 (e.g., for setting up, provisioning, configuring and otherwise managing a set of interactions between a set of value chain network entities 652 in the value chain network 668), and a set of other systems.

[0298] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data. This layer may include, but is not limited to, real-time monitoring systems 1520 (such as on-board monitoring systems on ships and other floating assets, on delivery vehicles, on trucks and other transport assets, and event and status reporting systems at shipyards, ports, warehouses, distribution centers, and other locations; on-board diagnostics (OBD) and telematics systems on floating assets, vehicles, and equipment; systems that provide diagnostic codes and events through event buses, communication ports, or other communication systems; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, people tracking systems, ambient sensing systems, etc., installed in various environments where value chain activities and other events occur); and removable and replaceable monitoring systems such as handheld and mobile data collectors, RFID and other tag readers, smartphones, tablets, and other data-capable mobile devices). software interaction observation system 1500 (e.g., for recording and tracking events involving user interactions with a software user interface, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigational actions, eye movements, finger movements, gestures, menu selections, and many more, as well as software interactions that occur as a result of other programs, such as through an API); mobile data collector 1170 (as broadly described herein and in documents incorporated by reference); visual monitoring system 1930 (e.g., using video and still imaging systems, LIDAR, IR and other systems capable of visualizing entity 652 items, people, materials, components, machinery, equipment, personnel, gestures, facial expressions, location, configuration and other factors or parameters, as well as inspection systems that monitor processes, worker activities, etc.); interaction point system 1530 (e.g., dashboards,user interfaces and control systems for value chain entities); physical process observation systems 1510 (such as for tracking the physical activities of operators, workers, customers, etc.; the physical activities of individuals (such as shippers, delivery people, packers, pickers, assemblers, customers, merchants, vendors, distributors, etc.); the physical interactions of workers with other workers; the interactions of workers with physical entities such as machines and equipment; and the interactions of physical entities with other physical entities, including, but not limited to, the use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking the movement of systems attached to humans or physical entities), and many others; machine condition monitoring systems 1940 (such as for monitoring the state, condition, operating parameters, or other measures of state of any value chain entity, such as a client, server, cloud resource, control system, display screen, sensor, camera, vehicle, robot, or other machine or its components, etc.); sensors and cameras 1950 and other IoT data collection systems 1172 (including on-board sensors, sensors or other data collectors (including click tracking sensors) of or relating to the value chain environment (e.g., but not limited to, points of origin, loading docks, vehicles or floating assets used to transport goods, vessels, ports, distribution centers, storage facilities, warehouses, delivery vehicles, etc.), and points of destination; cameras for monitoring the overall environment; dedicated cameras for specific machines, processes, workers, etc.; wearable cameras; mobile cameras; cameras located on mobile robots; cameras on mobile devices such as smartphones and tablets; and any of the many other sensor types disclosed throughout this disclosure or in documents incorporated by reference herein); indoor location monitoring systems 1532 (including cameras, IR systems, motion detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectral detection systems, time-of-flight systems, chemical noses and other chemical sensor sets, and other sensors);monitor user feedback systems 1534 (survey systems, touchpads, voice-based feedback systems, rating systems, facial expression monitoring systems, affect monitoring systems, gesture monitoring systems, etc.), behavior monitoring systems 1538 (movements, shopping behavior, purchasing behavior, clicking behavior, behavior indicative of fraud or deception, user interface interactions, product return behavior, behavior indicative of interest, attention, boredom, etc., behavior indicative of mood (such as fidgeting, staying still, moving closer, or changing posture), and many others), and any of a wide variety of Internet of Things (IoT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated herein by reference;

[0299] In an embodiment, the value chain monitoring system layer 614 and its data collection system 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or subsystems for searching for entities within the value chain network 668, by device identifier, by network location, by geolocation (e.g., by geofencing), by indoor location (e.g., by proximity to known resources such as IoT-enabled devices and infrastructure, Wifi routers, switches, etc.), cellular location (e.g., proximity to cellular towers), by identity management system (e.g., where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise, by an identifier assigned and / or managed by the platform 604), etc. The entity discovery 1900 may initiate a handshake between a set of devices, such as to initiate interactions to serve various applications 630 or other capabilities of the platform 604.

[0300] Referring to FIG. 14 , a management platform for information technology systems, such as a management platform for goods and / or services value chains, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide collaborative intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) for a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 may provide the artificial intelligence 1160 through a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of value chain applications (e.g., the demand management applications 824 and the supply chain applications 812). The adaptive intelligence system 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0301] In an embodiment, the user interface may include an interface for configuring the artificial intelligence system 1160 to enhance, control, improve, optimize, configure, adapt or otherwise affect the value chain of a category of goods 3010, such as by taking inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and feeding them to a neural network, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated by reference herein. In an embodiment, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes related to the value chain, category of goods 3010, etc.

[0302] In embodiments, providing the tailored intelligence may include providing an artificial intelligence capability, such as artificial intelligence system 1160. The artificial intelligence system may facilitate the tailored intelligence for a set of demand management applications 824 or a set of supply chain applications 812, or both, for a category of goods, by processing data available in any of the value chain data sources, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, product design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, product lifecycle management (PLM) systems, etc.

[0303] In embodiments, user interface 3020 may provide access to artificial intelligence capabilities, applications, systems, etc. for tailoring intelligence for value chain applications, particularly for the value chain applications for the category of commodity 3010. User interface 3020 may be adapted to receive information describing the category of commodity 3010 and configure user access to responsive artificial intelligence capabilities such that a user may be guided via the user interface to artificial intelligence capabilities suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the category of commodity 3010. User interface 3020 may facilitate providing tailored intelligence comprised of artificial intelligence capabilities that provide tailored intelligence for specific operators and / or companies participating in the supply chain for the category of commodity.

[0304] In embodiments, the user interface 3020 may be configured to facilitate a user's selection and / or configuration of multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities, each receiving, processing, and generating output that may be shared between the applications. The type of artificial intelligence system 1160 may be indicated in the user interface 3020 in response to the set of connected applications or their data elements being indicated in the user interface, such as by a user hovering a pointer near the connected set of applications. In embodiments, the user interface 3020 provides a set of functionality that facilitates access to the set of adaptive intelligence systems and facilitates the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent trading, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0305] The adaptive intelligence system 614 may be comprised of data processing, artificial intelligence, and computing systems 634 that may operate collaboratively to provide collaborative intelligence, such as when the artificial intelligence systems 1160 operate on or respond to data collected or generated by other systems in the adaptive intelligence system 614, such as data processing systems. In an embodiment, providing collaborative intelligence may include employing one or more types of neural networks described herein and in the documents incorporated by reference to operate part of a set of artificial intelligence systems 1160 that process either demand management application outputs and supply chain application outputs to provide collaborative intelligence.

[0306] In an embodiment, providing coordinated intelligence for the set of demand management applications 824 may include configuring at least one of the adaptive intelligence systems 614 (e.g., through a user interface 3020, etc.) for at least one or more demand management applications selected from a list of demand management applications including a demand planning application, a demand forecasting application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavior tracking application, a marketing analytics application, a location-based product or service targeting application, a collaborative filtering application, a product or service recommendation engine, etc.

[0307] Similarly, providing collaborative intelligence for a set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, a parts management application, and the like.

[0308] In embodiments, the management platform 102 may facilitate access, such as via a user interface 3020, to a set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence. In such embodiments, a user may seek to align supply with demand while ensuring profitability of a value chain for a category of goods 3010, etc. By providing access to artificial intelligence capabilities 1160, the management platform allows a user to focus on demand and supply applications while taking advantage of technologies such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, and deep learning systems.

[0309] In an embodiment, management platform 102 may provide, such as through user interface 3020, a set of adaptive intelligence systems 614 that provide tailored artificial intelligence 1160 for a set of demand management applications 824 and supply chain applications 812 for a category of goods 3020, for example, by (automatically) determining relationships between the demand management applications and the supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes. Artificial intelligence 1160 may be tailored by, for example, a set of data processing, artificial intelligence, and computation systems 634 available through adaptive intelligence systems 614.

[0310] In embodiments, the management platform 102 may be configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provides coordinated intelligence for the set of demand management applications 824 and supply chain applications 812 for a category of goods 3010. The set of artificial intelligence systems 1160 may provide collaborative intelligence such that at least one supply chain application in the set of supply chain applications 812 generates results that address at least one aspect of supply for at least one of the goods in the category of goods determined by at least one demand management application in the set of demand management applications 824. In an example, a behavior-tracking demand management application may generate results regarding the usage behavior 3010 of goods in the category of goods. The artificial intelligence system 1160 may process the behavioral data and conclude that there is a perceived need for greater consumer access to a second good in the category of goods 3010. This coordinated intelligence may be optionally automatically applied to the set of supply chain applications 812, for example, to allocate production resources or other resources in the value chain of the goods category to the second product. In an example, a distributor that handles stocking of retailer shelves may receive a new stocking plan that allocates more retail shelf space for a second product, such as by taking space away from a lower-margin product.

[0311] In embodiments, the set of artificial intelligence systems 1160, etc., may provide coordinated intelligence to the set of supply chain and demand management applications, for example, by determining a given temporal priority of demand management application outputs that influence the control of the supply chain applications so that a given temporal demand for at least one of the products in the product category 3010 can be met. Seasonal adjustments in the prioritization of demand application results are an example of a temporal change. Prioritization adjustments may also be localized, for example, a regional supply of tailgating equipment may be temporarily adjusted when a large college football team is playing in its home stadium and demand management application results suggest that small propane stoves are not currently in demand in the wider region.

[0312] The set of adaptive intelligence systems 614 that provide coordinated intelligence by providing artificial intelligence capabilities 1160 or the like may facilitate the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent trading, intelligent operations, remote control, analytics, monitoring, reporting, status management, event management, and process management. The set of adaptive intelligence systems 614 may be configured as a layer within a platform, wherein the artificial intelligence systems can act on or respond to data collected by and / or generated by other systems (e.g., data processing systems, expert systems, machine learning systems, etc.) in the adaptive intelligence systems layer.

[0313] In addition to providing collaborative intelligence configured for a particular product category, collaborative intelligence may be provided for specific value chain entities 652, such as supply chain operators, businesses, enterprises, etc., participating in the supply chain for the product category.

[0314] Providing tailored intelligence may include employing a neural network to process at least one of the inputs and outputs of a set of demand management and supply chain applications. The neural network may be used with demand applications such as demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, and product or service recommendation engines. The neural network may also be used in supply chain applications such as product timing management applications, product quantity management applications, logistics management applications, shipping applications, delivery applications, product order management applications, and parts order management applications. The neural network may provide tailored intelligence by processing data available from any of a number of value chain data sources for a category of goods, including, but not limited to, processes, bills of materials, weather, traffic, design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, product lifecycle management (PLM) systems, etc. A neural network configured to provide tailored intelligence may share adaptive capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that promotes such shared adaptation. In an embodiment, the neural network can facilitate providing available value chain / supply chain network resources for both a set of demand management applications and a set of supply chain applications.In embodiments, the neural network may provide tailored intelligence to improve at least one of a list of outputs: process output, application output, process outcome, application outcome, and the like.

[0315] Referring to FIG. 15 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provides coordinated intelligence through the application of a hybrid artificial intelligence system 3060, and optionally through the application of one or more expert systems, machine learning systems, or other artificial intelligence, for use with a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The hybrid adaptive intelligence system 614 may provide two types of artificial intelligence systems, Type A 3052 and Type B 3054, through a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the hybrid adaptive intelligence system 614 is selectable and / or configurable via the user interface 3020 such that one or more of the hybrid adaptive intelligence system 614 can operate on or in cooperation with a set of supply chain applications (e.g., demand management application 824 and supply chain application 812). The hybrid adaptive intelligence system 614 may include a hybrid artificial intelligence system 3060 that can include at least two types of artificial intelligence capabilities, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The hybrid adaptive intelligence system 614 may facilitate applying a first type of artificial intelligence system 1160 to a set of demand management applications 824 and a second type of artificial intelligence system 1160 to a set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence systems 1160 can operate independently, collaboratively, and optionally coordinate their operations to provide coordinated intelligence for the operation of a value chain that produces at least one of the goods in the category 3010 of goods.

[0316] In embodiments, user interface 3020 may include an interface for configuring hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by set of demand management applications 824 and / or set of supply chain applications 812) and feed them into at least one of two types of artificial intelligence systems of hybrid artificial intelligence system 3060, the types of which are described throughout this disclosure and in documents incorporated by reference herein, to enhance, control, improve, optimize, configure, adapt or otherwise influence the value chain of category of commodity 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes related to the value chain, category of commodity 3010, etc.

[0317] In an embodiment, the hybrid adaptive intelligence system 614 provides a plurality of separate artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof. In an embodiment, any of the plurality of separate artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification adaptive neural network, a predictive adaptive neural network, etc. As an example of the hybrid adaptive intelligence system 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824, and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. As an example of the hybrid artificial intelligence system 3060, the hybrid adaptive intelligence system 614 may provide a hybrid artificial intelligence system 3060 that includes a first type of artificial intelligence applied to the demand management applications 824 and that is different from a second type of artificial intelligence applied to the supply chain applications 812. The hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems, including a plurality of first type artificial intelligences (e.g., neural networks) and at least one second type artificial intelligence (e.g., expert system), etc. In an embodiment, the hybrid artificial intelligence system may configure a hybrid neural network that applies a first type of neural network for demand management applications 824 and a second type of neural network for supply chain applications 812. Additionally, the hybrid artificial intelligence system 3060 may provide two types of artificial intelligence for different applications, such as different demand management applications 824 (e.g., a sales management application and a demand forecasting application) or different supply chain applications 812 (e.g., a logistics management application and a production quality control application).

[0318] In embodiments, the hybrid adaptive intelligence system 614 may be applied as a separate artificial intelligence capability to a separate demand management application 824. By way of example, tailored intelligence from a hybrid artificial intelligence capability may be provided to a demand planning application with a feedforward neural network, a demand forecasting application with a machine learning system, a sales application with a self-organizing neural network, a future demand aggregation application with a radial basis function neural network, a marketing application with a convolutional neural network, an advertising application with a recurrent neural network, an e-commerce application with a hierarchical neural network, a marketing analytics application with a probabilistic neural network, a customer relationship management application with an associative neural network, etc.

[0319] Referring to FIG. 16 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of predictions 3070. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of predictions 3070 through the application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can deliver the set of predictions 3070 through a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with a coordinated set of value chain applications. The adaptive intelligence system 614 may include an artificial intelligence system that provides artificial intelligence capabilities known to be associated with artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The adaptive intelligence system 614 may facilitate applying adaptive intelligence functions to the coordination set of demand management applications 824 and supply chain applications 812, such as by generating a set of forecast values ​​3070 that may facilitate coordinating the two sets of value chain applications, or at least one demand management application and at least one supply chain application from their respective sets.

[0320] In an embodiment, the set of forecasts 3070 includes at least one forecast of an impact on a supply chain application based on the current state of the coordinated demand management applications, such as a forecast that demand for a good will decrease more quickly than previously predicted. The converse is also true in that the set of forecasts 3070 includes at least one forecast of an impact on a demand management application based on the current state of the coordinated supply chain applications, such as a forecast that a supply shortage of a good will likely affect a measure of demand for a related good. In an embodiment, the set of forecasts 3070 is a set of forecasts of an adjustment in supply needed to meet demand. Another forecast includes at least one forecast of a change in demand that will affect supply. Yet another forecast in the set of forecasts predicts a change in supply that will affect at least one of the set of demand management applications, such as a promotional application for at least one product in a category of products. A forecast in the set of forecasts may be as simple as a probability that the supply of a product in a category of products will not meet the demand set by a demand setting application.

[0321] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing a set of predictions for a coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict fault or problem conditions in demand management applications, such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict problem conditions in machines performing value chain operations (e.g., production machines, automated processing machines, packaging machines, shipping machines, etc.) based on a collection of machine operation information and machine preventive maintenance information.

[0322] In an embodiment, the set of predictions 3070 may be provided directly by the management platform 102 through a set of adaptive artificial intelligence systems.

[0323] In an embodiment, a set of forecasts 3070 may be provided for a tuned set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities to tune the set of demand management applications and supply chain applications.

[0324] In an embodiment, the set of forecasts 3070 may be a prediction of outcomes for operating a value chain with a collaborative set of demand management applications and supply chain applications for a category of goods, and may allow a user to run test cases of the collaborative set of demand management applications and supply chain applications to determine which sets are likely to produce desirable outcomes (viable candidates for applications of the collaborative set) and which sets are likely to produce undesirable outcomes.

[0325] Referring to FIG. 17 , a management platform of an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of classifications 3080. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of classifications 3080 through artificial intelligence, such as by application of artificial intelligence systems 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced, sold, resold, rented, leased, given away, serviced, recycled, refurbished, enhanced, and the like throughout the value chain. The adaptive intelligence systems 614 can deliver the set of classifications 3080 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of coordinated value chain applications. The adaptive intelligence systems 614 may include artificial intelligence systems that provide classification capabilities through any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference, among others. The adaptive intelligence system 614 may facilitate applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a set of classifications 3080 that may facilitate coordinating the two sets of value chain applications, or at least facilitating coordinating at least one demand management application and at least one supply chain application from their respective sets.

[0326] In an embodiment, the set of categories 3080 includes at least one classification of the current state of a supply chain application for use by the adjusted demand management application, such as a classification of a problem condition that may affect the operation of a demand management application, such as a marketing application. Such classifications are useful in determining how to adjust market expectations for a commodity that is likely to have a lower yield than previously expected. The converse may also be true, in that the set of categories 3080 includes at least one classification of the current state of a demand management application and its relationship to the adjusted supply chain application. In an embodiment, the set of categories 3080 is a set of classifications of adjustments to supply needed to meet demand; for example, an adjustment to production worker needs would be classified differently from an adjustment to a third-party logistics provider. Another classification may include at least one classification regarding a perceived change in demand and the resulting potential impact on supply management. Yet another classification in the set of categories may include the impact of a supply chain application on at least one of the set of demand management applications, such as a promotional application for at least one commodity in a commodity category. A classification in the set of categories may be as simple as classifying the likelihood that the supply of a commodity in a commodity category will not meet the demand set by a demand setting application.

[0327] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 for a coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to classify fault or problem conditions of a demand management application, such as classifying a lack of sufficient validated feedback. The probabilistic neural network may be used to classify problem conditions of machines performing value chain operations (e.g., production machines, automated processing machines, packaging machines, shipping machines, etc.) as related to at least one of machine operational information and preventive maintenance information.

[0328] In embodiments, the set of classifications 3080 may be provided directly by management platform 102 through a set of adaptive artificial intelligence systems. Additionally, the set of classifications 3080 may be provided for coordination of demand management applications and supply chain applications for categories of goods by applying artificial intelligence functions to coordinate the set of demand management applications and supply chain applications.

[0329] In an embodiment, the set of classifications 3080 may be a classification of outcomes for operating a value chain with a collaborative set of demand management applications and supply chain applications for a category of goods, and may enable a user to run test cases of the collaborative set of demand management applications and supply chain applications to determine which sets may produce outcomes classified as desirable (e.g., viable candidates for application of the collaborative set) and outcomes classified as undesirable.

[0330] In embodiments, the set of classifications may include a set of adaptive intelligence functions, such as a neural network, that may be adapted to classify information related to categories of goods. In one example, the neural network may be a multi-layer feedforward neural network.

[0331] In an embodiment, performing the classification may include classifying the discovered value chain entities as one of a demand center and a supply center.

[0332] In embodiments, the set of classifications 3080 may be achieved through the use of the artificial intelligence system 1160 to coordinate the set of coordinated demand management applications and supply chain applications. The artificial intelligence system may configure and generate the set of classifications 3080 as a means by which the demand management applications and supply chain applications can be coordinated. As an example, classifications of information flows throughout a value chain may be classified as relevant to both demand management applications and supply chain applications, and this common association may be a point of coordination between the applications. In embodiments, the set of classifications may be artificial intelligence-generated classifications of the results of supply chain operations that rely on the coordinated demand management applications 824 and supply chain applications 812.

[0333] Referring to FIG. 18 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for implementing automated control intelligence. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide automated control signaling 3092 for a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 may provide the automated control signals 3092 via a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 such that one or more of the adaptive intelligence systems 614 can automatically control a set of supply chain applications (e.g., the demand management applications 824 and the supply chain applications 812). The adaptive intelligence system 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0334] In an embodiment, the user interface 3020 may include an interface for configuring the adaptive intelligence system 614 to take input from selected data sources of the value chain 3094 (such as data sources used by the collaborative set of demand management applications 824 and / or the set of supply chain applications 812) and feed them to a neural network, such as the artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated by reference herein, to generate automated control signals 3092, for example, to enhance, control, improve, optimize, configure, adapt or otherwise influence the value chain of the category of commodity 3010. In an embodiment, the selected data sources of the value chain may be used to determine aspects of the automated control signals, such as the temporal coordination of control results associated with the value chain for at least the category of commodity 3010.

[0335] In one example, the set of automation control signals may include at least one control signal for automating the execution of a supply chain application, such as a production start, automated material ordering, inventory check, or invoice generation application, in a coordinated set of demand management and supply chain applications. In yet another example of automation control signal generation, the set of automation control signals may include at least one control signal for automating the execution of a demand management application, such as a product recall application, email distribution application, or the like, in a coordinated set of demand management and supply chain applications. In yet another example, the automation control signals may control the timing of a demand management application based on commodity supply conditions.

[0336] In embodiments, the adaptive intelligence system 614 may apply machine learning to the supply results to automatically adapt the set of demand management application control signals. Similarly, the adaptive intelligence system 614 can apply machine learning to the demand management results to automatically adapt the set of supply chain application control signals. The adaptive intelligence system 614 may provide further processing for automatic control signal generation, such as by applying artificial intelligence to determine aspects of the value chain that affect the automatic control of the coordinated set of demand management applications and supply chain applications for a category of goods. The determined aspects may be used in the generation and operation of automatic control intelligence / signals, such as by filtering value chain information for aspects that do not affect the targeted demand management and supply chain applications.

[0337] For example, automated controls of a supply chain application may be limited by policies, operational limits, safety constraints, etc. A set of adaptive intelligent systems may determine ranges of supply chain application control values ​​within which the controls may be automated. In embodiments, the ranges may be associated with supply rates, supply timing rates, mix of products within a category of products, etc.

[0338] Described herein are embodiments for using artificial intelligence systems or capabilities to identify, configure, and adjust automated control signals. Such embodiments may further include a closed loop of feedback (e.g., status information, output information, outcomes, etc.) from a coordination set of demand management and supply chain applications, optionally processed with machine learning, used to adapt the automated control signal for at least one commodity within a category of commodities. The automated control signal may be adapted based on an indication of feedback from the supply chain applications that, for example, an item's yield suggests a production problem. In this example, the automated control signal affects production rate, and the feedback may cause the signal to automatically self-adjust to a slower production rate until the production problem is resolved.

[0339] Referring to FIG. 19 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and exemplary interconnections for providing information routing recommendations. The management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a set of information routing activities, the information including outcomes, parameters, routing activity information, etc. Within the set of value chain networks 3102, a selected value chain network 3104 is selected for which at least one information routing recommendation 3130 is provided. The artificial intelligence system 1160 may include a machine learning system and may be trained using a training set obtained from the outcomes, parameters, and routing activity information of the network data 3110 for the set of value chain networks 3102. The artificial intelligence system 1160 may further provide the information routing recommendation 3130 based on the current state 3120 of the selected value chain network 3104. The artificial intelligence system can use machine learning to train on information transaction types within the set of value chain networks 3102, thereby learning appropriate factors for different transaction types (e.g., real-time inventory updates, buyer credit checks, engineering sign-offs, etc.) and contributing to information routing recommendations accordingly. The artificial intelligence system can also use machine learning to train on information value for different types and / or classes of information routed within and throughout the set of value chain networks 3102. Information may be evaluated on a wide range of factors, such as the timing of information acquisition and the timing of information consumption, as well as information content-based value, such as information that prevents a value chain network element (e.g., a production provider) from taking a desired action (e.g., starting mass production without a work order). Thus, information routing recommendations can be based on training on transaction type, information value, and combinations thereof.These are merely exemplary information routing recommendation training and recommendation basis elements presented herein without limitation to other elements for training and recommendation basis.

[0340] In embodiments, the artificial intelligence system 1160 may provide information routing recommendations 3130 based on transaction type, transaction type and information type, network type, etc. The information routing recommendations may be based on a combination of factors such as information type and network type, such as when an information type (streaming) is incompatible with a network type (small transactions).

[0341] In embodiments, the artificial intelligence system 1160 can use machine learning to develop an understanding of the networks in the selected value chain network 3104, such as network topology, network load, network reliability, and network latency. This understanding can be combined with, for example, detected or expected network conditions to form an information routing recommendation. Aspects such as the presence of edge intelligence in the value chain network 3104 can influence one or more information routing recommendations. As an example, the type of information may be incompatible with the type of network; however, the network may be configured with edge intelligence that can be leveraged by the artificial intelligence system 1160 to adapt the format of routed information to be compatible with the type of network in question. This is also an example of network resources (e.g., presence, availability, and capacity), such as edge computing, server access, and network-based storage resources, being more general considerations for information routing recommendations. Similarly, value chain network entities can influence the information routing recommendation. In embodiments, the information routing recommendation may avoid routing information that is confidential to a first supplier in the value chain through network nodes controlled by the supplier's competitors. In an embodiment, the information routing recommendation may include routing the information to a first node where the information is partially consumed and partially processed for further routing, such as by splitting the partially processed portions into destination-specific information sets for further routing.

[0342] In embodiments, the artificial intelligence system 1160 can provide information routing recommendations based on goals, such as value chain network goals and information routing goals. The goal-based information routing recommendations may include routing goals such as a quality of service routing goal, a routing reliability goal (which may be measured based on a transmission failure rate, etc.). Other goals may include a latency measure associated with one or more candidate routes. Information routing recommendations may be based on information availability in the selected value chain network, such as when the information is available and when it needs to be delivered. For information available well in advance of when it is needed (e.g., an overnight production report routable at 2:00 AM is first needed by 7:00 AM), routing recommendations may include the use of lower-cost resources, the likelihood of short delays in routing, etc. Information available shortly before it is needed (e.g., product test results needed within a few hundred milliseconds of the test completion time to maintain production uptime, etc.) may be included.

[0343] Information routing recommendations may be formed by the artificial intelligence system 1160 based on information persistence factors, such as the time the information is available for immediate routing within the value chain network. Information routing recommendations driven by information persistence may select network resources based on availability, cost, etc. during the time period of information persistence.

[0344] Information value and its impact on information value may influence information routing recommendations. As an example, information that is valid for a single shipment (e.g., production volume of a product) may lose substantial value once that shipment is satisfactorily received. In such an example, information routing recommendations may indicate routing the relevant information to all of the highest priority consumers of that information while it is still valid. Similarly, the routing of information that is consumed by multiple value chain entities may need to be coordinated so that each value chain entity receives the information at a desired time / moment, such as during the same production shift, at the start of work (which may be different if the entities are in different time zones).

[0345] In embodiments, information routing recommendations may be based on the topology of the value chain, based on the location and availability of network storage resources, and so on.

[0346] In embodiments, one or more information routing recommendations may be adapted while the information is being routed based on, for example, changes in network resource availability, discovery of network resources, dynamic network loading, priority of recommendations generated after the information for the first recommendation is in-routed, etc.

[0347] Referring to FIG. 20 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for semi-aware problem recognition of pain points in a value chain network. The management platform includes a set 3152 of value chain network entities from which entity-related data 3160 is collected, including outcomes, parameters, activity information, etc., related to the entities. Within the set 3152 of value chain network entities, a set 3154 of selected value chain network entities is selected in which at least one pain point problem state 3172 has been detected. The artificial intelligence system 1160 can be trained on a training set obtained from the entity-related data 3160, including training on outcomes related to the value chain entities, e.g., parameters related to value chain operation, value chain activity information, etc. The artificial intelligence system may further employ machine learning to facilitate learning of problem state factors 3180 that may characterize the problem state input as training data. These factors 3180 may be further used by an instance of artificial intelligence 1160' running on a computing resource 3170 that is local to the value chain network entity experiencing the pain point problem / outcome. The goal of this configuration of artificial intelligence systems, datasets, and value chain networks is to recognize problem conditions in selected portions of the value chain.

[0348] In embodiments, recognizing a problem state may be based on a variance analysis of value chain measures (e.g., load, wait time, delivery time, cost, etc.), particularly variances occurring over time in a particular measure. Variances exceeding a variance threshold (e.g., an arbitrarily dynamic range of results for a value chain operation, such as production, shipping, customs clearance, etc.) may indicate a pain point.

[0349] In addition to detecting problem conditions, the platform 102 predicts pain points based at least in part on correlations with the detected problem conditions, such as through semi-perceptual problem recognition methods. Correlations may be derived from the value chain, such as a shipper being unable to deliver international goods until they are processed by customs, or a sales forecast cannot be reliably provided without high-quality field data. In embodiments, the predicted pain points may be points in value chain activities further along the supply chain, activities occurring in related activities (e.g., tax planning related to tax laws), etc. Predicted pain points may be assigned a risk value based on the aspect of the detected problem condition and the correlation between the predicted pain point activity and the problem condition activity. If a production operation can receive materials from two suppliers, a problem condition with one of the suppliers may indicate a low risk of a pain point in the use of the material. Similarly, if a demand management application indicates high demand for a product and a problem is detected in the information underlying that demand, the risk of excess inventory (pain point) may be high, depending, for example, on how far along the value chain the product is.

[0350] In embodiments, semi-sensory problem recognition can include more than just linking data and operational states of entities engaged in the value chain. Problem recognition can also be based on human factors, such as perceived stress of production supervisors, shippers, etc. Human factors for use in semi-sensory problem recognition can be collected from sensors (e.g., wearable physiological sensors, etc.) that facilitate detection of human stress levels, etc.

[0351] In embodiments, semi-perceptual problem recognition can also be based on unstructured information, such as digital communications, voice messages, etc., that may be shared, sent, or received among people involved in value chain operations. As an example, natural language processing of email communications between employees within a company may indicate, for example, the level of discomfort with a supplier in the value chain. While data related to a supplier (e.g., on-time production, quality, etc.) may be within a variance considered acceptable, information within this unstructured content may indicate a potential pain point, such as a personal issue with a key participant at the supplier. Natural language processing, artificial intelligence, and optionally machine learning can be employed to enhance problem state recognition.

[0352] In embodiments, semi-perceptual problem recognition may be based on an analysis of variances of measures of value chain operations / entities / applications, including variances of a given measure over time, variances of two related measures, etc. In embodiments, variances of results over time may indicate a problem state and / or suggest a pain point. In embodiments, an artificial intelligence-based system may determine an acceptable range of result variance and apply that range to measurements of a select set of value chain network entities, such as entities that share one or more affinities, to facilitate detection of a problem state. In embodiments, the acceptable range of result variance may indicate a problem state trigger threshold that can be used by a local instance of the artificial intelligence to signal a problem state. In such a scenario, a problem state may be detected when at least one measurement of a value chain activity / entity, etc., is greater than a problem state threshold determined by the artificial intelligence. Variance analysis for problem condition detection may include detecting variances in start / end times of scheduled value chain network entity activities, variances in at least one of production time, production quality, production rate, production start time, production resource availability or trends, variances in measurements of shipping supply chain entities, variances in transfer times from one transportation mode to another (e.g., when the variance exceeds a transportation mode problem condition threshold), variances in quality testing, etc.

[0353] In embodiments, the semi-sensory problem recognition system may include machine learning / artificial intelligence prediction of pain points that are further correlated along the supply chain with the detected pain points, such as the risk and / or need for overtime, expedited shipping, discounts on product prices, etc.

[0354] In an embodiment, the machine learning / artificial intelligence system may process results, parameters, and data collected from a set of data sources related to a set of value chain entities and activities to detect at least one pain point selected from a list of pain points consisting of late shipments, damaged containers, damaged goods, wrong goods, customs delays, unpaid duties, weather events, infrastructure damage, waterway closures, incompatible infrastructure, congested ports, congested handling infrastructure, congested roads, congested distribution centers, rejected goods, returns, waste, wasted energy, wasted labor, untrained labor, poor customer service, empty return vehicles, excessive fuel prices, excessive tariffs, etc.

[0355] Referring to FIG. 21 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for automating the coordination of a set of value chain network activities for a set of products of an enterprise. The management platform includes a collection of networked value chain network entities 3202 that generate activity information 3208 that is used by an artificial intelligence system 1160 to provide automated coordination 3220 of value chain network activities 3212 for a set of products 3210 for the enterprise 3204. In an embodiment, a value chain monitoring system 614 may monitor the activities of the set of networked value chain entities 3202 and, in cooperation with a data collection and management system 640, collect and store value chain entity monitoring information, such as activity information, configuration information, etc. This collected information may be configured as activity information 3208 for a set of activities related to the set of products 3210 of the enterprise 3204. In an embodiment, the artificial intelligence system 1160 may use an application programming connection facility 642 to automate access to the monitored activity information 3208.

[0356] A value chain may include multiple interconnected entities, each performing several activities to complete the value chain. While humans play a significant role in some activities within a value chain network, more automated coordination and unified orchestration of supply and demand can be achieved using artificial intelligence-based systems to coordinate supply chain activities (e.g., including systems based on machine learning, expert systems, self-organizing systems, and the like, as described herein and in documents incorporated by reference). The use of artificial intelligence not only provides greater capabilities to end users, but can also further enrich the emerging phenomenon of self-adaptive systems, including Internet of Things (IoT) devices and intelligent products, that can play a key role in automatically coordinating supply chain activities.

[0357] For example, an IoT system deployed at a fulfillment center 628 may interface with an intelligent product 650 that takes customer feedback regarding the product 650, and an application 630 for the fulfillment center 628, upon receiving customer feedback via a connection path to the intelligent product 650 about a problem with the product 650, may initiate a workflow to take corrective action on the product 650 before it leaves the fulfillment center 628. The workflow may be configured by an artificial intelligence system 1160 that analyzes the problem with the product 650, develops an understanding of the value chain network activities that produce the product, determines resources required for the workflow, coordinates with inventory and production systems to adapt any existing workflow, etc. The artificial intelligence system 1160 may further interface with a demand management application to address temporary impacts on product availability, etc.

[0358] In embodiments, automated coordination of value chain network activities for a set of products for an enterprise may rely on the coordination intelligence methods and systems described herein, including, optionally, using artificial intelligence to provide coordinated intelligence, coordinate activities, etc., to facilitate coordination of demand management activities, supply chain activities, etc. As an example, artificial intelligence may facilitate determining relationships between value change network activities based on inputs used by the activities and results produced by the activities. Artificial intelligence may be integrated with and / or work in concert with platform activities, such as value chain network entity activities, to continuously monitor activities, identify temporal aspects requiring adjustment (e.g., when a change in supply temporarily impacts demand activity), and automate such adjustments. Automated coordination of value chain network activities within and between value chain network entity activities can benefit from advanced artificial intelligence systems, which may enable the use of different artificial intelligence capabilities for any value chain set of entities, applications, or conditions. The use of hybrid artificial intelligence systems can benefit by applying one or more types of intelligence to a set of conditions to facilitate automated selection by humans and / or computers. Artificial intelligence may further enhance the automated coordination of value chain network entity activities through intelligent operations such as generating a set of predictions, a set of classifications, generating automated control signals (such as those that may be communicated between value chain network entities), etc. Other exemplary artificial intelligence-based influences on the automated coordination of value chain network entity activities include machine learning-based information routing and recommendations therefor, semi-awake problem recognition based on both structured (e.g., production data) and unstructured (e.g., human emotion) sources, and the like.The artificial intelligence system may facilitate automatic adjustment of a set of products or the enterprise's value chain network entity activities based on adaptive intelligence provided by the platform for a category of goods into which the enterprise's set of products may be grouped. As an example, adaptive intelligence may be provided by the platform for a curtain hook category of goods, and the enterprise's product set may include a line of adaptive curtain hook hangers. Through the understanding developed of the overall curtain hook category, artificial intelligence capabilities can be applied to the enterprise's value chain network activities to automate aspects of the value chain, such as the exchange of information between activities.

[0359] [Digital Twin System in Value Chain Entity Management Platform] 22 , the adaptive intelligence layer 614 can include a value chain network digital twin system 1700, which can include a set of components, processes, services, and interfaces, and other elements for the development and deployment of digital twin capabilities for visualization of various value chain entities 652, environments, and applications 630, as well as collaborative intelligence (including artificial intelligence 1160, edge intelligence 1400, analytics, and other capabilities) and other value-added services and capabilities enabled or facilitated by the digital twin 1700. Without limitation, the digital twin 1700 can be used and / or applied to each of the processes managed, controlled, or mediated by each of the set of applications 630 in the platform application layer.

[0360] In an embodiment, digital twin 1700 takes advantage of the presence of multiple applications 630 within the value chain management platform layer 604, allowing the set of applications to share data sources (such as data storage layer 624) and other inputs (such as from monitoring layer 614) collected about value chain entities 652, and may also share outputs, events, state information and outputs, which collectively may provide a much richer environment for enriching the content within digital twin 1700 through the use of artificial intelligence 1160 (including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference), and through the use of content collected by monitoring layer 614 and data collection systems 640.

[0361] In embodiments, digital twin 1700 may be used in connection with a sharing or convergence process between various sets of applications 630 in application layer 604, such as, but not limited to, a convergence process including security application 834 and inventory control application 820, automating the integration of blockchain-based application 844 with facility management application 850, and many others. In embodiments, the convergence process may also include a shared data structure for multiple applications 630 (including those that track the same transactions on the blockchain but may consume different subsets of available attributes of data objects maintained on the blockchain or that use a common set of nodes and links of a knowledge graph) that may be connected with digital twin 1700 so that digital twin 1700 is updated accordingly. For example, a transaction indicating a change in ownership of entity 652 may be stored on the blockchain and used by multiple applications 630 that may be connected and shared with digital twin 1700 so that digital twin 1700 is updated accordingly to enable role-based access control, role-based authorization for remote control, identity-based event reporting, etc. In an embodiment, a converged process may include a shared process flow across applications 630 that includes a subset of a larger flow involving one or more of the set of applications 630 that may be connected to and shared with digital twin 1700 so that digital twin 1700 may be updated accordingly. For example, an inspection flow for value chain network entity 652 may provide services to analytics solutions 838, asset management solutions 814, and others.

[0362] In embodiments, digital twin 1700 may be provided for a wide range of value chain network applications 630 mentioned throughout this disclosure and the documents incorporated herein by reference. The development environment for digital twin 1700 may include a set of interfaces for developers to configure the artificial intelligence system 1160 to take inputs from selected data sources in data storage layer 624 and events or other data from monitoring systems layer 614 and provide them for inclusion in digital twin 1700. The development environment for digital twin 1700 may be configured to capture outputs and results from various applications 630. Value Chain Network Digital Twins

[0363] Referring to FIG. 23 , any of the value chain network entities 652 can be depicted in a set of one or more digital twins, such as by populating the digital twin 1700 with value chain network data objects 1004, such as event data 1034, state data 1140, or other data related to the value chain network entity 652, application 630, or components or elements of the platform 604 described throughout this disclosure.

[0364] Thus, the platform 604 may include, integrate, assemble, manage, control, coordinate, or otherwise handle any of a wide variety of digital twins 1700, such as a distribution twin 1714 (e.g., representing a distribution facility, assets, objects, workers, etc.), a warehouse twin 1712 (e.g., representing a warehouse facility, assets, objects, workers, etc.), a port infrastructure twin 1714 (e.g., representing a seaport, airport, or other facility, assets, objects, workers, etc.), a shipping facility twin 1720, an operations facility twin 1722, a customer twin 1730 (e.g., representing physical, behavioral, demographic, psychographic, financial, historical, affinities, interests, and other characteristics of a customer group or individual customer), a worker twin 1740 (e.g., representing a worker's physical attributes, physiological data, state data, psychographic information, emotional state, fatigue / energy state, attention state, skills, training, abilities, roles, authorities, responsibilities, work status, activities, and other attributes and stakeholders), etc. Wearable / Portable Device Twin 1750, Process Twin 1760, Machinery Twin 1770 (e.g., various machines used to support the value chain network 668), Product Twin 1780, Origin Twin 1560, Supplier Twin 1630, Supply Factor Twin 1650, Offshore Facility Twin 1572, Floating Asset Twin 1570, Boathouse Twin 1620, Destination Twin 1562, Fulfillment Twin 1600, Delivery System Twin 1610, Demand Requirement These include Factors Twin 1640, Retailers Twin 1790, E-commerce and Online Sites and Operators Twin 1800, Waterways Twin 1810, Roadways Twin 1820, Railroads Twin 1830, Aviation Facilities Twin 1840 (including twins of aircraft, runways, airports, hangars, warehouses, airways, fueling facilities, and other assets, objects, workers, etc. used in connection with the air transport of Products 650), Autonomous Vehicles Twin 1850, Robotics Twin 1860, Drones Twin 1870, and Logistics Factors Twin 1880.Each of these may have the characteristics of a digital twin described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in state of associated physical objects or other entities, providing functionality for modeling the behavior or interactions of associated physical objects or other entities, enabling simulation, providing a representation of state, and many others.

[0365] In an exemplary embodiment, the digital twin system may be configured to generate various enterprise digital twins 1700 in relation to a value chain (e.g., specifically, value chain network entities 652). For example, a company that produces goods internationally (or across multiple facilities) may configure a set of digital twins 1700, such as a supplier twin depicting the company's supply chain, a factory twin for various production facilities, a product twin representing the products manufactured by the company, a distribution twin representing the company's distribution chain, and other suitable twins. In doing so, the company may define not only the structural elements of each respective digital twin, but also any system data corresponding to the structural elements of the digital twin. For example, when generating a production facility twin, the company may define the layout and spatial definition of the facility and any processes performed at the facility. The company may also define data sources corresponding to the value chain network entities 652, such as sensor systems, smart manufacturing equipment, inventory systems, and logistics systems that provide data related to the facility. The company may associate the data sources with elements of the production facility and / or processes occurring at the facility. Similarly, the company may define the structure, processes, and layout of its supply chain and distribution chains and connect relevant data sources, such as supplier databases and logistics platforms, to generate respective distribution chain and supply chain twins. An enterprise can further correlate these digital twins to have a view of its value chain. In embodiments, the digital twin system may run a simulation of the enterprise's value chain, incorporating real-time data from the enterprise's various value chain network entities 652.In some of these embodiments, the digital twin system may recommend decisions to a user interacting with the enterprise digital twin 1700, such as when to order a particular part to manufacture a particular product given the predicted demand for the manufactured product, when to schedule maintenance on a machine and / or when to replace a machine (e.g., when a digital simulation on the digital twin indicates that demand for a particular product may be lowest or that it will have the least impact on the enterprise's income statement), when to ship an item, etc. The foregoing examples are non-limiting examples of how a digital twin can ingest system data and run simulations to achieve one or more goals.

[0366] [Entity Discovery and Interaction Management] Referring to FIG. 24, the monitoring system layer 614, which includes various data collection systems 640 (such as IoT data collection systems, data collection systems that search social networks, websites, and other online resources, crowdsourcing systems, etc.), may include a series of entity discovery systems 1900, for example, for identifying a set of value chain network entities 652, identifying types of value chain network entities 652, identifying specific value chain network entities 652, etc., as well as for managing the identities of value chain network entities 652, including for resolving ambiguity (such as when a single entity is identified differently in different systems, when different entities are identified similarly, etc.), for entity ID deduplication, for entity ID resolution, for entity ID enrichment (such as enriching data objects with additional data collected about the entities within the platform), etc. Entity discovery 1900 may also include discovery of interrelationships between entities, such as how entities are connected (e.g., by what network connections, data integration systems, and / or interfaces), what data is exchanged between entities (including what types of data objects are exchanged, what common workflows involve the entities, what inputs and outputs are exchanged between entities, etc.), and what rules or policies govern the entities. Platform 604 may include a set of entity interaction management systems 1902, including one or more artificial intelligence systems (including any of the types described throughout this disclosure) for managing the set of interactions between entities discovered through entity discovery 1900, which learn on a training set of data to manage interactions between entities based on how the entities have been managed by human overseers or by other systems.

[0367] As one illustrative example among many possibilities, the entity discovery system 1900 can be used to discover network-connected cameras that show loading docks at facilities that produce products for a company and identify the interface or protocol needed to access a feed of video content from the cameras. The entity interaction management system 1902 can then interact with the interface or protocol to set up access to the feed and provide the feed to another system for further processing, such as having an artificial intelligence system 1160 process the feed to discover content related to the company's activities. For example, the artificial intelligence system 1160 may process image frames from the video feed to discover markings (e.g., produce labels, SKUs, images, logos, etc.), shapes (e.g., packages of a particular size or shape), activities (e.g., loading or unloading activity), etc. that may indicate that a product has moved through a loading dock. This information may substitute, augment, or be used to verify other information, such as RFID tracking information. Similar discovery and interaction management activities can be performed with any of the types of value chain network entities 652 described throughout this disclosure.

[0368] [Robotic Process Automation in Value Chain Networks] 25 , the adaptive intelligence layer 614 may include a robotic process automation (RPA) system 1442, which may include a collection of components, processes, services, interfaces, and other elements for the development and deployment of automation capabilities to various value chain entities 652, environments, and applications 630. Without limitation, the robotic process automation 1442 may be applied to each of the processes managed, controlled, or mediated by each of the set of applications 630 in the platform application layer, to the functions, components, workflows, processes of the VCNP 604 itself, processes involving the value chain network entities 652, and other processes.

[0369] In an embodiment, robotic process automation 1442 takes advantage of the presence of multiple applications 630 within the value chain management platform layer 604, allowing the set of applications to share data sources (such as in the data storage layer 624) and other inputs (such as from the monitoring layer 614) collected about the value chain entities 652, as well as outputs, events, and state information, which collectively may provide a much richer environment for process automation, such as through the use of artificial intelligence 1160 (including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference). For example, the asset management application 814 may use the robotic process automation 1442 to automate an asset inspection process that is typically performed or overseen by a human (e.g., by automating a process that includes visual inspection using video or still images, such as from a camera displaying images of the entities 652), such as when the robotic process automation 1442 system is trained to automate the inspection by observing the interaction of a set of human inspectors or supervisors with an interface used to identify, diagnose, measure, parameterize, or otherwise characterize possible defects or advantageous characteristics of equipment or other assets. In an embodiment, the interactions of the human inspectors or supervisors may include a labeled dataset in which labels or tags indicate the type of defect, advantageous characteristic, or other characteristic, such that a machine learning system can learn to identify the same characteristics using the training dataset, which may be used to automate the inspection process such that defects or advantageous characteristics are automatically classified and detected in a set of video or still images, which may then be used to flag items requiring further inspection, items that should be rejected, items that should be disclosed to a prospective buyer, items that should be repaired, etc., within the value chain network asset management application 814.In embodiments, robotic process automation 1442 may include multi-application or cross-application sharing of inputs, data structures, data sources, events, states, outputs, or results. For example, asset management application 814 may receive information from marketplace application 854 that may enrich the robotic process automation 1442 of asset management application 814, such as information regarding the current characteristics of items from a particular vendor in the supply chain for an asset, which may assist in entering characteristics about the asset to facilitate the inspection process, negotiation process, delivery process, etc. These and many other examples of multi-application or cross-application sharing for robotic process automation 1442 across applications 630 are encompassed by this disclosure. Robotic process automation 1442 may be used with various functionality of VCNP 604. For example, in some embodiments, robotic process automation 1442 may be described as training a robot to operate and automate tasks that were previously dominated, at least to a significant extent, by humans. One of these tasks may be used to train a robot that can train other robots. The robotic process automation 1442 may be trained (e.g., through machine learning) to mimic interactions on a training set, and then have this trained robotic process automation 1442 (e.g., a trained agent or a trained robotic process automation system) perform these tasks previously performed by humans. For example, the robotic process automation 1442 may utilize software that may provide software interaction observations (such as mouse movements, mouse clicks, cursor movements, navigational actions, menu selections, keyboard typing, and many others), such as a customer 714 purchasing a product, recorded and / or tracked by the software interaction observation system 1500.This may include monitoring a user's mouse clicks, mouse movements, and / or keyboard typing to learn to perform the same clicks and / or typing. In another example, the robotic process automation 1442 may utilize software to learn physical interactions with robots and other systems to train the robotic system to sequence or undertake the same physical interactions. For example, a robot may be trained to reconstruct a set of bearings by having it watch a video of a person performing this task. This may include tracking physical interactions as well as tracking interactions at the software level. The robotic process automation 1442 may understand what the underlying capabilities are being deployed so that the VCNP 604 pre-configures a combination of neural networks that can be used to replicate the performance of human capabilities.

[0370] In embodiments, robotic process automation may be applied to a sharin...

Claims

1. 1. A robot fleet management platform for configuring robot fleet resources, comprising: a set of one or more processors that execute a set of computer readable instructions; The set of one or more processors: a job configuration system that receives a job request and determines a set of robotic tasks to be performed by the robot fleet based on job content associated with the job request and at least one fleet objective in a set of fleet objectives; a fleet configuration proxy service that applies a fleet configuration service to the set of robot tasks and the job content to generate a fleet resource configuration data structure for the job request; a fleet intelligence layer that operates a set of intelligence services to generate at least one recommended robot task and associated context information that facilitates robot selection and task sequencing in a robot task workflow; a job workflow system that generates a workflow that defines an execution order of the robot tasks based on the fleet resource configuration data structure and the set of robot tasks; a workflow simulation system configured to simulate performance of the job based on the workflow and a job execution simulation environment to generate simulation results, the workflow simulation system using the simulation results to recursively redefine one or more of the set of tasks, the fleet resource configuration data structure, or the workflow until the simulation results satisfy a second fleet objective of the set of fleet objectives corresponding to the job request; and a job execution plan generator that collectively generates a job execution plan based on the set of tasks, the fleet resource configuration data structure, and the workflow in response to the simulation results satisfying the set of fleet objectives.

2. 2. The robot fleet management platform of claim 1, wherein the job configuration system includes a job analysis system that applies content and structure filters to job content received in connection with a job request to identify portions thereof that are suitable for robotic automation.

3. 3. The robot fleet management platform of claim 2, wherein the job configuration system includes a task definition system that establishes a set of robot tasks, each of which defines at least a robot type and a task objective, the set of robot tasks being based at least in part on the portions of the job request that are suitable for robotic automation and that satisfy a first fleet objective of the set of fleet objectives.

4. 2. The robot fleet management platform of claim 1, wherein the fleet resource configuration data structure defines a set of task associations and a set of robot adaptation instructions, each of the task associations associating at least one robot operating unit with a respective robot task in the set of robot tasks, and the set of robot adaptation instructions defines how to adapt one or more robot operating units of a robot fleet to perform respective tasks assigned to the robot.

5. 2. The robot fleet management platform of claim 1, wherein the workflow simulation system applies the workflow to generate the simulation results in the job execution simulation environment, the job execution simulation environment including a digital model of the robot operating units assigned to the robot fleet and a digital model of the task definition.

6. The robotic fleet management platform of claim 1 , wherein the job configuration system interacts with the intelligence layer to suggest alternative tasks that satisfy a second fleet objective.

7. 2. The robot fleet management platform of claim 1, wherein the job configuration system interacts with the intelligence layer to optimize at least one of a robot type and a task objective based on at least one of the set of fleet objectives.

8. 8. The robotic fleet management platform of claim 7, wherein the first fleet objective comprises a fleet resource utilization criterion.

9. The robot fleet management platform of claim 1 , wherein the job configuration system receives from the fleet configuration proxy service a particular robot type to use when performing the robot task.

10. 10. The robot fleet management platform of claim 9, wherein the job configuration system configures the set of robot tasks based on the specific robot type provided by the fleet configuration proxy service.

11. 2. The robot fleet management platform of claim 1, wherein the job configuration system generates a data structure for each task in the set of tasks for use by the workflow simulation system that includes a reference to a digital twin for at least one of the task and at least one robotic operating unit for performing the task.

12. 2. The robot fleet management platform of claim 1, wherein the job configuration system generates a data structure for each task in the set of tasks that identifies at least one of a type of robot and a robotic operating unit to perform the task, and a configuration data structure for configuring a robot to perform the task.

13. 2. The robot fleet management platform of claim 1, wherein the job configuration system generates a data structure for each task in the set of tasks and stores the data structures in a library of robot tasks indexed by information indicative of the job request and an identifier for at least one of the robot type and the robot operating unit.

14. 2. The robot fleet management platform of claim 1, wherein the job configuration system matches robot capabilities with requirements of constraints specified in a job request when identifying the type of robot to fulfill the task objective.

15. 2. The robot fleet management platform of claim 1, wherein the job configuration system generates a plurality of robot tasks for a plurality of different robot types to accomplish a task objective.

16. 2. The robot fleet management platform of claim 1, wherein the job configuration system queries a library of robot tasks for candidate robot tasks that satisfy the task objective and interacts with the fleet configuration proxy service to select a robot task from the candidate robot tasks based on the at least one fleet objective.

17. 20. The robotic fleet management platform of claim 16, wherein the at least one fleet objective is compatibility with available robotic operating units.

18. 2. The robot fleet management platform of claim 1, wherein the job configuration system queries a library of robot tasks for candidate robot tasks that satisfy the task objective and interacts with the fleet intelligence layer to select a robot task from the candidate robot tasks based on suitability of the candidate robot tasks for achieving the task objective.

19. The robotic fleet management platform of claim 1 , wherein the job configuration system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining the set of tasks.

20. The robot fleet management platform of claim 1 , wherein the job workflow system references descriptive information in a sensor detection package indicating a preferred sequence of sensing tasks when defining the workflow for a robot task.

21. 2. The robot fleet management platform of claim 1, wherein the job workflow system generates the workflow for robot tasks based on a dependency of the second task on a first task to satisfy an objective of the second task.

22. 2. The robot fleet management platform of claim 1, wherein the job workflow simulation system manipulates digital twins of tasks in the set of tasks to determine an optimized workflow ordering of tasks.

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