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 with related applications) This application claims priority to U.S. Provisional Patent Application No. 63 / 127,983, filed on 18 December 2020, and U.S. Provisional Patent Application No. 63 / 185,348, filed on 6 May 2021. This application also claims priority to Indian Patent Application No. IN202111029964, filed on 3 July 2021, and Indian Patent Application No. IN202111036187, filed on 10 August 2021. The entire disclosure of the above applications is incorporated by reference.

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

[0003] Historically, many items across various categories purchased and used by household consumers, businesses, and other customers were supplied in a relatively linear fashion. Manufacturers and other suppliers of finished goods, components, and other items would hand over the goods to carriers, freight forwarders, etc., who would then deliver them to temporary storage warehouses, retail stores where customers purchased them, or directly to the customer's location. Manufacturers and retailers engaged in various sales and marketing activities, such as product design, shelf and advertising placement, and pricing, to stimulate and meet customer demand.

[0004] Product orders were fulfilled by manufacturers through a supply chain as depicted in Figure 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 the origin 102 upon order. Products 130 passed through the supply chain and were transported and stored via various transport facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112, and delivery systems 114 including trucks and other vehicles, trains, etc. Often, maritime facilities and infrastructure such as ships, barges, docks, and ports provided waterway transport between the origin 102 and one or more destinations 104.

[0005] Organizations have access to a virtually limitless amount of data. With the emergence of smart connected devices, wearable technology, and the Internet of Things (IoT), the amount of data available to organizations planning, overseeing, managing, and operating value chain networks has increased dramatically, and is likely to continue to increase. For example, in manufacturing facilities, warehouses, campuses, or other operating environments, there may be hundreds or even thousands of IoT sensors providing various metrics, such as vibration data to measure 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 others. Furthermore, with the proliferation of wearable technology, wearables may enable organizations to understand workers' movements, health indicators, physiological status, activity levels, movements, and other characteristics. Furthermore, as organizations implement CRM systems, ERP systems, business systems, information technology systems, advanced analytics, and other systems that leverage information and information technology, they will gain increasingly broad access to other large datasets, including marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, and supply chain data (including datasets generated by or for the organization and third-party datasets).

[0006] The presence of more data and new types of data offers organizations many opportunities to achieve a competitive advantage, but it also presents problems such as complexity and volume, which can overwhelm users and cause them to miss opportunities for insight. There is a need for methods and systems that enable companies not only to acquire data, but also to transform that data into insights, and to translate those insights into well-informed decision-making and the timely execution of efficient operations.

[0007] (Additive manufacturing) Additive manufacturing, encompassing 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 manufacture three-dimensional products from a design, reaching a finished part or system in any intermediate or subsequent step through a process of forming continuous layers of the product. The design may take the form of a data source, such as a computer-aided design package or an electronic 3D model created with a 3D scanner. 3D printing or other additive processes form the initial material layer, then add continuous material layers, where each new material layer is added on top of a pre-formed material 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 indicates otherwise.

[0008] Currently, many additive processes are available. These processes may differ in how they deposit continuous layers to create 3D products. They may also differ in the materials used to form the product. Metals (including alloys unless otherwise specified in the context, and special metals such as shape memory materials) are increasingly popular 3D printing materials. Common examples include titanium, stainless steel, aluminum, tool steel, Inconel, and cobalt-chromium. There are also methods that involve melting or softening the metal 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 a polymer filament or rod containing a large amount of metal powder is extruded from a nozzle (like FDM) to form a "green" part, which is then post-processed (degreasing and sintering) to create a fully metal part; (b) metal binder spraying, in which a liquid binder is applied to the powder layer using a print head; and (c) nanoparticle spraying, in which metal nanoparticles are sprayed in an ultrathin layer from an inkjet nozzle.

[0009] 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, regardless of the design data source or methodology. As a result, problems may arise such as the final 3D printed product not meeting customer expectations or product specifications, or low-quality 3D printed products or parts failing. These problems can also increase the operating costs of 3D printing service providers due to material waste, reduced throughput from machine downtime and unproductive printing time, and associated supply chain risks and inefficiencies. For example, 3D printed products often deform during or after manufacturing due to printing procedures or unoptimized printing parameters. Common problems stemming from inefficient manufacturing supply chains include fraud, delivery delays, contractual liability, and product recalls.

[0010] To ensure that final metal 3D printed products meet customer expectations regarding quality, cost, and delivery time, as well as manufacturer specifications, there is a need for smarter product design, manufacturing, supply chain, and demand management methods and systems. Furthermore, there is a need for improved methods and systems to monitor, manage, and optimize additive manufacturing capabilities across various stakeholders.

[0011] Conventional machine vision systems consist of a combination of optics, lighting, sensors, and software, and aim to replicate the function of the human eye. Such systems create an image of an object by capturing and analyzing reflected light 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 photosensitive elements manufactured on a silicon chip using integrated circuit technology. The sensor device converts the light entering through the camera lens into analog electrical signals corresponding to the light intensity. In this way, the object image is broken down into an array of individual image elements (pixels). Analog-to-digital converters are used to convert the analog voltage of the pixels into digital values. When the voltage level of each pixel is assigned either a value of 0 or 1 according to a certain threshold, it is called a binary system. On the other hand, in a grayscale system, each pixel is assigned up to 256 different values ​​depending on the intensity. Therefore, in addition to black and white, various shades of gray can be distinguished. A grayscale image can be considered to have one channel, represented by a two-dimensional matrix of pixels with pixel values ​​ranging from 0 to 255. A color image, on the other hand, represents the brightness and color of pixels in the image using the three primary colors: R (red), G (green), and B (blue). Thus, a color image has red, green, and blue (RGB) channels, each representing the RGB components of the image. The raw data captured by this image sensor is sent to an image processing system for analysis. The image processing system processes the raw data to extract information useful for analyzing the image and makes decisions regarding such analysis. The image processing system may include pre-processing 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. Furthermore, the image may be analyzed using machine learning or other algorithms to identify one or more objects within the image and determine the position and orientation of such objects.

[0012] Vision technology has advanced significantly in recent years, much of which concerns the processing of image data captured by vision sensors. This is thought to be due to the use of big data, advanced machine learning algorithms such as convolutional neural networks (CNNs), and graphics processing units (GPUs) for image data processing. However, conventional vision technology has significant limitations, particularly in acquiring raw data about objects and scenes. For example, the optical lenses of conventional vision systems rely on simple focusing techniques, attempting to extract information in a linear and obtuse-angled manner. Attempts to focus on objects in an image ultimately result in the loss of a large amount of information and other optical properties. Therefore, there is a need to capture previously lost or inferred information and generate images that are not "perfect" to the eye, but "rich" to algorithms. Furthermore, there is a need for richer object recognition and composite vision applications where conventional vision technologies have proven insufficient, including object recognition in dynamic environments such as when objects or vision systems are moving, as in mobile and robotic use cases; recognition of three-dimensional (3D) objects by acquiring depth data; prediction of object attributes such as depth, orientation, and motion; recognition of small objects; recognition of facial features; recognition of objects in power-constrained or network-constrained environments; and other use cases where conventional machine vision systems and methods are unsuitable.

[0013] Furthermore, automation is revolutionizing the value chain of almost every item, with robotics at its heart. While the role of physical robots in manufacturing has been expanding for many years, typical deployments have focused on stationary robots performing predetermined tasks in fixed positions, such as painting and welding on assembly lines. While this limited role has brought significant improvements in quality, cost, and productivity, and continues to do so, 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 advancements in robotics (including hardware and software robotics), can create innovative arrays of highly functional, autonomous robots with conversational capabilities. Emerging and future robot classes and capabilities offer increasingly expanding robot use cases and management platform opportunities, enabling the automatic configuration, organization, deployment, and control of robots and robot fleets to securely deliver reliable services, including contract services that access robot fleet capabilities on a "Robotics as a Service" platform. [Overview of the project]

[0014] The robot fleet management platform includes a set of data stores that store a governance library, which defines a set of governance standards, including at least one set of security standards, legal standards, ethical standards, regulatory standards, quality standards, or engineering standards, that apply to decisions made by one or more intelligence services. 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 a governance-enabling intelligence layer that receives and responds to intelligence requests received from each intelligence service client. 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, partially based on a set of governance criteria. In response to intelligence requests, the intelligence layer returns a collectively determined decision by the artificial intelligence service, ensuring that the decision is based on a set of intelligence service data sources and a set of analyses.

[0015] In other features, the intelligence layer controller is configured to receive intelligence requests from intelligence service clients indicating the requested decision, determine zero or more governance criteria implied by the 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 zero or more predefined analyses to the artificial intelligence service. The zero or more governance criteria are selected from governance criteria defined in the 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 AI ​​service in response to decisions made by the AI ​​service until the requested decision is provided by the AI ​​service. In other features, the intelligence layer is further configured to determine a set of intelligence layer data sources based on the type of decision requested. In other features, the requesting intelligence service client provides a set of intelligence layer data sources along with the request. In other features, the decisions provided by the intelligence layer define the respective actions to be taken by each intelligence service client. In other features, each action includes an action that requests human intervention.

[0017] Other features include: Each action includes a non-adaptive predefined action; Each action includes a domain-specific action in response to each request; In other features, the intelligence service client includes a security system that requests a classification of potential security risks; In other features, the intelligence service client includes a resource supply system that requests recommendations for resources to support a robot fleet; In other features, the intelligence service client includes a logistics system that requests logistics-based recommendations for one or more robot fleets; In other features, the intelligence service client includes a job configuration system that requests proposed job configurations given a job request; In other features, the intelligence service client includes a fleet configuration system that requests proposed fleet configurations given a set of tasks to be completed by a robot fleet; In other features, the intelligence service client includes a robot operating unit deployed by the robot fleet management platform.

[0018] A robot fleet management platform for configuring robot fleet resources 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 job analysis system that applies a set of filters to the job content received in connection with a job request in order to identify the parts suitable for robot automation. A task definition system establishes a set of robot tasks, each defining at least the type of robot and the task objective, and the set of robot tasks is at least partially based on the parts of the job request that are suitable for robot automation and satisfy a first fleet objective from a 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 that defines a set of task associations and a set of robot adaptive instructions. Each task association associates at least one robot operating unit with each robot task in the set of robot tasks, and the set of robot adaptive instructions defines a method by which one or more robot operating units in the robot fleet are adapted to perform the 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 contextual information, facilitating robot selection and task ordering within the robot task workflow. The job workflow system generates a workflow that defines the execution order of 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 a workflow to a job execution simulation environment that includes digital models of robot operating units assigned to a robot fleet and digital models of task definitions to generate simulation results. The simulation results are used to repeatedly redefine one or more of the set of tasks, fleet resource configuration data structures, or workflows until the simulation results satisfy a second fleet objective from a set of fleet objectives corresponding to a job request. The job execution plan generator generates a job execution plan based on the set of tasks, fleet resource configuration data structures, and workflows 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 propose alternative tasks that satisfy a second fleet objective. In other features, the task definition system interacts with the intelligence layer to optimize robot types and at least one of task objectives 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 specific robot types for use when executing robot tasks from the fleet configuration proxy service. In other features, the task definition system configures a set of robot tasks based on the specific robot types provided by the fleet configuration proxy service. In other features, the task definition system generates data structures for each task in the set of tasks, including a digital twin for at least one of the tasks and a reference to at least one robot operating unit for executing the task, for use by the workflow simulation system. In other features, for each task in the set of tasks, the task definition system generates a data structure that identifies at least one robot type and robot operating unit for executing the task, and a configuration data structure for configuring the robot for executing the task. In other features, the task definition system generates a data structure for each task in a set of tasks and stores the data structure in a library of robot tasks, indexed by information indicating the job request and at least one identifier of the robot type and robot operating unit. In other features, the task definition system matches the robot's capabilities with the constraint requirements specified in the job request when identifying the 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 queries a library of robot tasks for candidate robot tasks that satisfy the task objectives and interacts with the fleet configuration proxy service to select robot tasks from the candidate robot tasks based on at least one fleet objective. In other features, at least one fleet objective is compatibility with available robot operating units. In other features, the task definition system queries a library of robot tasks for candidate robot tasks that satisfy the task objectives and interacts with the fleet intelligence layer to select robot tasks from the candidate robot tasks based on their suitability for achieving the task objectives. In other features, when defining a set of tasks, the task definition system refers to information descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks. In other features, when defining a workflow for robot tasks, the job workflow system refers to information descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks. In other features, when defining a workflow for robot tasks, the job workflow system generates a workflow for robot tasks based on the dependency of a second task on a first task to satisfy the objectives of the second task. In other features, the job workflow simulation system operates a digital twin of tasks in a set of tasks to determine the optimized workflow sequence of tasks.

[0021] A robot fleet management platform for configuring robot fleet resources includes a set of one or more processors that execute a set of computer-readable instructions. Collectively, this set of processors forms a job configuration system that receives job requests and determines a set of robot tasks to be executed by the robot fleet based on the 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 robot tasks and job content to generate a fleet resource configuration data structure for job requests. A fleet intelligence layer activates a set of intelligence services to generate at least one recommended robot task and associated contextual information to facilitate robot selection and task ordering in the robot task workflow. A job workflow system generates a workflow that defines the 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 job execution simulation environment, and to generate simulation results that are used to recursively redefine one or more of the set of tasks, fleet resource configuration data structures, or workflows until the simulation results satisfy the second fleet objective from 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, fleet resource configuration data structures, and workflows 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 relation to a job request to identify portions suitable for robot automation therefrom. In other features, the job configuration system includes a task definition system that establishes a set of robot tasks, each defining at least a robot type and a task objective, the set of robot tasks being suitable for robot automation and at least partially based on a portion of the job request that meets a first fleet objective of a 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 robot operating unit with each of the robot tasks in the set of robot tasks, and the set of robot adaptation instructions defines how one or more robot operating units of the robot fleet are adapted to execute each task assigned to the robot. In other features, the workflow simulation system applies a workflow in a job execution simulation environment that includes a digital model of the robot operating units assigned to the robot fleet and a digital model of the task definition to generate simulation results. In other features, the job configuration system interacts with an intelligence layer to propose alternative tasks that meet a second fleet objective. In other features, the job configuration system interacts with an intelligence layer to optimize at least one of the robot type and the task objective based on at least one of the set of fleet objectives. In other features, the first fleet objective includes fleet resource utilization criteria.

[0023] In other features, the job configuration system receives specific robot types from the fleet configuration proxy service for use when executing robot tasks. In other features, the job configuration system configures a set of robot tasks based on the specific robot types provided by the fleet configuration proxy service. In other features, the job configuration system generates data structures for each task in the set of tasks, including references to a digital twin of at least one of the tasks and at least one robot operating unit for executing the task, for use by the workflow simulation system. In other features, for each task in the set of tasks, the job configuration system generates a data structure that identifies at least one of the robot types and robot operating units for executing the task, and a configuration data structure for configuring the robot for executing the task. In other features, the job configuration system generates data structures for each task in the set of tasks and stores the data structures in a library of robot tasks, indexed by information indicating the job request and at least one identifier of the robot type and robot operating unit. In other features, when identifying the type of robot to satisfy the task objective, the job configuration system matches the requirements and robot capabilities to the constraints identified in the job request. In other features, the job configuration system generates multiple robot tasks for multiple different robot types to achieve task objectives. In other features, the job configuration system queries a library of robot tasks for candidate robot tasks that satisfy task objectives and interacts with a fleet configuration proxy service to select robot tasks 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 queries a library of robot tasks for candidate robot tasks that satisfy a task objective and interacts with the fleet intelligence layer to select a robot task from the candidate robot tasks based on the suitability of the candidate robot tasks to achieve the task objective. In other features, the job configuration system refers to descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks when defining a set of tasks. In other features, the job workflow system refers to descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks when defining a workflow of robot tasks. In other features, the job workflow system generates a workflow of robot 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 a set of tasks to determine an optimized workflow order of the tasks.

[0025] The robot fleet management platform includes one or more sets of processors that execute a set of computer-readable instructions. Collectively, the set of one or more processors receives job requests that include descriptive information about deliverables and request-specific constraints for delivering the deliverables. It also performs content and structure filters on the received content related to the job request to identify portions suitable for robot automation. It also establishes a set of robot tasks, each defining at least the type of robot and the task objective, and the set of robot tasks is at least partially based on portions of the job request that are suitable for robot automation and satisfy a first fleet objective. Furthermore, it applies fleet configuration services to the job content and set of robot tasks to generate a fleet resource configuration data structure for the job request, associating at least one robot operating unit with each task in the task set and, based on at least one robot operating unit, with robot adaptive instructions for executing the associated tasks. Furthermore, the system performs the following actions: recommending robot tasks and related contextual information to facilitate robot selection and task ordering in the robot task workflow using a fleet intelligence layer; generating a robot task workflow based on the fleet resource configuration data structure and the set of robot tasks; simulating a digital model of a robot operating unit that executes a digital model of the task definition, thereby verifying the generated workflow and providing the results of a job execution simulation to recursively establish the set of robot tasks; and generating at least a first part of an execution plan for the robot fleet resources configured in the fleet resource configuration data structure.

[0026] In other features, the robot fleet management platform includes using a fleet intelligence layer to suggest alternative tasks that satisfy a second fleet objective. In other features, the robot fleet management platform includes using an intelligence layer to optimize robot types and at least one of task objectives 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 specific robot types for use when executing robot tasks from the fleet configuration proxy service. In other features, establishing a set of robot tasks is based on specific robot types provided by the fleet configuration proxy service. In other features, establishing a set of robot tasks includes generating a data structure for each task in the set of tasks, which includes a digital twin for at least one of the tasks and a reference to at least one robot operating unit for executing the task, for use by a workflow simulation system. In other features, establishing a set of robot tasks includes, for each task in the set of tasks, generating a data structure that identifies the robot type and at least one robot operating unit for executing the task, and a configuration data structure for configuring the robot for executing the task. Other features include establishing a set of robot tasks, which involves generating a data structure for each task in the set of tasks, and storing the data structure in a library of robot tasks, indexed by information indicating the job request and at least one identifier of the robot type and robot operating unit.

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

[0028] The robot fleet platform is designed to prepare job requests to facilitate the configuration of a robot fleet operated by the platform. This system 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 a job request ingestion system configured to receive job content related to at least one of the following: picking, packing, moving, storing, warehousing, transporting, or delivering a set of items in the supply chain, where the job content includes electronic job requests and associated data. A job content parsing system is configured to apply filters to the received job content to identify candidate portions for robot automation. The fleet intelligence layer processes the terms of the candidate portions of the job content and activates a set of intelligence services to receive from it at least one recommended robot task and associated contextual information to facilitate robot selection and task ordering in the robot task workflow. The demand intelligence layer provides real-time information related to demand parameters for a set of items in the supply chain. The job requirements system generates a job requirement set specific to each job request instance based on portions of the job content indicating automation by robots, real-time information from the demand intelligence layer, and at least one recommended robot task and associated contextual information. This set of job requirements is stored in non-transient, computer-readable memory accessible by at least one processor from a set of processors.

[0029] In other features, the job content analysis system retrieves a set of content and structure filters from a job configuration library that facilitates mapping job content metrics to target terms indicating robotic automation. In other features, the job content analysis system augments the default set of content and structure filters with filter criteria from the job configuration library that facilitates mapping job content metrics to target terms indicating robotic automation. In other features, content filters indicate terms within the job content that distinguish robotic automation content from other content within the job content. In other features, terms are obtained from a job configuration library that facilitates mapping job content metrics to terms indicating robotic automation. In other features, the fleet intelligence layer facilitates sending portions of job content identified as suitable for robotic automation to machine learning services of a set of intelligence services to improve job content analysis. In other features, the machine learning service is trained on a training dataset that includes human-generated feedback on job content analysis results for multiple job requests, a knowledge base for robotic automation, 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 within the job content, facilitating the automatic determination of at least one of the following: transport options, operational constraints, permit requirements, transport restrictions, local fleet assets, and logistics constraints.

[0030] In other features, physical location information includes one or more of the following: address, region, GPS data, aerial photographs, marked locations on map images, map coordinates, latitude, longitude, altitude, route, and 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 multiple voltages, frequencies, currents, available schedules, grid-provided electricity cost schedules, cost per kWh, power demand profile, maximum thermal density, and proximity to at least one location. In other features, the job analysis system is configured to detect digital data representing the layout of a portion of the job site present or referenced within the job content, in order to facilitate the generation of at least one job request instance-specific requirement related to the layout of the job site. In other features, the job analysis system is configured to detect at least one of the following: information describing the operating environment, artifacts, interfaces through which information about the job request is communicated with the job requester, wireless communication network accessibility, budget constraints for performing the task, and resource scheduling 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 relevant data. In other features, the job request ingestion system is configured to retrieve relevant data for use by the robot fleet platform based on external links. In other features, the job request ingestion system is configured to determine portions of the received job content, including references to activities suitable for execution by the robot fleet resources, and forward them to the job content analysis system.

[0031] In other features, the job request ingestion system is configured to process received content using a job configuration metric filter that automatically routes job configuration metrics within the job content to a job configuration library search service for classifying these job configuration metrics as either the current job configuration, a previous job configuration, or an unknown job configuration. In other features, the job content analysis system is configured to identify structure and content elements within the received content to facilitate the identification of candidate robot tasks. In other features, the job content analysis system is configured to identify structural elements within the received content that indicate at least one of tasks, subtasks, task sequences, task dependencies, and task requirements to facilitate the selection of fleet robot operating units. In other features, the job content analysis system is configured to identify content terms that indicate at least one minimum robot capability. In other features, the job content analysis system is configured to include a robot type filter that, when applied to job request content, identifies terms that indicate the type of robot to perform the task. In other features, the job request ingestion system includes a job request ingestion interface for receiving electronic job requests. In other features, applying content and structure filters includes scanning the received content for data indicating 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 identifies terms indicating the type of robot to perform the task when applied to job request content.

[0032] In other features, the job analysis system utilizes content filters to detect qualified job data. In other features, the robot fleet platform includes a qualified data query generation system configured to generate queries for at least one element of qualified data within the job content for clarification. In other features, the queries for at least one element of qualified data are presented to the user interface. In other features, the queries for at least one element of qualified data are provided to the fleet intelligence layer for processing with at least one intelligence service from a set of intelligence services to provide at least one clarification item of data for at least one element of qualified data through the fleet intelligence layer. In other features, the robot fleet platform includes a qualified data resolution system configured to evaluate at least one qualified data element within the job content for similarity with clarified data from multiple job requests, and, based on the results of this evaluation, adjust at least one qualified data element based on similar clarified data elements. In other features, adjusting at least one qualified data element includes replacing a qualified data value within the qualified data element with a corresponding data value from an clarified data element. In other features, the content filter is configured to identify qualified data that includes at least one of missing data, ambiguous data, and qualitative references. In other features, the fleet intelligence layer facilitates the processing of qualified data by machine learning services of a set of intelligence services to improve the analysis of qualified data. In other features, the content filter is configured to identify qualified data and relevant context to facilitate the resolution of at least one of missing data, ambiguous data, and qualitative references in this qualified data.

[0033] The value chain network automation system includes a supply chain robot fleet dataset containing a set of state and capability attributes for a set of robotic systems in the supply chain for a set of goods. The system also includes a demand intelligence robotic process automation dataset containing a set of state attributes for a set of robotic process automation systems that undertake the automation of a set of demand forecasting tasks for the set of goods. The system includes 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 the supply and demand of the set of goods.

[0034] (Summary of further inventions) According to some embodiments of the present disclosure, methods and systems relating to an information technology system may include a cloud-based management platform with a microservices architecture, a set of interfaces, network connectivity equipment, adaptive intelligent equipment, data storage equipment, and monitoring equipment, and a set of applications for managing a set of value chain network entities from the origin of the enterprise to the point of use of the customer.

[0035] In particular, provided herein are methods, systems, components, processes, modules, blocks, circuits, subsystems, goods, services, software, hardware, and other elements (collectively referred to as "platforms" or "systems" in some cases, and these terms should be understood to encompass either of the above unless the context requires otherwise) (such terms include numerous 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, thereby optimizing 3D printing parameters, achieving higher fidelity and accuracy in printing, and enhancing efficiency and traceability in the design process, manufacturing, supply chain demand management systems, products, and product use cases.

[0037] Embodiments 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 a value chain network, in order to optimize digital production processes and workflows.

[0038] The embodiments provided herein include an information technology system for a distributed manufacturing network, which includes: an additive manufacturing management platform configured to manage processes and production workflows for a set of distributed manufacturing network entities throughout the design, modeling, printing, supply chain, distribution, point of sale, and point of use stages; an artificial intelligence system configured to learn from a training set of results, parameters, and data collected from distributed manufacturing network entities of the distributed manufacturing network to optimize 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 within the distributed manufacturing network.

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

[0040] In an embodiment, the information technology system includes a digital twin system configured to construct a digital twin of one or more distributed manufacturing network entities, the digital twin providing 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 an embodiment, the digital twin may represent various parameters and attributes of the manufacturing entity (whether additive, subtractive, biological, chemical, or other entities), such as the types of materials it can handle, the current level of available source materials, processing / output speed, operational capability, biological manufacturing capability, vacuum processing capability, energy production and consumption information (e.g., for heating, laser processing, etc.), pricing parameters, etc. In an embodiment, a platform, such as using an artificial intelligence system, may perform simulations on the digital twin or its predicted outputs to predict possible future states of the distributed manufacturing network entities and / or one or more of its outputs.

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

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

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

[0044] Embodiments provided herein include an autonomous additive manufacturing platform comprising: a plurality of sensors positioned 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 sensor data from the plurality of sensors; a machine learning system configured to input the sensor data into 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 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 enables 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 one or more models are used by the artificial intelligence system to perform classification, prediction, recommendation, and / or generate or facilitate decisions or instructions regarding the product and part (such as decisions or instructions that manage design, configuration, material selection, shape selection, manufacturing type, job scheduling, etc.).

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

[0046] In one embodiment, a model trained by a machine learning system is used by an artificial intelligence system to perform simulations on a part twin to predict part warping.

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

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

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

[0050] In the embodiment, a model trained by a machine learning system is used by an artificial intelligence system to run simulations on a part twin to predict deformation or defects in 3D printed parts. In the embodiment, the model can also determine a set or sequence of process control parameter adjustments to implement corrective actions, such as adjusting layer dimensions or thickness, to correct defects. In the embodiment, the system can send a warning or error signal to the 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 length and variable lighting settings, etc., to facilitate AI-based object recognition, boundary detection, item classification, material recognition, or other elements related to the design, manufacture, or use of parts or other components. In embodiments, the output from the integrated AI and the variable-focus lens system is integrated with or incorporated into a digital twin representing a set of items, such as parts, captured by a system using a variable-focus lens.

[0052] In this embodiment, the model trained by the machine learning system is used by the artificial intelligence system to run simulations on the part twin to optimize the construction process to minimize the occurrence of deformation.

[0053] In embodiments, a model trained by a machine learning system is used by an artificial intelligence system to run simulations on a product twin to predict the cost and / or price of a product or its components. Cost predictions 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 the cost of additive manufacturing input materials and the cost of additive manufacturing processing time. Cost predictions may also use inputs related to process costs, including energy costs and labor costs. Price predictions may be based on similar inputs, such as publicly available information from various sources indicating the current or historical market price of a product. Cost or price predictions may also use inputs from smart contracts, such as smart contract parameters indicating current cost and price information provided in third-party contracts for materials, parts, etc.

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

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

[0056] In one embodiment, the artificial intelligence system performs simulations on one or more of the part twin, product twin, and printer twin to generate 3D printing estimates. In another embodiment, the set of additive manufacturing estimates may optionally be embodied in a blockchain-linked smart contract so that the additive manufacturing operation can be contracted via the smart contract.

[0057] In embodiments, the artificial intelligence system performs simulations on one or more of a part twin, product twin, printer twin, or other twins to generate a set of recommendations for the platform user related to printing or other additive manufacturing. 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 or steps for scheduling an additive manufacturing job, and design parameters (e.g., from a set of possible designs). In embodiments, the recommendations relate to the selection of materials for printing. In embodiments, the recommendations relate to the selection of 3D printing technology.

[0058] In the embodiment, the artificial intelligence system performs simulations on one or more of the part twin, product twin, and printer twin in order to generate print-related recommendations for the platform user.

[0059] In this embodiment, the artificial intelligence system performs simulations on one or more of the part twin, product twin, and printer twin to predict the delivery date of a 3D printing job.

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

[0061] In one embodiment, the artificial intelligence system performs simulations on one or more of the part twins, product twins, printer twins, and manufacturing node twins to optimize the production sequence of parts and products based on estimated price, delivery date, sales margin, order size, or similar characteristics.

[0062] In one embodiment, the artificial intelligence system performs simulations on one or more of the part twins, product twins, printer twins, and manufacturing node twins in order to optimize the cycle time for manufacturing.

[0063] In this embodiment, the artificial intelligence system performs simulations on one or more of the component twins, product twins, printer twins, customer twins, and manufacturing node twins to predict and manage product demand from one or more customers.

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

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

[0066] In embodiments, a distributed manufacturing entity includes: linking to, using, obtaining input from, or integrating with a set of other systems, such as enterprise resource planning (ERP) systems, manufacturing execution systems (MES), product lifecycle management (PLM) systems, maintenance management systems (MMS), quality management systems (QMS), certification systems, compliance systems, robot / cobot (Cobot) systems, and SCCG systems.

[0067] Embodiments provided herein include a computer-implemented method for facilitating the manufacturing and delivery of 3D printed products to a customer using one or more manufacturing nodes of a distributed manufacturing network, the method including: receiving one or more product requirements from a customer; tokenizing the product requirements and storing them in a distributed ledger system; determining one or more manufacturing nodes, printers, processes, and materials based on the product requirements; generating a quote including price and delivery timeline; and manufacturing and delivering the 3D printed product to the customer once the quote is accepted by the customer. In embodiments, the quote is automatically generated and set up in a smart contract for additive manufacturing.

[0068] In the embodiment, the determination includes matching a customer's order with a manufacturing node or 3D printer based on factors such as printer capabilities, customer and manufacturing node locations, available capabilities at each node, price and timeline requirements, and customer satisfaction scores.

[0069] In various embodiments, including entity matching, design selection, manufacturing type selection, material selection, recommendations, and scheduling, location-based decisions may include geofencing and other distance-based information, route-based information (such as considering traffic congestion and other factors that may affect delivery times), and other location-related information regarding distribution points, transport facilities, sales points, and / or usage points, such as infrastructure information, resource availability information, weather information, climate information, and many others. Location-based decisions can, for example, reflect ambient temperature and other conditions of a location (or a combination of location and intended use) in the selection of materials and structures for manufacturing (such as considering the possibility of expansion or contraction under extreme high or low temperature conditions).

[0070] In an embodiment, the method further includes rating one or more manufacturing nodes based on customer satisfaction scores to meet customer requirements.

[0071] In embodiments, the method can help manage production workflows within and between one or more manufacturing nodes, thereby facilitating collaboration between manufacturing nodes through the sharing of resources, capabilities, and intelligence. In embodiments, manufacturing nodes can collaborate for material supply and forecasting and predicting product demand. In embodiments, manufacturing nodes can collaborate for design and product development. In embodiments, manufacturing nodes can collaborate for the manufacture and assembly of one or more components 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 utilizing the unused capacity of one or more manufacturing nodes or 3D printers by making capacity available to one or more users seeking the manufacture of 3D printed parts. In embodiments, manufacturing as a service may optionally be provided via smart contracts using blockchain and / or distributed ledgers. In embodiments, manufacturing as a service may be governed and managed by an artificial intelligence system for a set of additive manufacturing entities, for purposes such as structuring the offerings, scheduling jobs, setting prices, and setting other contract terms.

[0073] The embodiments provided herein include a distributed manufacturing network comprising: a distributed ledger system integrated with a digital thread of a set of distributed manufacturing network entities for storing information relating to events, activities, and transactions associated with the distributed manufacturing network entities; and an artificial intelligence system configured to learn from a training set of results, parameters, and data collected from the distributed manufacturing network entities to optimize the manufacturing and value chain workflows.

[0074] In one embodiment, the distributed ledger system includes a distributed application that can be downloaded by entities in a distributed manufacturing network.

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

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

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

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

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

[0080] In one embodiment, the distributed ledger system includes a system for certifying the experience of a manufacturing node.

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

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

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

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

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

[0086] In one embodiment, the distributed ledger system includes or supports smart contracts for processing customer payments at the time of product delivery.

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

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

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

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

[0091] In embodiments, the connectivity equipment includes network connectivity, interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, microservices, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud functionality, or similar.

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

[0093] In one embodiment, the artificial intelligence system provides generative design and topology optimization for determining at least one product design that is suitable for manufacturing, suitable for meeting customer needs, suitable for meeting manufacturer specifications, or similar.

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

[0095] In one embodiment, the artificial intelligence system optimizes the parts orientation process to obtain superior production results.

[0096] In one embodiment, the artificial intelligence system provides the ability to optimize toolpath generation.

[0097] In the embodiment, the artificial intelligence system provides optimized dynamic 2D, 2.5D, and 3D nesting, maximizing the number of printed parts while minimizing waste of raw materials.

[0098] In the 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 one embodiment, the user interface includes a dashboard that provides batch traceability for identifying parts from the same batch.

[0100] In the embodiment, the user interface includes a digital twin interface for resolving inquiries from users of a network related to a component or product.

[0101] In one embodiment, the user interface includes a virtual reality (VR) interface configured to allow the user to build 3D models in VR.

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

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

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

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

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

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

[0108] In one embodiment, the artificial intelligence system is configured to analyze usage patterns related to one or more users and to learn the users' preferences regarding materials, orientation, and / or printing strategies.

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

[0110] In one embodiment, the artificial intelligence system is configured to optimize material utilization during an additive manufacturing process by providing a set of instructions that take into account waste generation and material recapture or recycling.

[0111] In one embodiment, the artificial intelligence system is configured to optimize the combination of material use, energy use, and other resource use during the additive manufacturing process by taking energy and labor costs into account when optimizing the instruction set.

[0112] In one embodiment, the 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 one embodiment, the artificial intelligence system is configured to build, maintain, and provide a library of parts with pre-set parameters, which can be searched by material, properties, function, equipment compatibility, shape compatibility, 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 embodiment described herein) may use any of the artificial intelligence types described herein or in documents incorporated herein by reference. In embodiments, an artificial intelligence system (in any embodiment described herein) may utilize training datasets that include, in particular, one or more of the following: a set of expert actions or operations on information; process and / or workflow data; a set of various types of models; a set of results (such as from additive manufacturing processes, from the 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 databases and information technology systems such as those of companies; information from crowdsourcing; Internet of Things information; and / or other data sources and inputs, etc.

[0115] In one embodiment, the distributed manufacturing network information technology system is configured to provide 3D printed products that conform to a part of the user's body or anatomical structure, and the 3D printed products are wearables selected from a group consisting of eyewear, footwear, earwear, and headgear.

[0116] Embodiments provided herein are information technology systems 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 from a training set of results, parameters, and data collected from one or more additive manufacturing nodes in order to optimize additive manufacturing and value chain processes and workflows; and a distributed ledger system configured to store data relating to manufacturing nodes.

[0117] In this 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 this 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 feedstock for additive manufacturing.

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

[0120] In this 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 manufacturing or delivery of parts or products to customers by one or more manufacturing nodes.

[0121] In this 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 with respect to parts or products manufactured by one or more manufacturing nodes and delivered to customers.

[0122] Summary of liquid lenses

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

[0124] Embodiments 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 in order to recognize objects.

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

[0126] In an embodiment, a 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. In an embodiment, the object concept includes contextual intelligence about the object and its environment, providing excellent object recognition by the dynamic vision system.

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

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

[0129] In embodiments, 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 several other properties.

[0130] In an embodiment, the processing system can be trained with a set of results, parameters, and data from a 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] Embodiments provided herein include a robot vision system comprising: an optical assembly comprising one or more sensors, a variable focus liquid lens, and a photon capture board; a processing system configured to dynamically learn with a training set of results, parameters, and data collected from the optical assembly in order to train an artificial intelligence model to recognize objects; and in embodiments, the robot vision system further comprises a control system configured to adjust one or more optical parameters and data collected from the optical assembly in real time.

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

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

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

[0135] In one embodiment, the artificial intelligence model can construct a three-dimensional representation of an object in one or more steps, without an intermediate step of processing it into a two-dimensional image.

[0136] In the embodiments, one or more sensors may include a camera, LIDAR, RADAR, SONAR, thermal imaging sensor, hyperspectral imaging sensor, illuminance sensor, force sensor, torque sensor, velocity 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 types of sensors.

[0137] In one 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 movement.

[0138] Embodiments provided herein include a vision system for dynamically learning an object concept about 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, wherein the pixel array represents an object concept; and an adaptive intelligence system configured to process the object concept and 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, wherein the object concept is used as training data for the machine learning models; and an artificial intelligence system configured to make classification, prediction, and other decisions about the object, including determining the position, orientation, and motion of the object.

[0139] Embodiments provided herein include a method for recognizing an object, the method comprising: receiving a real-time adjustable data stream in a sensor representing visual and contextual information about an object of interest; generating an object concept, including contextual intelligence about the object and its environment, by an image processing system; adjusting the optical parameters of a conformable liquid lens by a control system; modifying the object concept in accordance with the adjustment of the optical parameters of the conformable liquid lens by a machine learning system; and determining object attributes, including classification, depth, position, orientation, and motion, by an artificial intelligence system; wherein the object concept is constantly modified in accordance with the adjustment of the optical parameters of the conformable liquid lens and 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] You can find a summary of the robots here.

[0141] This disclosure relates to a fleet management platform capable of organizing, deploying, and controlling special-purpose, multi-purpose, and other classes of robots. Such a platform, capable of securely providing reliable contractual services, is a key to unlocking the value-creating potential of autonomous robots. This value proposition can be further amplified if highly configurable robots are designed with state-of-the-art capabilities and equipped with advanced artificial intelligence; if the platform has the intelligence and computing power to integrate data from various sources, including deployed robots, value chain network (VCN) entities encompassing a wide range of supply chain activities (such as picking, packing, moving, storage, warehousing, transportation, and delivery) and demand-related activities (such as marketing, sales, advertising, forecasting, pricing, location, deployment, and design); and if the platform learns from and manages performance based on operational results.

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

[0143] The accompanying drawings included to provide a better understanding of this disclosure illustrate embodiments of this disclosure and, together with the description, help to illustrate many aspects of this disclosure. In the drawings:

[0144] [Figure 1] Figure 1 is a block diagram showing the relationship between prior art of various entities and facilities in a supply chain.

[0145] [Figure 2] Figure 2 is a block diagram showing the system and process components and interrelationships of the value chain network as described in this disclosure.

[0146] [Figure 3] Figure 3 is another block diagram showing the system and process components and interrelationships of the value chain network as disclosed herein.

[0147] [Figure 4] Figure 4 is a block diagram showing the system and process components and interrelationships of the digital products network shown in Figures 2 and 3 of this disclosure.

[0148] [Figure 5] Figure 5 is a block diagram showing the system and process components and interrelationships of the value chain network technology stack according to this disclosure.

[0149] [Figure 6]Figure 6 is a block diagram showing a platform and relationships for orchestrating the control of various entities in a value chain network in accordance with this disclosure.

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

[0151] [Figure 8] Figure 8 is a block diagram showing the components and relationships of a value chain entity managed by an embodiment of the value chain network management platform according to this disclosure.

[0152] [Figure 9] Figure 9 is a block diagram showing the network relationships of entities in the value chain network related to this disclosure.

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

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

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

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

[0157] [Figure 14] Figure 14 is a block diagram illustrating the provision of an adaptive intelligence system for collaborative intelligence for a set of supply and demand applications for a category of goods, in accordance with this disclosure.

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

[0159] [Figure 16] Figure 16 is a block diagram illustrating the provision of an adaptive intelligence system for predictive intelligence for a set of supply and demand applications for a category of goods, in accordance with this disclosure.

[0160] [Figure 17] Figure 17 is a block diagram illustrating an adaptive intelligence system for classification intelligence for a set of supply and demand applications for a category of goods, in accordance with this disclosure.

[0161] [Figure 18] Figure 18 is a block diagram illustrating an adaptive intelligent system provided in accordance with this disclosure to generate automatic control signals for a set of supply and demand applications for a category of goods.

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

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

[0164] [Figure 21] Figure 21 is a block diagram showing a collection of artificial intelligence systems that operate on value chain information to enable the automated coordination of a company's value chain activities in accordance with this disclosure.

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

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

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

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

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

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

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

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

[0173] [Figure 30] Figure 30 is a block diagram showing 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 this disclosure.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0190] [Figure 47] Figure 47 is a block diagram showing 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 showing the components and relationships in an embodiment of a value chain network management platform using a microservices architecture.

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

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

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

[0195] [Figure 52] Figure 52 is a perspective view depicting an embodiment of a set of value chain network digital twins representing a virtual model of a set of value chain network entities in accordance with this disclosure.

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

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

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

[0199] [Figure 56] Figure 56 is a perspective view showing an embodiment of a machine twin deployment for predictive maintenance of a set of machines in accordance with this disclosure.

[0200] [Figure 57] Figure 57 is a schematic diagram illustrating some examples of systems for value chain customer digital twins and customer profile digital twins according to several embodiments of the present disclosure.

[0201] [Figure 58] Figure 58 is a schematic diagram showing an example of an advertising application that interfaces with the adaptive intelligent system layer according to this disclosure.

[0202] [Figure 59] Figure 59 is a schematic diagram illustrating an example of an e-commerce application integrated with the adaptive intelligent system layer described herein.

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

[0204] [Figure 61] Figure 61 is a schematic diagram illustrating some examples of a system for a value chain smart supply component digital twin according to several embodiments of the present disclosure.

[0205] [Figure 62] Figure 62 is a schematic diagram illustrating an example of a risk management application that interfaces with the adaptive intelligent system layer according to this disclosure.

[0206] [Figure 63] Figure 63 is a perspective view of the maritime assets related to the value chain network management platform, including port infrastructure components, as disclosed in this disclosure.

[0207] [Figure 64] Figures 64 and 65 are perspective views of maritime assets related to a value chain network management platform, including the components of the vessel relating to this disclosure. [Figure 65] Figures 64 and 65 are perspective views of maritime assets related to a value chain network management platform, including the components of the vessel relating to this disclosure.

[0208] [Figure 66] Figure 66 is a perspective view of the maritime assets related to the value chain network management platform, including the components of the barge as disclosed in this disclosure.

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

[0210] [Figure 68] Figure 68 is a schematic diagram illustrating an example of an enterprise and executive control tower and management platform environment, including data sources that communicate with it, according to several embodiments of the present disclosure.

[0211] [Figure 69] Figure 69 is a schematic diagram showing examples of sets of components for an enterprise control tower and management platform according to some embodiments of the present disclosure.

[0212] [Figure 70] Figure 70 is a schematic diagram illustrating examples of enterprise data models according to several embodiments of the present disclosure.

[0213] [Figure 71] Figure 71 is a schematic diagram illustrating different types of enterprise digital twins, including executive digital twins, related to the data layer, processing layer, and application layer of an enterprise digital twin framework according to several embodiments of the present disclosure.

[0214] [Figure 72] Figure 72 is a schematic diagram illustrating exemplary embodiments of corporate executive control towers and management platforms according to several embodiments of the present disclosure.

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

[0216] [Figure 74]Figure 74 shows an example of a set of operations for configuring a digital twin of an organization.

[0217] [Figure 75] Figure 75 shows an example of an operational set for generating an executive digital twin.

[0218] [Figure 76] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 77] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 78] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 79]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 80] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 81] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 82] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 83]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 84] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 85] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 86] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 87]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 88] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 89] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 90] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 91]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 92] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 93] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 94] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 95]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 96] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 97] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 98] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 99]Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 100] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 101] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 102] Figures 76 to 103 are schematic diagrams of embodiments of neural network systems that enable intelligent trading, including expert systems, self-organizing systems, machine learning systems, and artificial intelligence systems, and that are connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 103]Figures 76 to 103 are schematic diagrams of embodiments of a neural network system that enables intelligent transactions including expert systems, self-organization, machine learning, artificial intelligence, etc., and is connected, integrated, and accessible by a platform including neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes.

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

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

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

[0222] [Figure 107] Figure 107 is a schematic diagram showing an exemplary neural network for performing natural language processing according to some embodiments of the present disclosure.

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

[0224] [Figure 109] Figure 109 is a schematic diagram showing an exemplary physical orientation chip according to some embodiments of the present disclosure.

[0225] [Figure 110] Figure 110 is a schematic diagram illustrating exemplary network enhancement chips according to several embodiments of the present disclosure.

[0226] [Figure 111] Figure 111 is a schematic diagram illustrating exemplary diagnostic chips according to several embodiments of the present disclosure.

[0227] [Figure 112] Figure 112 is a schematic diagram illustrating exemplary governance chips according to several embodiments of the present disclosure.

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

[0229] [Figure 114] Figure 114 is a perspective view showing example environments for autonomous additive manufacturing platforms according to several embodiments of the present disclosure.

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

[0231] [Figure 116] Figure 116 is a flowchart illustrating the optimization of different parameters in an additive manufacturing process according to several embodiments of the present disclosure.

[0232] [Figure 117] Figure 117 is a schematic diagram illustrating a system that learns 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] Figure 118 is a schematic diagram showing an exemplary implementation of an autonomous additive manufacturing platform including various components together with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.

[0234] [Figure 119] Figure 119 is a schematic diagram showing 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] Figure 120 is a perspective view of a distributed manufacturing network realized by an autonomous additive manufacturing platform and constructed on a distributed ledger system according to some embodiments of the present disclosure.

[0236] [Figure 121] Figure 121 is a schematic diagram showing 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 within the distributed manufacturing network according to some embodiments of the present disclosure.

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

[0238] [Figure 123] Figure 123 is a schematic diagram showing an exemplary implementation of a dynamic vision system for dynamically learning an object concept regarding an object of interest according to some embodiments of the present disclosure.

[0239] [Figure 124]Figure 124 is a schematic diagram showing exemplary architectures of dynamic vision systems according to several embodiments of the present disclosure.

[0240] [Figure 125] Figure 125 is a flowchart illustrating a method for object recognition using a dynamic visual acuity system according to several embodiments of the present disclosure.

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

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

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

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

[0245] [Figure 130] Figure 130 is a schematic diagram illustrating examples of platform-level intelligence layers in fleet management platforms according to several embodiments of the present disclosure.

[0246] [Figure 131] Figure 131 is a schematic diagram showing examples of intelligence layer configurations according to several embodiments of the present disclosure.

[0247] [Figure 132] Figure 132 is a schematic diagram illustrating exemplary security frameworks according to several embodiments of the present disclosure.

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

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

[0250] [Figure 135] Figure 135 is a schematic diagram illustrating exemplary data flows of fleet operation systems according to several embodiments of the present disclosure.

[0251] [Figure 136] Figure 136 is a schematic diagram illustrating exemplary job analysis systems and task definition systems, as well as exemplary data flows, according to some embodiments of the present disclosure.

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

[0253] [Figure 138] Figure 138 is a schematic diagram illustrating exemplary workflow definition systems and exemplary data flows according to several embodiments of the present disclosure.

[0254] [Figure 139] Figure 139 is a schematic diagram showing examples of the configuration of a multi-purpose robot and its components according to some embodiments of the present disclosure.

[0255] [Figure 140] Figure 140 is a schematic diagram showing an exemplary architecture of a robot control system according to several embodiments of the present disclosure.

[0256] [Figure 141] Figure 141 is a schematic diagram illustrating exemplary architectures of a robot control system 12150 that utilizes data from multiple sensors in a vision and sensing system, according to several embodiments of the present disclosure.

[0257] [Figure 142] Figure 142 is a schematic diagram illustrating exemplary vision and sensing systems for robots according to several embodiments of the present disclosure. [Modes for carrying out the invention]

[0258] Over time, companies have increasingly utilized technological solutions to improve traditional supply chain-related outcomes, as depicted in Figure 1, such as software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods moving through the supply chain, and navigation and routing systems for improving the efficiency of route selection. However, several major trends are pushing companies, such as manufacturers and retailers, to improve supply chain performance. First, online and e-commerce operators, particularly Amazon®, have become the largest retail channels for many product categories, deploying distribution and fulfillment centers¹¹² accommodating hundreds of thousands, and sometimes more, of product categories (SKUs) across some regions, such as the United States, enabling customers to receive their orders the day after they place them, and sometimes even the same day (sometimes delivered to their doorstep by drones, robots, and / or autonomous vehicles). For retailers whose fulfillment centers and warehouses are not geographically distributed over a wide area, customer expectations for delivery speed increase the pressure on supply chain efficiency and optimization. Thus, the need for improved supply chain methods and systems remains.

[0259] Secondly, agile manufacturing capabilities such as 3D printing and robotic assembly technologies, customer profiling techniques, and online ratings and reviews are driving increased customer expectations for product customization and personalization. Therefore, manufacturers and retailers need to improve their methods and systems for understanding, predicting, and satisfying customer demand in order to stay competitive.

[0260] Historically, supply chain management and demand planning and management were largely separate activities, primarily integrated when demand was converted into orders and passed to the supply side for fulfillment within the supply chain. With increasing expectations for speed and personalization, there is a need for methods and systems to achieve integrated orchestration of demand and supply.

[0261] Alongside this larger trend, the emergence of the Internet of Things has led to increasingly enhanced onboard network connectivity and processing power in some products, particularly smart home products such as thermostats, lighting systems, and speakers. Voice-controlled intelligent agents like Alexa® and Siri® often enable device control, activation of specific application functions such as music playback, or product ordering. In some cases, smart products 650 can even initiate orders, such as ordering refill cartridges for a printer. While intelligent products 650 may also be involved in collaborative systems, such as Amazon® Echo® products controlling a TV or sensor-enabled thermostats and security cameras connecting to mobile devices, most intelligent products still engage in a largely isolated set of application-specific interactions. As artificial intelligence capabilities improve and more computing and networking capabilities move to network-enabled edge devices and systems present in every location, system, and facility in the supply environment 670, the demand environment 672, and the product 650 from the manufacturer's entry point 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 Figure 2, a block diagram 200 is shown illustrating the system and process components and interrelationships of a value chain network. In exemplary embodiments, as used herein, “value chain network” 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 numerous diverse technologies. In exemplary embodiments, the value chain control tower 260 (for example, referred herein as, as may be, “Value Chain Network Management Platform,” “VCNP,” or simply “System,” or “Platform”) may be connected to, communicate with, or otherwise operationally 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 functions that receive data flows, data pools, data streams, and / or other data configurations and transmission modes received from, for example, a digital product network 252, directly from customers (e.g., directly connected customers 250), or any other third party 220. Market orchestration activities and communications related to communications 210, analyses 232, or any other type of input may also be utilized by the Value Chain Control Tower for demand enhancement 262, synchronized planning 234, intelligent procurement 238, dynamic execution 240, or any other smart operations informed by coordinated and adaptive intelligence, as described herein.

[0263] Referring to Figure 3, another block diagram is shown illustrating the system and process components and interrelationships of the value chain network, as well as related use cases, data processing, and related entities. In an exemplary embodiment, the value chain control tower 360 may coordinate market orchestration activities 310, including but not limited to demand curve management 352, ecosystem synchronization 348, intelligent procurement 344, dynamic fulfillment 350, value chain analysis 340, and / or smart supply chain operations 342. In an exemplary embodiment, the value chain control tower 360 may be connected, otherwise connected, so connected, so communicated, or otherwise operationally connected to processing equipment, which may include an intelligent user-adaptive interface, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein, and an adaptive data pipeline 302 and an external data source 320 and a data handling stack 330 (e.g., value chain network technology), and may be further connected, otherwise connected, so connected, so communicated, or otherwise operationally connected. The value chain control tower 302 may also be further connected, communicated with, or otherwise operationally coupled to additional value chain entities, including but not limited to, the 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 Figure 4, a block diagram is shown illustrating the system and process components and interrelationships of the digital product network in 400. In exemplary embodiments, products (including goods and services) can generate data, such as product-level data, and transmit it to the communication layer and / or edge data processing facilities within the value chain network technology stack. This data can generate enhanced product-level data and be combined with third-party data for further processing, modeling, or other adaptive or collaborative information activities, as described herein. This includes, but is not limited to, generating and / or simulating product and value chain use cases, for which the data may be utilized by products, product development processes, product designs, etc.

[0265] [Example of a stack view] Referring to Figure 5, a block diagram is shown at 500 illustrating the system and process components and interrelationships of the value chain network technology stack, which may include, but is not limited to, a presentation layer, an intelligence layer, and serverless functionalities such as platforms (e.g., development platform and host platform), data facilities (e.g., data-related for IoT and big data), and data aggregation facilities. In exemplary embodiments, the presentation layer may include, but is not limited to, a user interface, and modules for investigation and discovery, as well as tracking of user experience and engagement. In exemplary embodiments, the intelligence layer may include, but is not limited to, statistics and computation methods, semantic models, analytical libraries, development environments for analysis, algorithms, logic and rules, and machine learning. In exemplary embodiments, 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 exemplary embodiments, 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 exemplary embodiments, the data infrastructure or layer may include, but is not limited to, access, control, and collection of IoT and big data. In exemplary embodiments, 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] Figure 6 shows a linked value chain 668 in which the Value Chain Network Management Platform 604 (hereinafter referred to as the “Value Chain Control Tower,” “VCNP,” or simply “System,” or “Platform” as applicable) organizes various factors involved in the planning, monitoring, control, and optimization of various entities and activities related to the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors. The unified platform 604 for monitoring and managing supply and demand factors as well as status information (e.g., quality and status, planning, ordering and confirmation, and / or tracking and tracking) can be shared with and among various entities (e.g., distribution such as customers / consumers, suppliers, and distributors, and production such as producers and production equipment) so that demand factors are understood and explained, orders are generated and fulfilled, and products are created and moved through the supply chain. The value chain network 668 may include not only the intelligent product 650 but also all the equipment, infrastructure, personnel, and other entities involved in planning and fulfilling the demand for it.

[0267] [Value chain network and value chain network management platform] Referring to Figure 7, the value chain network 668 managed by the value chain management platform 604 includes, but is not limited to, a set of value chain network entities 652, a series of production facilities 674 involved in the production of products 650, which may be intelligent products 650, finished goods, components, systems, subsystems, materials used in articles, etc., various entities, activities and other supply elements 648 involved in the supply environment 670, such as suppliers 642, places of origin 610, etc., and various entities involved in the demand environment 672. Activities and other demand factors 644 include, for example, customers 662 located and / or operating in various destinations 612 (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.), various distribution environments 678 and distribution facilities 658 such as warehouse facilities 654, fulfillment facilities 628, and distribution systems 632, as well as maritime facilities 622 such as port infrastructure facilities 660, floating assets 620, and shipyards 638. In embodiments, the value chain network management platform 604 enables monitoring, control and other management (and in some cases, autonomous or semi-autonomous operation) of a wide range of processes, workflows, activities, events, and applications 630 (in some cases collectively referred to simply as “Applications 630”) of the value chain network 668.

[0268] Referring further to Figure 7, a high-level schematic diagram of the Value Chain Network Management Platform 604 is shown. The Value Chain Network Management Platform 604 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, trade, own, operate, support, or enable therein, relating to one or more value chain network processes, workflows, activities, events and / or applications 630 or products 650 (which can be products of any category such as finished goods, software products, hardware products, component products, materials, equipment items, consumer packaged goods items) A set of consumer products, food products, beverage products, household goods, business supplies, consumables, pharmaceuticals, medical device products, technology products, entertainment products, or any other type of products and / or related services that may be part of, integrate, link, or operate VCNP604 in conjunction may, in embodiments without limitation, include intelligent products 650 that enable a set of capabilities such as data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, touch interfaces, remote operation, 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.

[0269] In embodiments, the management platform 604 may include a set of data processing layers 624, each configured to provide a set of functions that facilitate the development and deployment of intelligence, such as to facilitate automation, machine learning, artificial intelligence applications, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data processing layer 624 is 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 from or collected about value chain entities 652, as well as data from or collected about various data layers 624 or their services or components. In embodiments, 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 by a value chain network-oriented data storage system layer 624, which may in some cases be simply referred to herein as a data storage layer 624 or storage layer 624 for convenience. As shown in Figure 7, the data processing layer 624 may also include an adaptive intelligent system layer 614. The adaptive intelligent system layer 614 may include a set of data processing, artificial intelligence, and computing systems 634, which are described in more detail elsewhere in this disclosure. The data processing, artificial intelligence, and computing systems 634 may relate 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 computing systems 634 may, in some embodiments, be used in the following ways: the use of recurrent networks as an adaptive intelligence system operating on a blockchain of transactions in a supply chain to determine patterns; use with biosystems; opportunity mining (e.g., an artificial intelligence system may be used to monitor new data sources as opportunities for automatically deploying intelligence); automation of robotic processes (e.g., automation of intelligent agents for various workflows); edge and network intelligence (e.g., involved in 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, and adaptive sensing based on a kind of context sensing).

[0270] In embodiments, the data processing layer 624 may be depicted in the diagram as a vertical stack or ribbon and can represent many functionalities available to the platform 604, including storage, monitoring, and processing applications and resources, as well as combinations thereof. In embodiments, the set of functionalities of the data processing layer 624 may include a shared microservices architecture. In these examples, the set of capabilities may be deployed to provide several different services or applications, which may consist of one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or residing within a particular application or process. In some examples, the set of capabilities may include one or more activities marshalled 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 platform's sets of 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 platform's sets of functionalities may be deployed within at least a portion of a common architecture that can support common storage. In embodiments, one of the platform's sets of functionalities 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 capable of supporting one or more common processing frameworks. In embodiments, the set of capabilities of the data processing layer 624 may include examples where storage capabilities support scalable processing capabilities, scalable monitoring systems, digital twin systems, payment interface systems, and the like.These examples show that one or more software development kits may be provided by the platform along with deployment interfaces to facilitate the connection and use of the capabilities of the data processing layer 624. In further examples, an adaptive intelligent system may analyze, learn, configure, and reconfigure one or more of the capabilities of the data processing layer 624. In embodiments, platform 604 may include, for example, a common data storage schema that provides shipyard entity-related services and warehousing entity services. Many other applicable examples and combinations exist that are applicable to the above examples, which include many of the value chain entities disclosed herein. These examples may show platform 604 creating connectivity (e.g., supplying capabilities and information) across many value chain entities. In many examples, there are pairs (duplicate, triple, quadruple, etc.) of entities of similar types of value chains that use one or more smaller sets of the capabilities of the data processing layer 624 to deploy (interact with, depend on, etc.) a common data schema, a common architecture, a common interface, etc. While services and capabilities can be provided to a single value chain entity, a platform can demonstrate that it offers countless benefits to the value chain and consumers by supporting connectivity between value chain entities and the applications used by those entities.

[0271] [Value chain network entities managed by the platform] Referring to Figure 8, the value chain network management platform 604 is illustrated in relation to a set of value chain entities 652 that may be subject to management by platform 604, may be integrated with platform 604, may be integrated into platform 604, and / or may supply input to platform 604 or take output from platform 604, for example, a wide range of value chain activities (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 such as those related to various value chain network processes, workflows, activities, events and applications 630 (collectively referred to as “Applications 630” or simply “Activities”)). Connectivity with value chain entities 652 may be facilitated by a set of connectivity equipment 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 functions for individual services of the platform, for the data processing layer, for the entire platform, 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 referred to in this disclosure or in documents incorporated herein by reference, for example, but not limited to, machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loaders, cranes, lifts, transport vehicles, trucks, loaders, unloaders, packaging machines, picking machines, 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 products, e.g., finished goods, software products, hardware products, component products, materials, equipment items, consumer packaged goods items, consumer products, food products, beverage products, household goods, business supplies, 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, transport processes, sea shipping Examples include processes, inspection processes, transportation processes, loading / unloading processes, packaging / 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.), packaging / loading processes, financial processes (e.g., insurance processes, reporting processes, transaction processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others), wearables and portable devices 720 (mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-mounted devices, clothing-integrated devices, armbands, bracelets, neck-mounted devices, AR / VR devices, headphones, and many others), workers 718 (delivery drivers, seafarers, barge workers, port workers, dockworkers, 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, cargo handlers, 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, etc., suppliers 642 (suppliers of all kinds of goods and related services, parts suppliers, component suppliers, material suppliers, manufacturers and many others, etc.), customers 662 (including consumers, licensees, corporations, value-added and other resellers, retailers, end users, distributors and others who purchase, license or otherwise use categories of goods and / or related services), extensive operating facilities 712 (including loading and unloading docks, storage and warehousing facilities 654, safes, distribution facilities 658 and fulfillment centers 628, etc.), air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, etc.), maritime facilities 622 (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.), facilities and other items at starting points 610 and / or ending points 628, transport facilities 710 (container ships, barges, other floating assets 620, as well as land vehicles and other distribution systems used for transporting goods such as trucks, trains, etc. 632), items or factors that incorporate demand (e.g., demand factors 644) (market factors, i 738,These include robotic systems 744 (including mobile robots, cobots, robotic systems to assist human workers, robotic delivery systems, etc.), drones 748 (including those for package delivery, site mapping, monitoring or inspection, etc.), autonomous vehicles 742 (for package delivery, etc.), software platforms 752 (including enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, Platform as a Service, Infrastructure as a Service, software-based data storage platforms, analytics platforms, artificial intelligence platforms, etc.), and many others. In some exemplary embodiments, product 650 may be encompassed as intelligent product 650, or VCNP 604 may include intelligent product 650. Intelligent product 650 may be enabled by a set of capabilities such as data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech 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 comprise 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 from which the intelligent product can 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 include sensors, IoT devices, tags,Alternatively, it can be integrated into a value chain network through connectivity methods such as establishing connectivity around the intelligent product 650 via other components.

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

[0274] [Network characteristics of value chain network entities] Referring to Figure 9, the orchestration of a set of deeply interconnected value chain network entities 652 within a value chain network 668 by the value chain network management platform 604 is illustrated. Each of the value chain network entities 652 may have a connection to VCNP 604, a connection to another set of value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a cloud connection, or other connection), and / or a connection to another value chain network entity 652 via VCNP 604. The value chain network management platform 604 can configure or provide the resources that enable connectivity and manage applications 630 that utilize connectivity, such as coordinating the activities of a set of entities 652 by notifying applications 630 that include another set of entities 652 using information from one set of entities 652, and interacting with edge computing systems deployed on or within the entities 652 by inputting into the artificial intelligence system of VCNP 604 or a set of entities 652 or about them.

[0275] Entity 652 may be external, such that VCNP604 can interact with these entities 652. If VCNP604 acts as a control tower and establishes monitoring (e.g., establishing monitoring such as common monitoring across multiple entities 652), there may be an interface on a unified platform where a user can view various items such as their destinations, ports, air and rail assets, as well as orders. Next, it is conceivable to establish a common data schema that enables services operating in any one of these applications. This may involve taking any of the data flowing through or about any of these entities 652 and drawing the data into a framework that 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 the monitoring layer, stored in the common data schema in the storage layer, and then various intelligences may be trained to identify meaning across these entities 652. In an exemplary embodiment, if a decision is made that a supplier is bankrupt or has gone bankrupt, VCNP604 can then automatically trigger an alternative smart contract sent to a secondary supplier with modified terms. It could also involve managing different aspects of the supply chain. For example, in response to the confirmation that one or more suppliers have gone bankrupt (e.g., from a bankruptcy announcement), the demand side could immediately and automatically change its pricing. Other similar examples could be used based on what happens in its automation layer, which can be enabled by VCNP604. Then, in this interface layer of VCNP604, the user can use a digital twin to view all these entities 652 that are not normally displayed together and monitor what is happening to each of these entities 652, including identifying problematic conditions. For example, after seeing three-quarters bad financial reports regarding a supplier, the report can be flagged to be closely monitored for potential future bankruptcies, etc.

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

[0277] [Value chain network activities and applications managed by the platform] Referring to Figure 10, the set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or with a set of value chain network entities 652, includes, but is not limited to, supply chain management applications 812 (e.g., for managing the timing, quantity, logistics, shipment, delivery, and other details of orders for goods, components, and other items), asset management applications 814 (e.g., for managing value chain assets such as flotation assets (e.g., ships, boats, barges, and floating platforms), real estate (e.g., used for the locations of warehouses, ports, shipyards, distribution centers, and other buildings), equipment, machinery and fixtures (e.g., used for handling 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 relating to entities and assets in the value chain, including but not limited to payments, collateral, security, debt, customs, duties, levies, taxes, etc.), risk management applications 818 (e.g., for managing risks or liabilities relating to shipments, goods, products, assets, people, floating assets, vehicles, equipment items, components, information technology systems, security systems, security events, cybersecurity systems, property items, health conditions, death, fire, flood, weather, failure, negligence, business interruption, injury, property damage, business damage, breach of contract, etc.), demand management applications 824 (e.g., 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 a category of goods that can be supplied by or through a value chain product or service facility, including 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, product or service recommendation engines, and others (including those that use or enable one or more functions of Intelligent Product 650, or those that are performed using the intelligence functions of Intelligent Product 650), trading applications 858 (but not limited to buy applications, sell applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, analytical applications for analyzing value chain performance, yield, return on investment, or other metrics, etc.), and tax applications 858 50 (for example, but not limited to, managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, or other factors relating to taxes, customs duties, levies, taxation, customs duties, credits, fees, or other charges imposed by the government, such as customs duties, value-added tax, sales tax, income tax, property tax, municipal fees, pollution tax, renewable energy credits, pollution mitigation credits, import duties, export duties, etc.), Identity Management Applications 830 (for managing the identities of one or more entities 652 involved in the value chain, including, but not limited to, one or more identity verification applications, biometric verification applications, pattern-based identity verification applications, location-based identity verification applications, user behavior-based applications, fraud detection applications, network address-based fraud detection applications, blacklisting applications, whitelisting applications, content inspection-based fraud detection applications, or other fraud detection applications,Inventory management applications 820 (but not limited to, for managing inventory in fulfillment centers, distribution centers, warehouses, storage facilities, stores, ports, ships or other floating assets, or other locations), security applications, solutions or services 834 (hereinafter referred to as security applications, and for example, but not limited to, any of the identity management applications 830 described above, physical security systems (e.g., access control systems (using biometric access control, fingerprints, retinal scans, passwords and other access controls, etc.), safes, vaults, secure storage facilities, etc.), surveillance systems (using cameras, motion sensors, infrared sensors and other sensors, etc.), perimeter security systems, floating security systems for floating assets, cybersecurity systems (virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyberattack detection and remediation, packet inspection, traffic inspection, DNS attack remediation 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 scope of accidents or other damage events, such as safety management based on any data sources, events or entities pointed out in this disclosure or documents incorporated herein by reference) to improve worker safety, reduce the likelihood of damage to property, reduce accident risk, reduce the likelihood of damage to goods (such as cargo), for risk management relating to insured goods, collateral for loans, etc.), 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.), facility management applications 850 (including, but not limited to, information technology systems, for design purposes, for managing infrastructure, buildings, systems, real estate, personal assets and other assets involved in supporting a value chain, such as shipyards, ports, distribution centers, warehouses, docks, stores, fulfillment centers, storage facilities, etc.),Regulatory applications 852 (applications for regulating any of the applications, services, transactions, activities, workflows, events, entities, or other items pointed out in this Specified and documents incorporated herein by reference, such as regulations on authorized routes, authorized goods and products, authorized trading parties, required disclosures, privacy, pricing, marketing, provision of goods and services, use of data (including data privacy regulations, data retention regulations, etc.), banking, marketing, sales, financial planning, and many others), commerce applications, solutions or services 854 (marketplaces for e-commerce sites, online sites, auction sites or marketplaces, marketplaces for goods, advertising marketplaces, reverse auction sites, etc.) that manage or control systems and equipment on or around the premises, such as robot / autonomous vehicle systems, packaging systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, workflow systems for human assets, etc.), commerce applications, solutions or services 854 (marketplaces for e-commerce sites, online sites, auction sites or marketplaces, marketplaces for goods, advertising marketplaces, reverse auction sites, etc.) 832 (including, but not limited to, marketplaces, advertising networks, or other marketplaces), vendor management applications (including, but not limited to, applications for managing a set of vendors or prospective vendors, and / or for managing the procurement of a set of goods, components, or materials that may be supplied in a value chain, including functions such as vendor qualification, vendor rating, requests for proposals, requests for information, other assurances of obligation or performance, contract management), analytics applications (including, but not limited to, analytics applications relating to any of the data types, applications, events, workflows, or entities referred to throughout this disclosure or the documents incorporated herein by reference, including big data applications, user behavior applications, predictive applications, classification applications, dashboards, pattern recognition applications, econometric applications, financial yield applications, return on investment applications, scenario planning applications, decision support applications,Demand forecasting applications, demand planning applications, route planning applications, weather forecasting applications, and many others), pricing applications 842 (for example, but not limited to, those for pricing goods, services (including those referred to throughout this disclosure and the documents incorporated herein by reference), and smart contract applications, solutions, or services (collectively referred herein as smart contract applications 848, for example, any of the smart contract types referred to in this disclosure or the documents incorporated herein by reference, for example, smart contracts relating to the sale of goods, smart contracts relating to the order of goods, smart contracts relating to shipping resources, smart contracts relating to workers, smart contracts relating to the delivery of goods, smart contracts relating to the installation of goods, smart contracts for consideration of tokens or cryptocurrency, rights based on future conditions) This may include one or more of a wide range of application types such as smart contracts that attribute options, futures, or interest; smart contracts such as securities, commodities, futures, options, derivatives; smart contracts for present or future resources; smart contracts configured to consider or accommodate tax, regulatory, or compliance parameters; smart contracts configured to execute arbitrage; and many others. Thus, the value chain management platform 604 can host and enable interaction between a wide range of heterogeneous applications 630 (such terms include above and other value chain applications, services, solutions, etc.) so that any set or larger combination or permutation of such services can be improved compared to isolated applications of the same type.

[0278] Referring further to Figure 10, the set of applications 630 that are provided on VCNP 604, integrated with VCNP 604, and / or managed by or for VCNP 604, and / or include a set of value chain network entities 652, but are not limited to, payment applications 860 (for any of the applications 630 pointed out herein, for payment calculation (including based on situational factors such as taxes, tariffs, etc. applicable to the geography of entities 652), fund transfers, payment settlements to parties, etc.), process management applications 862 (for supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, order processes, navigation processes, and many others), etc., for managing any of the processes or workflows described through this disclosure, such as compatibility testing applications 864, for evaluating compatibility between value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein, etc. 650 compatibility between containers or packages and product 650, product 650 compatibility with a set of customer requirements, etc., compatibility between product 650 and another product 650 (such as when one is a replenishment, replacement, or replacement part for the other), compatibility between infrastructure and equipment entities 652 (such as between container ships or barges and ports or waterways, between containers and storage facilities, between trucks and roads, between drones or robots and cargo, between drones, AVs or robots and delivery destinations, etc., and many others), infrastructure test applications 802 (such as for testing the capabilities of infrastructure elements supporting product 650 or application 630 (but not limited to storage capacity, lifting capacity, mobility capacity, memory capacity, networking capacity, environmental control capacity, software capacity, security capacity, etc.)), and / or incident management applications 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments including value chain network entities 652, but not limited to vehicle accidents, worker injuries, shutdown incidents, property damage incidents,This includes product damage cases, product liability cases, regulatory non-compliance cases, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, etc., and combinations thereof), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others.

[0279] Referring further to Figure 10, the set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or with a set of value chain network entities 652, includes, but is not limited to, predictive maintenance applications 910 (for predicting, forecasting, and undertaking actions to manage failures, malfunctions, shutdowns, damage, required maintenance, required repairs, required services, required support, etc. for a set of value chain network entities 652 such as products 650, equipment, infrastructure, buildings, vehicles, etc.), logistics applications 912 (for logistics management for pickup, delivery, transfer of goods to transport facilities, loading, unloading, packing, picking, shipping, driving, and other activities related to scheduling and managing the movement of products 650 and other goods between origin and destination via various intermediate locations), and reverse logistics applications 914 (for logistics of returned products 650, waste, damaged goods, or other items that can be transported along the return logistics route). Waste reduction applications 920 (for reducing packaging waste, solid waste, energy waste, liquid waste, pollution, computing resource waste, human resource waste, or other waste related to value chain network entities 652 or activities, such as for processing tics), augmented reality, mixed reality and / or virtual reality applications 930 (for visualization of one or more value chain network entities 652 or activities related to one or more applications 630, such as the movement of products 650, the interior of a facility, the condition or state of goods items, one or more environmental conditions, weather conditions, packaging configuration for a container or set of containers, or many other things), demand forecasting applications 940 (for forecasting demand for products 650, product categories, potential products, and / or demand-related factors such as market factors, affluence factors, demographic factors, weather factors, economic factors, etc.), demand aggregation applications 942 (information on one or more products 650, categories, etc., including current demand for existing products and future demand for products not yet available,This may further include, for example, one for aggregating orders and / or commitments (which may optionally be embodied in one or more contracts, which may be smart contracts), a customer profiling application 944 (such as one for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, such as based on historical purchase 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 one for managing the supply chain of components for a set of products 650).

[0280] Referring further to Figure 10, the set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or accompanied by a set of value chain network entities 652, may further include, but are not limited to, policy management applications 868 (for example, to manage the execution of one or more workflows (which may include configuring policies on platform 604 for each workflow), to manage compliance with regulations (including maritime, food and drug, medical, environmental, health, healthcare, and pharmaceutical), to deploy one or more policies, rules, etc. for the governance of one or more value chain network entities 652 or applications 630). The platform 604 may automatically deploy governance functions to the relevant entities 652 and applications 630, for example, via the connectivity equipment 642, govern interactions with other entities (including policies on information sharing and access to resources), and govern data access (including privacy data, operational data). This includes governing the provision of resources (such as connectivity, computing, human resources, energy, and other resources), managing compliance with corporate policies, and managing compliance with contracts (including smart contracts), where Platform 604 may automatically deploy governance functions to the relevant entities 652 and applications 630, for example, via the connectivity equipment 642. This includes governing security access to infrastructure, products, equipment, locations, etc., including data, status data, and many other data types, and the Product Configuration Application 870 (Product Manager and / or Automated Product Configuration Process (optionally using Robotic Process Automation)) which allows the Product Manager and / or Automated Product Configuration Process (optionally using Robotic Process Automation) to determine the configuration of Product 650, including on-the-fly configuration during agile manufacturing, configuration or customization at the root (such as by 3D printing one or more features or elements), or remote configuration or customization by firmware download, field-programmable gate array configuration, software installation, etc.Warehouse management and fulfillment applications 872 (for managing warehouses, distribution centers, fulfillment centers, etc., including product selection, configuration of product storage locations, determination of routes for personnel and mobile robots to move products within the facility, picking and packing schedules, route and workflow determination, operation management of robots, drones, conveyors, and other facilities, scheduling for moving products to exits, etc., and many other functions), kit configuration and deployment applications 874 (allowing VCNP users to configure kits, boxes, or other pre-integrated, pre-provisioned, and / or pre-configured systems so that customers or workers can quickly deploy a subset of VCNP 604 functions for specific value chain network entities 652 and / or applications 630), and / or product testing applications 878 for testing products 650 (including testing of performance, function and feature activation, safety, compliance with policies or regulations, quality, quality of service, potential for failure, and many other factors).

[0281] Referring further to Figure 10, the set of applications 630 provided on VCNP604, integrated with VCNP604, and / or managed by or for VCNP604, and / or accompanied by a set of value chain network entities 652, further include, but are not limited to, the Maritime Fleet Management Application 880 (for managing a set of maritime assets such as container ships, barges, boats, etc., to determine the optimal route of fleet assets based on weather, market, traffic, and other conditions, to ensure compliance with policies and regulations, to ensure safety, to improve environmental factors, to improve financial indicators, and many others), the Shipping Management Application 882 (for managing a set of shipping assets such as trucks, trains, airplanes, etc., for example to optimize financial yield, to improve safety, to reduce energy consumption, to reduce delays, to mitigate environmental impacts, and many other purposes), the Opportunity Matching Application 884 (one Or for matching multiple demand factors with one or more supply factors, for matching the needs and capabilities of value chain network entities 652, for identifying reverse logistics opportunities, for identifying input opportunities to enrich analytics, artificial intelligence and / or automation, for identifying cost reduction opportunities, for identifying profit and / or arbitrage opportunities, and for many other purposes, etc.), workforce management applications 888 (for managing workers in various workforces, including workforces in or for fulfillment centers, ships, ports, warehouses, distribution centers, corporate management locations, retail stores, online / e-commerce site management facilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure), distribution and delivery applications 890 (for planning, scheduling, routing, and otherwise managing the distribution and delivery of products 650 and other items, etc.), and / or enterprise resource planning (ERP) applications 892 (for planning the use of enterprise resources, such as workforce 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 Figure 11, a high-level schematic diagram of one embodiment of the Value Chain Network Management Platform 604 is shown, which includes 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, trade, etc., or are owned, operated, supported or realized, or otherwise are part of, integrated with, or linked to, one or more value chain network processes, workflows, activities, events and / or applications 630, or are operated by the Platform 604 in relation to 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 goods, business supplies, consumables, pharmaceuticals, medical device products, technology products, entertainment products, or any other type of product or related service, which in embodiments may include intelligent products enabled by processing, networking, sensing, computing, and / or other Internet of Things capabilities). A value chain entity 652 is an entity that is involved in or for the purpose of a wide range of value chain activities (including a wide range of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities that relate to 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) in connection with or for such activities.

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

[0284] In embodiments, each data processing layer 608 has a set of application programming connection equipment 642 for automating data exchange with each of the other data processing layers 624. These may include data integration functions, such as extracting, transforming, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 630, for example, transforming data from one format or protocol to another as needed for one layer to consume output from another layer. In embodiments, the data processing layer 624 is 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 from or about value chain entities 652, as well as data from or about various data layers 624 or their services or components. For example, a stream of physiological data from wearable devices worn by workers undertaking tasks or consumers engaged in activities can be distributed via the monitoring system layer 614 to multiple different applications of the value chain management platform layer 604, such as facilitating monitoring of workers' physiological, psychological, performance levels, attention, or other states, and others facilitating operational efficiency and / or effectiveness. In embodiments, the monitoring system layer 614 facilitates alignment, such as time synchronization and normalization, of data collected with respect to one or more value chain network entities 652.For example, one or more video streams or other sensor data collected from or relating to a worker 718 or other entity within 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 set of video or other data can be understood by a system capable of processing the video, such as a machine learning system that manipulates images within the video, or changes between images within different frames of the video. In such an example, the monitoring system layer 614 may further align sets such as video, camera images, and sensor data with other data, such as streams of data from wearable devices, streams of data generated by value chain network systems (ships, lifts, vehicles, containers, cargo handling systems, packaging systems, delivery systems, drones / robots, etc.), and streams of data collected by mobile data collectors. Configuring the monitoring system layer 614 as a common platform accessed across many applications, or as a set of microservices, can dramatically reduce the number of interconnections required by owners or other operators within the value chain network to have a growing set of applications to monitor a growing set of IoT devices and other systems and devices under its control.

[0285] In embodiments, 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 by a value chain network-oriented data storage system layer 624, which in some cases is simply referred to herein as the data storage layer 624 or storage layer 624 for convenience. For example, various data collected about the value chain entity 652, as well as data generated by other data processing layers 624, may be stored in the data storage layer 624 so that any of the services, applications, programs, etc. of the various data processing layers 624 can access a common data source (which may consist of a single logical data source distributed across heterogeneous physical and / or virtual storage locations). This can facilitate a dramatic reduction in the amount of data storage required to handle the enormous amounts of data generated by or about the value chain network entity 652 as the applications 630 and the value chain network usage grow and proliferate. For example, a supply chain or inventory management application in value chain management platform layer 604, such as one for ordering replacement parts for machinery or equipment items, could access the same dataset of which parts have been replaced for a set of machinery, as a predictive maintenance application used to predict whether a ship's components or port equipment are likely to require replacement parts. Similarly, predictions can also be used for the resupply of goods.

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

[0287] In embodiments, the data storage system layer 624 may provide an extremely rich environment for collecting data that can be used for feature extraction or input for expert systems, analytical systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems, as disclosed through this disclosure and documents incorporated herein by reference. As a result, each application 630 within platform 604 and each adaptive intelligent system within adaptive intelligent system layer 614 can benefit from the data collected or generated by or for each of the others. In embodiments, the data storage system layer 624 may facilitate the collection of data that can be used for feature extraction or input for intelligent systems, such as development frameworks from artificial intelligence. For example, data collection may involve pulling and / or accommodating event logs (naturally stored or ad-hoc, as needed), performing periodic checks on onboard diagnostic data, or so. In embodiments, feature pre-computation may be deployed using, for example, AWS Lambda or various other cloud-based on-demand computing capabilities, with pre-computation, multiplexed signals, etc. In many examples, even when aggregating hundreds or tens of data types from relatively heterogeneous entities, there exist pairs of similar types of value chain entities (2x, 3x, 4x, etc.) that can utilize one or more sets of data handling layer 624 capabilities to deploy connectivity and services across value chain entities and applications used by those entities. In these examples, at least in part, various pairs of similar types of value chain entities that use connectivity and services across value chain entities and applications can direct information from the connected data pairs to artificial intelligence services, including the various neural networks and their hybrid combinations disclosed herein.In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the pairing of connected data. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairing. These genetic programming techniques may be shown to enhance the effectiveness of decisions established by artificial intelligence services. In these examples, the information from the pairing of connected data may be transferred to other layers on the platform, including to support or deploy robotic process automation, prediction, forecasting, and other resources, such that the shared data schema can be facilitated as a capability and resource for platform 604.

[0288] A wide range of data types can be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, but are not limited to these, including asset and facility data 1030, status data 1140 (such as showing the state, status status, or other indicators relating to 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 (such as 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), and event data 1034 (such as process events, financial events, transaction events, etc. relating to any of the wide range of events, including operational data, transaction data, workflow data, maintenance data, and many other types of data relating to or relating to events occurring within the value chain network 668 or relating to one or more applications 630).Claims data 664 (related to insurance claims such as business interruption insurance, product liability insurance, insurance on goods, facilities or equipment, flood insurance, insurance on contract-related risks, and many others; claim data related to product liability, general liability, workers' compensation, injury and other liability claims; claim data related to contracts such as supply contract performance claims, product delivery requirements, warranty claims, compensation claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.), accounting data 730 (related to data such as completion of contract requirements, satisfaction of debts, payment of customs duties, etc.), and risk management data 732 (related to supplied items, amounts, prices, delivery, sources, routes, customs information, etc.), among many other data types related to value chain network entities 652 and applications 630, including output events, input events, state change events, operation events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, charging events, shipping events, warehouse events, movement of goods, cross-borders, movement of cargo, inspection events, supply events, and many others), claim data related to product liability, general liability, workers' compensation, injury and other liability claims, claim data related to contracts such as supply contract performance claims, product delivery requirements, warranty claims, compensation claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.). .

[0289] In embodiments, the data processing layer 624 is configured in a topology that facilitates shared adaptive capabilities, which may be provided, managed, mediated, etc., by one or more of the services, components, programs, systems, or capabilities of the adaptive intelligence system layer 614, which for convenience is referred to herein as the adaptive intelligence layer 614. The adaptive intelligence system layer 614 may include a set of data processing, artificial intelligence, and computing systems 634, which are described in more detail elsewhere throughout this disclosure. Therefore, computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peered networks), and available cloud infrastructure), data storage resources (such as local storage on devices, storage resources within entities or environments in the value chain (including on-device storage, storage on asset tags, 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 way that coordinates or shares on behalf of operators, enterprises, etc., for the benefit of multiple applications, programs, workflows, etc. For example, the adaptive intelligence layer 614 may manage and provide available network resources so that low-latency resources are used for supply chain management applications (where rapid decision-making may be critical) and higher-latency resources are used for demand planning applications (among many other possibilities).As will be described in more detail through this disclosure and the documents incorporated herein by reference, various adaptations may be provided for various services and functions across various layers, including those based on application requirements, service quality, on-time delivery, service objectives, budget, cost, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, return on investment, profitability, uptime / downtime, worker utilization, and many others.

[0290] The value chain management platform layer 604, which may be referred to as the platform layer 604 for convenience in this specification, is a common application environment in which the operator utilizes multiple aspects of the value chain network environment or entity 652 (for example, common data storage in the data storage layer 624, common data collection or monitoring in the monitoring system layer 614, and / or common adaptive intelligence in the adaptive intelligence layer 614, whether the shared data is one-person, multiple-person, or anonymized, shared, pooled, and similarly licensed). Output from the application 630 of 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, and flows; 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 success and failure indicators, process or milestone completion indicators, correct or incorrect prediction indicators, correct or incorrect labeling or classification indicators, and success metrics (including those relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, etc.). The output from each application 630 is stored in the data storage layer 624, distributed for processing by the data acquisition layer 614, and can be used by the adaptive intelligence layer 614. Therefore, the cross-application nature of platform layer 604 facilitates the convenient organization of all the necessary infrastructure elements to add intelligence to any given application, such as providing machine learning on results from other applications or other elements of platform 604, thereby enriching the automation of a given application through machine learning based on results from other applications or other elements of platform 604, and enabling application developers to focus on application-native processes while benefiting from other capabilities of platform 604.In the examples, there may be systems, components, services, and other capabilities that can generally improve any of the outputs and results 1040 of processes and applications pursued by the use of one or more value chain network entities 652, or by optimizing one or more performance characteristics, or by using a platform 604. In some examples, outputs and results 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or similar actions involved in steps of a process that are intended to be automated. Details of the Data Storage Layer - Alternative Data Architecture

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

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

[0293] In embodiments, the value chain network-oriented data storage system 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, using NVMe, storage-attached networks, and 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 aperiodic graphs, data maps, data hierarchies, data clusters including links and nodes, self-organizing maps, etc.) within the graph database architecture 1124. In an exemplary embodiment, the knowledge graph may be a prime example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed aperiodic graph may be used. For a contingent workflow, a ring graph may be used. A graph database (e.g., graph database architecture vpc608) may include a knowledge graph, or the knowledge graph may be an example of a graph database. In an exemplary embodiment, the knowledge graph may include ontologs and connections (e.g., relationships) between ontologs in the knowledge graph. In an exemplary embodiment, the knowledge graph may be used to capture the explicit knowledge domain of a human expert, such that there may be an identification of an opportunity 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 are using this de facto knowledge base (from the knowledge graph) combined with capabilities that can be replicated by artificial intelligence, which may vary depending on the type of expert involved. For example, artificial intelligence such as a convolutional neural network might be used for spatiotemporal aspects that could be used for diagnosing problems or packing them into boxes in a warehouse. On the other hand, the platform can use different types of knowledge graphs for a self-organizing map of experts whose main job 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 lists, and analytical output artifacts. In embodiments, the data storage layer 624 may store data in digital threads, ledgers, etc., for maintaining over time serial or other records of entity 652, which includes 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 by means of access control, such as the use of access control, so that data storage and retrieval are optionally linked to local processes but optionally open to remote retrieval and storage options. In embodiments, the storage layer 624 may include one or more blockchains 1180, such as those storing ID data, transaction data, historical interaction data, etc., with access control that may be role-based or based on credentials associated with value chain entities 652, services, or one or more applications 630. The data stored by the data storage system 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as those 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 be related to, generated by, any of the value chain entities and activities described herein and the documents incorporated by reference. Adaptive intelligent systems and monitoring layers

[0295] Referring to Figure 13, additional details, components, subsystems, and other elements of an optional embodiment of platform 604 are illustrated. In various optional embodiments, the management platform 604 may include a set of applications 630, thereby enabling an operator or owner of a value chain network entity, or other user, to manage, monitor, control, analyze, or otherwise interact with one or more elements of the value chain network entity 652, such as any of the elements pointed out in relation above and throughout this disclosure.

[0296] In embodiments, the adaptive intelligent system layer 614 may include a set of systems, components, services, and other capabilities that collectively facilitate the collaborative development and deployment of the intelligent system, for example, those that can enhance one or more applications 630 in the application platform layer 604, those that can improve the performance of one or more components or the overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors), those that can improve other capabilities within the adaptive intelligent system layer 614, such as performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, or similar of one or more components of a value chain network-oriented data storage system 624), those that control, automate, or optimize one or more performance characteristics of one or more value chain network entities 652, or those that generally improve any of the processes and application outputs and results 1040 pursued by the use of 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, a machine miner 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction management systems 1900 (for setting up, provisioning, configuring and otherwise managing sets of interactions between sets of value chain network entities 652 in a value chain network 668), and a set of other systems.

[0298] In embodiments, the value chain monitoring system layer 614 and its data acquisition system 640 may include a wide range of systems for data acquisition. This layer may include, but is not limited to, a real-time monitoring system 1520 (onboard monitoring systems such as event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other transport assets, as well as onboard diagnostic (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, person tracking systems, ambient sensing systems installed in various environments where value chain activities and other events take place, as well as removable and replaceable monitoring systems such as mobile and cell phone data collectors, RFID and other tag readers, smartphones, tablets and other data-collecting mobile devices), and so Software interaction observation system 1500 (for recording and tracking events related to user interaction with a software user interface, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, and many others, as well as software interactions occurring as a result of other programs such as via APIs), mobile data collector 1170 (as extensively described herein and incorporated by reference), visual monitoring system 1930 (such as video and still image systems, LIDAR, IR and other systems that can visualize inspection systems that monitor entities 652 items, people, materials, components, machines, equipment, personnel, gestures, facial expressions, location, configuration and other factors or parameters, as well as processes, worker activities, etc.), interaction point system 1530 (dashboard,1940 (a machine condition monitoring system) (including, but not limited to, the use of video and still image cameras, motion sensing systems (including optical sensors, LIDAR, IR and other sensor sets), robot motion tracking systems (including tracking the movement of a human or a system attached to a physical entity) and many other things), and the use of video and still image cameras, motion sensing systems (including optical sensors, LIDAR, IR and other sensor sets), robot motion tracking systems (including tracking the movement of a human or a system attached to a physical entity), and many other things, and many other things, and the use of video and still image cameras, motion sensing systems (including optical sensors, LIDAR, IR and other sensor sets), and many other things, and the use of video and still image cameras, motion sensing systems (including optical sensors, LIDAR, IR and other sensor sets), and many other things, and the use of video and still image cameras, motion sensing systems (including optical sensors, IR and other sensor sets), and many other things, and the use of video and still image cameras, motion sensing systems (including optical sensors, IR and other sensor sets), and many other things, and the use of video and still image cameras, and the use of video and still image cameras, and many other things, and the use of video and still image cameras, and many other things, and the use of video and still image cameras, and many other things, and the use of video and still image cameras, and the use of video and still image cameras, and many other things, and the use of video and still image cameras, and the use of video and still image cameras, and many other things, and the use of video 1172 (including onboard monitors and external monitors), sensors and cameras 1950 and other IoT data acquisition systems 1172 (including, but not limited to, onboard sensors, sensors or other data collectors (including click tracking sensors and points of destination) of or relating to a value chain environment (e.g., origin, loading / unloading dock, vehicles or floating assets used to transport goods, containers, ports, distribution centers, storage facilities, warehouses, delivery vehicles, etc.), cameras for monitoring the entire environment, dedicated cameras for specific machines, processes, workers, etc., wearable cameras, mobile cameras, cameras placed on mobile robots, cameras on mobile devices such as smartphones and tablets, and many other sensor types disclosed in this disclosure or in documents incorporated herein by reference), 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),The system monitors any of the various Internet of Things (IoT) data collectors 1172, including user feedback systems 1534 (survey systems, touchpads, voice-based feedback systems, evaluation systems, facial expression monitoring systems, influence monitoring systems, gesture monitoring systems, etc.), behavioral monitoring systems 1538 (movements, shopping behavior, purchase behavior, click behavior, fraudulent or deceptive behavior, user interface interactions, product return behavior, behaviors indicating interest, attention, boredom, etc., mood-indicating behaviors (such as fidgeting, remaining still, moving closer, or changing posture), etc.), and those described in this disclosure as a whole and in documents incorporated herein by reference.

[0299] In embodiments, the value chain monitoring system layer 614 and its data acquisition 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 be by device identifier, network location, geolocation (such as by geofencing), indoor location (such as proximity to known resources such as IoT-enabled devices and infrastructure, Wi-Fi routers, etc., such as switches), cellular location (such as proximity to a cellular tower), identity management system (such as when entity 652 is associated with another entity 652, such as owner, operator, user, or enterprise, by identifiers assigned and / or managed by platform 604), etc. The entity discovery 1900 may initiate a handshake between sets of devices, such as to initiate interactions that serve various applications 630 or other capabilities of platform 604.

[0300] Referring to Figure 14, a management platform for an information technology system, such as a management platform for a value chain of goods and / or services, is depicted as a block diagram of functional elements and typical interconnections. The management platform includes, in particular, a user interface 3020 that provides 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 similar) for a set of demand management applications 824 and a set of 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 artificial intelligence 1160 through a set of data processing, artificial intelligence, and computing systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 operate on or in cooperation with a set of value chain applications (e.g., demand management applications 824 and 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 in this disclosure as a whole and in documents incorporated by reference.

[0301] In embodiments, the user interface may include an interface for configuring the artificial intelligence system 1160 to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of a category of goods 3010, such as by taking input from selected data sources of the value chain (e.g., data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and feeding it to one of the neural networks, the artificial intelligence system 1160, or other adaptive intelligence systems 614 described in this disclosure and other documents incorporated herein by reference. In embodiments, the selected data sources of the value chain may be applied either as input for classification or forecasting, or as output related to the value chain, the category of goods 3010, etc.

[0302] In embodiments, providing coordinated intelligence may include providing artificial intelligence capabilities such as an artificial intelligence system 1160. The artificial intelligence system may facilitate coordinated intelligence by processing data available in any of the value chain data sources for product categories, for a set of demand management applications 824 or a set of supply chain applications 812 or both, such as value chain processes, material lists, 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, and so on.

[0303] In embodiments, the user interface 3020 may, among other things, provide access to artificial intelligence capabilities, applications, systems, etc., for tailoring intelligence for value chain applications, particularly for the category of goods 3010. The user interface 3020 may be adapted to constitute user access to artificial intelligence capabilities that receive and respond to information describing the category of goods 3010, and the user may be guided through the user interface to artificial intelligence capabilities suitable for use with value chain applications (e.g., a set of demand management applications 824 and supply chain applications 812) that contribute to the goods / services of the category of goods 3010. The user interface 3020 can facilitate the provision of tailored intelligence consisting of artificial intelligence capabilities that provide tailored intelligence to specific operators and / or companies participating in the supply chain of the category of goods.

[0304] In an embodiment, the user interface 3020 may be configured to facilitate the user selecting and / or configuring multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present a set of demand management applications 824 and supply chain applications 812 as connected entities that each receive, process, and generate outputs that can be shared between the applications. The types of artificial intelligence systems 1160 may be presented to the user interface 3020 in response to the set of connected applications or their data elements being presented to the user interface, such as when the user places a pointer near the connected set of applications. In an embodiment, the user interface 3020 facilitates access to a set of adaptive intelligence systems and provides a set of functions that facilitate the development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent trading, intelligent operations, remote control, analysis, monitoring, reporting, status management, event management, and process management.

[0305] The adaptive intelligence system 614 may consist of data processing, artificial intelligence, and computing systems 634 that can work collaboratively to provide collaborative intelligence, such as when the artificial intelligence system 1160 operates on or responds to data collected or generated by other systems of the adaptive intelligence system 614, such as a data processing system. In embodiments, providing collaborative intelligence may include operating a portion of the set of artificial intelligence systems 1160 that employ one or more types of neural networks described herein and in documents incorporated herein by reference, to process either demand management application outputs or supply chain application outputs to provide collaborative intelligence.

[0306] In an embodiment, providing coordinated intelligence for a set of demand management applications 824 may include constituting at least one of the adaptive intelligence systems 614 (for example, through a user interface 3020, etc., for at least one demand management application selected from a list of demand management applications including demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, and marketing analytics applications), such as 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.

[0307] Similarly, providing coordinated intelligence for a set of supply chain applications 812 may involve configuring at least one adaptive intelligence system 614 for at least one supply chain application selected from a list of supply chain applications, including product timing management applications, product volume management applications, logistics management applications, shipping applications, delivery applications, order management applications for product management applications, parts management applications, and the like.

[0308] In one embodiment, the management platform 102 may facilitate access 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, such as via a user interface 3020. In such an embodiment, the user may request that supply be aligned with demand while ensuring profitability of the value chain for a category of goods 3010, etc. By providing access to artificial intelligence capabilities 1160, the management platform enables the 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, the management platform 102 may provide a set of adaptive intelligence systems 614 that provide artificial intelligence 1160 tailored to a set of demand management applications 824 and supply chain applications 812 for a product category 3020 by (automatically) determining the relationships between demand management applications and supply chain applications based, for example, on inputs used by applications, results generated by applications, and value chain outcomes, through a user interface 3020, etc. The artificial intelligence 1160 may be tailored, for example, by a set of data processing, artificial intelligence, and computing systems 634 available through the adaptive intelligence system 614.

[0310] In an embodiment, 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 provide coordinated intelligence for a 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 of 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 a category of goods determined by at least one demand management application of the set of demand management applications 824. For example, a behavior tracking demand management application may generate results regarding the behavior 3010 of the goods' use in the category of goods. The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010. This coordinated intelligence can optionally be automatically applied to the set of supply chain applications 812, for example, so that production resources or other resources in the value chain of the category of goods are allocated to the second product. In this example, a vendor responsible for purchasing shelves for a retailer might receive a new purchasing plan that allocates more retail shelf space to a second product, for example, by taking space away from a low-profit margin product.

[0311] In an embodiment, a set of artificial intelligence systems such as 1160 may provide tailored intelligence to a set of supply chain and demand management applications by determining arbitrarily temporal priorities of demand management application outputs that influence the control of supply chain applications, for example, so that the arbitrarily temporal demand for at least one of the products in product category 3010 can be met. Seasonal adjustments in prioritizing demand application results are an example of temporal changes. Prioritization adjustments may also be local; for example, a large college football team may be playing at its home stadium, and the regional supply of tailing supplies may be temporarily adjusted even if the results of the demand management application suggest that there is currently no demand for small propane stoves in a wider area.

[0312] A set of adaptive intelligence systems 614 that provide coordinated intelligence, such as by offering artificial intelligence capabilities 1160, may facilitate the development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent trading, intelligent operations, remote control, analysis, 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, in which the artificial intelligence systems can act on or respond to data collected and / or generated by other systems in the adaptive intelligence system layer (e.g., data processing systems, expert systems, machine learning systems, etc.).

[0313] In addition to providing coordinated intelligence configured for a specific product category, the coordinated intelligence may also be provided to specific value chain entities, such as supply chain operators, businesses, and companies, that participate in the supply chain of the product category.

[0314] Providing coordinated intelligence may involve employing neural networks to process at least one of the inputs and outputs of a set of demand management and supply chain applications. Neural networks 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. Neural networks may also be used with 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 process data available in any of several value chain data sources relating to 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, and product lifecycle management (PLM) systems, to provide coordinated intelligence. A neural network configured to provide coordinated intelligence may share adaptive capabilities with other adaptive intelligence systems 614, such as when those systems are configured in a topology that facilitates such shared adaptation. In embodiments, the neural network can facilitate the provision of available value chain / supply chain network resources for both a set of demand management applications and a set of supply chain applications.In one embodiment, the neural network may provide a tailored intelligence to improve at least one of a list of outputs, such as process output, application output, process results, and application results.

[0315] Referring to Figure 15, a management platform for an information technology system, such as a management platform for a value chain of goods and / or services, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes, among other things, a user interface 3020 that provides a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provides adaptive intelligence through the application of a hybrid artificial intelligence system 3060 for use with a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that can be produced and sold through the value chain, and through the application of artificial intelligence such as one or more expert systems, machine learning systems, etc. 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 computing systems 634. In embodiments, the hybrid adaptive intelligence system 614 is selectable and / or configurable via the user interface 3020 so that one or more of the hybrid adaptive intelligence systems 614 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 in this disclosure and documents incorporated by reference.The hybrid adaptive intelligence system 614 can facilitate the application of 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, each of the first and second type of artificial intelligence systems 1160 can independently, cooperatively, and arbitrarily coordinate their operations to provide coordinated intelligence for the operation of a value chain producing at least one of the goods in the category of goods 3010.

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

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

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

[0319] Referring to Figure 16, an information technology system management platform, such as a management platform for a value chain of goods and / or services, is depicted as a block diagram of functional elements and representative interconnections for providing a set of forecasts 3070. The management platform includes, in particular, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide a set of forecasts 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 can be produced and sold through the value chain. The adaptive intelligence systems 614 can deliver the set of forecasts 3070 through a set of data processing, artificial intelligence, and computing systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 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 related to 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 in this disclosure as a whole and in documents incorporated by reference. The adaptive intelligence system 614 may facilitate the application of adaptive intelligence capabilities to the adjustment sets of demand management application 824 and supply chain application 812, such as by generating a set of forecast values ​​3070 that can facilitate the adjustment of two sets of value chain applications, or by facilitating the adjustment of at least one demand management application and at least one supply chain application from at least each of those sets.

[0320] In embodiments, the set of forecasts 3070 includes at least one forecast of the impact on a supply chain application based on the current state of a coordinated demand management application, such as a forecast that demand for a good will decline faster than previously predicted. Conversely, the set of forecasts 3070 is true in that it includes at least one forecast of the impact on a demand management application based on the current state of a coordinated supply chain application, such as a forecast that a shortage of a good is likely to affect a measure of demand for a related good. In embodiments, the set of forecasts 3070 is a set of forecasts for adjustments in supply necessary to meet demand. Other forecasts include at least one forecast of a change in demand that will affect supply. Further other forecasts in the set of forecasts forecast a change in supply that will affect at least one of a set of demand management applications, such as a promotional application for at least one product in a product category. The forecasts in the set of forecasts may be as simple as setting the likelihood that the supply of a product in a product category 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 the provision of a set of predictions for a coordinated set of demand management and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that can be used to predict failure or problem conditions in a demand management application, such as a lack of adequately 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 the collection of machine operation information and machine preventive maintenance information.

[0322] In one 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 series of predictions 3070 may be provided for a set of coordinated demand management and supply chain applications for a product category by applying artificial intelligence capabilities to coordinate a set of demand management and supply chain applications.

[0324] In the embodiment, the set of predicted values ​​3070 may also be forecasts of outcomes for operating a value chain in a collaborative set of demand management and supply chain applications for product categories, and the user may run test cases of the collaborative set of demand management and supply chain applications to determine which sets are likely to produce desirable outcomes (feasible candidates for the collaborative set applications) and which sets are likely to produce undesirable outcomes.

[0325] Referring to Figure 17, an information technology system management platform, such as a management platform for a value chain of goods and / or services, is depicted as a block diagram of functional elements and representative interconnections for providing a set of classifications 3080. The management platform includes, in particular, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 make up a set of classifications 3080 through artificial intelligence, such as the application of artificial intelligence system 1160, and optionally, through one or more expert systems, machine learning systems, etc., for use with a set of adjustments for demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced, sold, resold, rented, leased, transferred, serviced, recycled, updated, upgraded, and similarly done 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 computing systems 634. In embodiments, the adaptive intelligence system 614 is selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 operate on or in cooperation with a set of coordinated value chain applications. The adaptive intelligence system 614 may include an artificial intelligence system that provides 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 in particular in this disclosure and in documents incorporated by reference. The adaptive intelligence system 614 may facilitate the application of adaptive intelligence capabilities to a coordinated set of demand management applications 824 and supply chain applications 812, for example, by generating a set of classifications 3080 that can facilitate the coordination of two sets of value chain applications, or by facilitating the coordination of at least one demand management application and at least one supply chain application from at least each of those sets.

[0326] In embodiments, the set of classifications 3080 includes at least one classification of the current state of a supply chain application for use by a coordinated demand management application, such as a classification of problem conditions 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 product that is likely to have a lower yield than previously expected. The reverse may also be true, in that the set of classifications 3080 includes at least one classification of the current state of a demand management application and its relationship to the coordinated supply chain application. In embodiments, the set of classifications 3080 is a set of classifications of supply adjustments necessary to meet demand, for example, adjustments to the needs of production workers would be classified differently from adjustments to third-party logistics providers. Other classifications may include at least one classification of perceived changes in demand and the resulting potential impact on supply management. Yet another classification in the set of classifications 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 product in a product category. The classifications in the set of classifications may be as simple as classifying the likelihood that the supply of a product in a product 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 the provision of a set of classifications 3080 for a coordinated set of demand management and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that can be used to classify failure or problem conditions in a demand management application, such as the classification of a lack of adequately validated feedback. The probabilistic neural network may be used to classify problem conditions in machines performing value chain operations (e.g., production machines, automated processing machines, packaging machines, shipping machines, etc.) as relating to at least one of machine operation information and predictive maintenance information.

[0328] In the embodiment, the set of classifications 3080 may be provided directly by the management platform 102 through a set of adaptive artificial intelligence systems. Furthermore, the set of classifications 3080 may be provided for the coordination of demand management applications and supply chain applications to product categories by applying artificial intelligence capabilities for coordinating a set of demand management applications and supply chain applications.

[0329] In an embodiment, the set of classifications 3080 may also be a classification of results for operating a value chain in a collaborative set of demand management and supply chain applications for product categories, allowing users to run test cases of the collaborative set of demand management and supply chain applications to determine which sets can produce results that are classified as desirable (e.g., viable candidates for the collaborative set's applications) and results that are classified as undesirable.

[0330] In an embodiment, the set of classifications may include a set of adaptive intelligence functions, such as a neural network, that can be adapted to classify information related to product categories. In one example, the neural network may be a multilayer feedforward neural network.

[0331] In some embodiments, performing classification may include classifying the discovered value chain entities as either demand-centric or supply-centric.

[0332] In an embodiment, the set of classifications 3080 may be achieved by using an artificial intelligence system 1160 to coordinate a set of coordinated demand management and supply chain applications. The artificial intelligence system can configure and generate the set of classifications 3080 as a means to coordinate the demand management and supply chain applications. For example, the classification of information flow across the entire value chain may be classified as relating to both demand management and supply chain applications, and this common relationship may be a point of coordination between applications. In an embodiment, the set of classifications may be classifications generated by artificial intelligence for the results of supply chain operations that depend on the coordinated demand management application 824 and supply chain application 812.

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

[0334] In embodiments, the user interface 3020 may include an interface for configuring an adaptive intelligence system 614 to take input from selected data sources of the value chain 3094 (such as data sources used by a coordinated set of demand management applications 824 and / or a set of supply chain applications 812) and feed it to a neural network or the like, to generate an automated control signal 3092 to, for example, enhance, control, improve, optimize, configure, adapt or otherwise influence the value chain of product category 3010. In embodiments, the selected data sources of the value chain may be used to determine aspects of the automated control signal, such as the temporal adjustment of control results related to the value chain for at least product category 3010.

[0335] In one example, a set of automated control signals may include at least one control signal to automate the execution of supply chain applications such as production start, automated material ordering, inventory check, and invoice creation applications in a coordinated set of demand management and supply chain applications. In yet another example of automated control signal generation, a set of automated control signals may include at least one control signal to automate the execution of demand management applications such as product recall applications and email distribution applications in a coordinated set of demand management and supply chain applications. In yet another example, automated control signals may control the timing of demand management applications based on the status of goods supply.

[0336] In embodiments, the adaptive intelligence system 614 may apply machine learning to supply results to automatically adapt a set of demand management application control signals. Similarly, the adaptive intelligence system 614 can apply machine learning to demand management results to automatically adapt a 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 a coordinated set of demand management and supply chain applications for a product category. The determined aspects can 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 target demand management and supply chain applications.

[0337] For example, automated control of supply chain applications may be limited by policies, operational restrictions, safety constraints, etc. A set of adaptive intelligent systems may determine a range of supply chain application control values ​​within which control can be automated. In embodiments, the range may be associated with supply rates, supply timing rates, product mixes in product categories, etc.

[0338] Embodiments for using an artificial intelligence system or capability to identify, configure, and adjust automated control signals are described herein. Such embodiments may further include a closed loop of feedback (e.g., status information, output information, results, etc.) from a set of adjustments of demand management and supply chain applications, which are optionally processed by machine learning and used to adapt an automated control signal for at least one product within a product category. The automated control signal may be adapted based on indications from the supply chain application, for example, that the yield of goods indicates a production problem. In this example, the automated control signal affects the 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 Figure 19, an information technology system management platform, such as a management platform for the value chain of goods and / or services, is depicted as a block diagram of functional elements and representative 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 results, parameters, routing activity information, etc. Within the set of value chain networks 3102, a selected value chain network 3104 is selected from which at least one information routing recommendation 3130 is provided. An artificial intelligence system 1160 may include a machine learning system and can be trained using a training set obtained from the results, 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 information routing recommendations 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 a 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 the information value of 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, including not only the timing of information acquisition and information consumption, but also 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). Therefore, information routing recommendations can be based on training on transaction types, information value, and combinations thereof.These are merely illustrative information routing recommendation training and recommendation foundation elements, and are presented here without limiting themselves to other elements for training and recommendation foundations.

[0340] In one embodiment, the artificial intelligence system 1160 may provide information routing recommendations 3130 based on the transaction type, the transaction type and information type, the network type, and so on. Information routing recommendations may be based on a combination of factors such as the information type and network type, for example, when the information type (streaming) is incompatible with the network type (small transaction).

[0341] In embodiments, the artificial intelligence system 1160 can use machine learning to deepen its understanding of the network within the selected value chain network 3104, including network topology, network load, network reliability, and network latency. This understanding can be combined, for example, with detected or expected network conditions to form information routing recommendations. Aspects such as the presence of edge intelligence in the value chain network 3104 can influence one or more information routing recommendations. For example, the type of information may be incompatible with the type of network, but the network may be configured with edge intelligence that can be leveraged by the artificial intelligence system 1160 to adapt the format of the information to be routed to be compatible with the type of network in question. This is also an example of network resources (e.g., presence, availability, and capabilities) such as edge computing, server access, and network-based storage resources, which are more general considerations for information routing recommendations. Similarly, value chain network entities may influence information routing recommendations. In embodiments, information routing recommendations can 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, information routing recommendations may include routing information to a first node where the information is partially consumed and partially processed for further routing, such as by dividing the partially processed portion for further routing into a destination-specific information set.

[0342] In embodiments, the artificial intelligence system 1160 can provide information routing recommendations based on objectives such as value chain network objectives and information routing objectives. Objective-based information routing recommendations may include routing objectives such as quality of service routing objectives and routing reliability objectives (which may be measured based on transmission failure rates, etc.). Other objectives may include measures of latency associated with one or more candidate routes. Information routing recommendations may also be based on the availability of information 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., a night production report that can be routed at 2 a.m. needs to be first needed by 7 a.m.), routing recommendations may include the use of lower-cost resources, the possibility of short delays in routing, etc. For information that is available immediately before it is needed (e.g., product test results need to be available within a few hundred milliseconds of the end of the test in order to maintain production uptime), etc.

[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 based on information persistence may select network resources based on availability, cost, etc., during the time period when the information is persistent.

[0344] Information value and its impact on information value can influence recommendations for information routing. For example, information that is valid for a single shipment (e.g., the quantity of goods produced) may effectively lose its value once that shipment is satisfactorily received. In such cases, recommendations for information routing might indicate routing the relevant information to all of its primary consumers while it is still valid. Similarly, routing information consumed by multiple value chain entities may require each value chain entity to coordinate to receive the information at a desired time / moment, such as during the same production shift, at the start of the workday (which may differ if the entities are in different time zones).

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

[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 load, and recommendation priorities generated after the information for the first recommendation has been routed.

[0347] Referring to Figure 20, an information technology system management platform, such as a management platform for the value chain of goods and / or services, is depicted as a block diagram of functional elements and representative interconnections for semi-conscious problem recognition of pain points in the value chain network. The management platform includes a set of value chain network entities 3152 from which entity-related data 3160 is collected, including results, parameters, activity information, etc., associated with the entities. Within the set of value chain network entities 3152, a set of selected value chain network entities 3154 is selected from 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, which includes training on results associated with value chain entities, such as parameters related to the operation of the value chain, value chain activity information, etc. The artificial intelligence system may further employ machine learning to facilitate the learning of problem state factors 3180 that may characterize the problem states input as training data. These factors 3180 may be further utilized by an instance of artificial intelligence 1160' running on computing resources 3170 that are local to the value chain network entity experiencing the pain point problem / outcome. The goal of such an configuration of the artificial intelligence system, dataset, and value chain network is to recognize problem conditions in a selected portion of the value chain.

[0348] In embodiments, recognizing a problem state may be based on an analysis of variance of value chain metrics (e.g., load, latency, delivery time, cost, etc.), particularly variances occurring over time on specific metrics. Variances exceeding a variance threshold (e.g., an arbitrarily dynamic range of results from value chain operations such as production, shipping, and customs clearance) may indicate a pain point.

[0349] In addition to detecting problem conditions, platform 102 predicts pain points, at least partially based on correlations with detected problem conditions, through methods such as semi-sensory problem recognition. Correlations can be derived from the value chain, such as the inability to deliver international goods until a shipper is processed at customs, or the inability to provide highly reliable sales forecasts without high-quality field data. In embodiments, predicted pain points may be points in value chain activities further along the supply chain, activities occurring in related activities (e.g., tax planning is related to tax law), etc. Predicted pain points can be assigned risk values ​​based on aspects of the detected problem conditions and the correlation between the predicted pain point activities and the problem condition activities. If a production operation can receive materials from two suppliers, a problem condition with one of the suppliers may indicate a low risk of pain point in material use. 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 increase, for example, depending on how far along the value chain the product is.

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

[0351] In embodiments, semi-perceptual problem recognition can also be based on unstructured information such as digital communications and voice messages that may be shared, transmitted, or received among people involved in value chain operations. For example, natural language processing of email communications between employees within a company may indicate, for instance, the degree of dissatisfaction with a value chain supplier. While supplier-related data (e.g., on-time production, quality) may be within an acceptable range of variation, the information in this unstructured content may reveal potential points of distress, such as personal issues with key participants in the supplier's operations. By employing natural language processing, artificial intelligence, and optionally machine learning, the recognition of problem conditions can be enhanced.

[0352] In embodiments, semi-sensory problem recognition may be based on an analysis of the variance of a scale of an operation / entity / application in the value chain, including the variance of a given scale over time, the variance of two related scales, etc. In embodiments, the variance of outcomes 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 for the variance of outcomes and apply that range to the measurement of a selection set of value chain network entities, such as entities that share one or more similarities, to facilitate the detection of a problem state. In embodiments, the acceptable range for the variance of outcomes may represent a problem state trigger threshold that can be used by a local instance of artificial intelligence to signal a problem state. In such a scenario, a problem state may be detected if at least one measurement, such as a value chain activity / entity, is greater than the problem state threshold determined by the artificial intelligence. Analysis of Variance (ANOVA) for problem state detection may include detecting variances in the start / end times of scheduled value chain network entity activities, variances in at least one of the following: production time, production quality, production rate, production start time, production resource availability or its trend, variances in measurements of shipping supply chain entities, variances in transfer times from one transport mode to another (e.g., when the variance exceeds a transport mode problem state threshold), and variances in quality testing.

[0353] In embodiments, the semi-sensory problem recognition system may include machine learning / artificial intelligence predictions of pain points that are further correlated along the supply chain by detected pain points, such as risks and / or needs, including overtime, expedited shipping, and discounts on product prices.

[0354] In an embodiment, a machine learning / artificial intelligence system may process results, parameters, and data collected from a set of data sources related to a set of entities and activities in a value chain to detect at least one pain point selected from a list of pain points, including delayed 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, defective goods, returns, waste, wasted energy, wasted labor, untrained labor, poor customer service, empty transport vehicles on the return journey, excessive fuel prices, excessive duties, etc.

[0355] Referring to Figure 21, an information technology system management platform, such as a management platform for a value chain of goods and / or services, is depicted as a block diagram of functional elements and representative interconnections, 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 used by an artificial intelligence system 1160 to provide an automated coordination 3220 of value chain network activities 3212 for a set of products 3210 for 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 and configuration information. This collected information may be configured as activity information 3208 for a set of activities related to a set of products 3210 for 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 crucial role in some activities within the value chain network, more automated coordination and unified orchestration of supply and demand can be achieved using artificial intelligence systems (including, for example, machine learning, expert systems, and self-organizing systems, as described herein and in documents incorporated herein by reference) to coordinate supply chain activities. The use of artificial intelligence can not only provide significant capabilities to end users but also further enrich the emergence of self-adaptive systems, including Internet of Things (IoT) devices and intelligent products, which can play a crucial role in the automated coordination of supply chain activities.

[0357] For example, an IoT system deployed in fulfillment center 628 may work with an intelligent product 650 to receive customer feedback on product 650, and an application 630 for fulfillment center 628, upon receiving customer feedback on a problem with product 650 via a connection to the intelligent product 650, may initiate a workflow to perform corrective actions on similar products 650 before they are shipped out of fulfillment center 628. The workflow may consist of an artificial intelligence system 1160 that analyzes problems with product 650, gains a deeper understanding of the value chain network activities that produce the product, determines the resources required for the workflow, and adapts any existing workflows in coordination with inventory and production systems. The artificial intelligence system 1160 can further work with a demand management application to address temporary impacts on product availability, etc.

[0358] In embodiments, the 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, such as using artificial intelligence to provide coordinated intelligence and coordinate activities, to facilitate the coordination of demand management activities, supply chain activities, etc. As an example, artificial intelligence may facilitate the determination of relationships between value change network activities based on the inputs used by the activities and the results produced by the activities. Artificial intelligence may be integrated with and / or work in conjunction with platform activities, such as value chain network entity activities, to continuously monitor activities, identify temporal aspects requiring adjustment (e.g., when changes in supply temporarily affect demand activities), 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 set of value chains 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 can further enhance the automated coordination of value chain network entity activities through intelligent operations such as generating sets of predictions, sets of classifications, and automated control signals (which can be communicated between value chain network entities). Other exemplary AI-based impacts on the automated coordination of value chain network entity activities include machine learning-based information routing and recommendations therefor, semi-conscious problem recognition based on both structured (e.g., production data) and unstructured (e.g., human emotions) sources, and such.Artificial intelligence systems can facilitate the automatic coordination of product sets or corporate value chain network entity activities based on adaptive intelligence provided by the platform regarding product categories in which a company's product set can be grouped. For example, adaptive intelligence may be provided by the platform for a product category of curtain hangers, and the company's product set may include a line of adaptive curtain hanger hangers. Through the understanding developed for the overall curtain hanger category, artificial intelligence capabilities can be applied to corporate value chain network activities to automate aspects of the value chain, such as information exchange between activities.

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

[0360] In an embodiment, the digital twin 1700 leverages 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 the data storage layer 624) and other inputs (such as from the monitoring layer 614) collected with respect to the value chain entity 652, and may also share outputs, events, state information, and outputs. Collectively, these can provide a much richer environment for enriching the content within the 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 documents incorporated by reference), and through the use of content collected by the monitoring layer 614 and the data acquisition system 640.

[0361] In embodiments, the digital twin 1700 may also be used in connection with sharing or convergence processes among various sets of applications 630 in the application layer 604, such as, for example, but not limited to, convergence processes involving security application 834 and inventory management application 820, integration automation 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 can consume different subsets of available attributes of data objects maintained on the blockchain or that use a set of nodes and links of a common knowledge graph) that can be connected to the digital twin 1700 so that the 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 can be connected to and shared with the digital twin 1700 so that the digital twin 1700 is updated accordingly to enable role-based access control, role-based authorization for remote control, identity-based event reporting, and the like. In an embodiment, the converged process may include a shared process flow across applications 630, which includes a subset of larger flows that are connected to and can be shared with the digital twin 1700, such that the digital twin 1700 can be updated accordingly. For example, an inspection flow relating to a value chain network entity 652 can provide services to an analytics solution 838, an asset management solution 814, and others.

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

[0363] Referring to Figure 23, any of the value chain network entities 652 can be represented in one or more sets of digital twins by inputting value chain network data objects 1004 into the digital twin 1700, such as event data 1034, state data 1140, or other data relating to the value chain network entity 652, application 630, or components or elements of platform 604 as described throughout this disclosure.

[0364] Therefore, platform 604 can integrate, consolidate, manage, control, coordinate, or otherwise handle any of the diverse digital twins 1700, such as distribution twins 1714 (representing distribution facilities, assets, objects, workers, etc.), warehouse twins 1712 (representing warehouse facilities, assets, objects, workers, etc.), port infrastructure twins 1714 (representing facilities, assets, objects, workers, etc., such as seaports and airports), shipping facility twins 1720, operational facility twins 1722, customer twins 1730 (representing the physical, behavioral, demographic, psychological, financial, historical, affinity, interests, and other characteristics of customer groups or individual customers, etc.), and worker twins 1740 (representing the physical attributes, physiological data, state data, psychometric information, emotional state, fatigue / energy state, attention state, skills, training, capabilities, roles, authority, responsibilities, work status, activities, and other attributes and stakeholders of workers, etc.). Wearable / portable device twin 1750, process twin 1760, machine twin 1770 (various machines used to support value chain network 668, etc.), 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, requirements These include twins for various industries such as vehicular activity (1640), retailers (1790), e-commerce and online site operators (1800), waterways (1810), roadways (1820), railroads (1830), aviation facilities (1840, including twins for aircraft, runways, airports, hangars, warehouses, air routes, refueling facilities, and other assets, goods, and workers used in connection with the air transport of 650 products), autonomous vehicles (1850), robotics (1860), drones (1870), and logistics factors (1880).Each of these may have the characteristics of a digital twin, as described throughout the documents incorporated herein by reference, such as mirroring or reflecting changes in the state of related physical objects or other entities, providing functionality for modeling the behavior or interactions of related physical objects or other entities, enabling simulation, providing a state representation, and many other things.

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

[0366] [Entity Discovery and Interaction Management] Referring to Figure 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 set of entity discovery systems 1900 for, for example, identifying a set of value chain network entities 652, identifying types of value chain network entities 652, identifying specific value chain network entities 652, as well as for managing the identity of value chain network entities 652, including resolving ambiguity (such as when a single entity is identified differently in different systems, or when different entities are identified similarly), entity ID deduplication, entity ID resolution, entity ID hardening (such as enriching data objects with additional data collected about entities within the platform), etc. Entity discovery 1900 may also include the discovery of inter-entity relationships, 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 entities, what inputs and outputs are exchanged between entities, etc.), and what rules or policies govern entities. Platform 604 may include a set of entity interaction management systems 1902, which include one or more artificial intelligence systems (including any of the types described through this disclosure) for managing the set of inter-entity interactions discovered through entity discovery 1900, and which learn on a training set of data to manage inter-entity interactions based on how entities have been managed by human supervisors or other systems.

[0367] As an exemplary example of many possibilities, the entity discovery system 1900 can be used to discover networked cameras indicating the entrances to facilities that produce products for a company, and to identify the interfaces or protocols necessary to access a feed of video content from the cameras. The entity interaction management system 1902 can then be used to interact with the interfaces or protocols to set up access to the feed and provide the feed to another system for further processing, such as having the artificial intelligence system 1160 process the feed to discover content relevant to the company's activities. For example, the artificial intelligence system 1160 may process image frames of the video feed to discover markings (such as agricultural product labels, SKUs, images, logos), shapes (such as packages of a particular size or shape), activities (such as inbound or outbound activities), etc., that may indicate that a product has moved through an inbound dock. This information may be used as a substitute, supplement, or to verify other information, such as RFID tracking information. Similar discovery and interaction management activities may be carried out with any of the types of value chain network entities 652 described throughout this disclosure.

[0368] [Robotic Process Automation in Value Chain Networks] Referring to Figure 25, the adaptive intelligence layer 614 may include a robotic process automation (RPA) system 1442, which may include a set of components, processes, services, interfaces, and other elements for developing and deploying automation capabilities for various value chain entities 652, environments, and applications 630. 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, including the functions, components, workflows, processes, value chain network entities 652, and other processes of the VCNP 604 itself.

[0369] In an embodiment, the robotic process automation 1442 may leverage 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 within the data storage layer 624) and other inputs (such as from the monitoring layer 614) collected with respect to the value chain entity 652, as well as outputs, events, and state information and outputs, collectively, which can 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 documents incorporated by reference). For example, the asset management application 814 may use robotic process automation 1442 to automate asset inspection processes that are normally performed or supervised by humans (for example, by automating processes that include visual inspections using video or still images from a camera displaying images of entity 652, for example, the robotic process automation 1442 system may be trained to automate inspections by observing the interaction of a set of human inspectors or supervisors having an interface used to identify, diagnose, measure, parameterize, or otherwise characterize potential defects or favorable characteristics of equipment or other assets. In embodiments, the interaction of human inspectors or supervisors may include a labeled dataset in which labels or tags indicate defect types, favorable characteristics, or other characteristics, so that a machine learning system can learn to identify the same characteristics using the training dataset, which can be used to automate the inspection process so that defects or favorable characteristics are automatically classified and detected in a set of video or still images, which can be used within the value chain network asset management application 814 to flag items that require further inspection, items that should be rejected, items that should be disclosed to prospective buyers, items that should be repaired, etc.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 could enrich robotic process automation 1442 of asset management application 814, such as information on the current characteristics of items from a particular vendor in the supply chain for a certain asset, which could help input characteristics of the asset for the purpose of facilitating inspection processes, negotiation processes, delivery processes, etc. 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 in conjunction with various functionalities of VCNP 604. For example, in some embodiments, robotic process automation 1442 may be described as training robots to operate and automate tasks that were, at least to a considerable extent, controlled by humans. One of these tasks may be used to train robots 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 this trained robotic process automation 1442 (e.g., a trained agent or a trained robotic process automation system) may be made to perform these tasks that were previously performed by humans. For example, the robotic process automation 1442 may utilize software that provides software interaction observations (such as mouse movements, mouse clicks, cursor movements, navigation actions, menu selections, keyboard typing, and many others) such as product purchases by customer 714, which are recorded and / or tracked by the software interaction observation system 1500.This could involve monitoring the user's mouse clicks, mouse movements, and / or keyboard typing to learn to perform the same clicks and / or typing. In another example, robotic process automation 1442 could utilize software to learn physical interactions with the robot and other systems, and train the robotic system to sequence or assume the same physical interactions. For example, the robot might be trained to rebuild a set of bearings by being shown a video of a person performing the task. This could involve tracking physical interactions and tracking interactions at the software level. Robotic process automation 1442 could understand what the underlying capabilities being deployed are, so that VCNP604 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 sharing or convergence process among various sets of applications 630 in the application layer 604, such as, for example, a convergence process involving a security application 834 and an inventory application 820, an integrated automation of a blockchain-based application 844 with a vendor management application 832, and many others. In embodiments, the convergence process may include a shared data structure for multiple applications 630 (including one that tracks the same transactions on a blockchain but can consume different subsets of available attributes of data objects maintained on the blockchain, or one that uses a set of nodes and links in a common knowledge graph). For example, a transaction indicating a change of ownership of entity 652 may be stored on the blockchain and used by multiple applications 630 to enable role-based access control, role-based authorization for remote control, identity-based event reporting, etc. In embodiments, the converged process may include a shared process flow across applications...

Claims

1. A robot fleet management platform for configuring robot fleet resources, It includes a set of one or more processors that execute a set of computer-readable instructions, The set of one or more processors is A job configuration system that receives a job request and determines a set of robot tasks to be performed by a robot fleet based on the job content associated with the job request and at least one fleet objective within 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 operates a set of intelligence services to generate at least one recommended robot task and associated contextual information to facilitate robot selection and task ordering in a robot task workflow. A job workflow system that generates a workflow defining the execution order of robot tasks based on the fleet resource configuration data structure and the set of robot tasks, A workflow simulation system configured to simulate the performance of a job requested by a job request and generate simulation results based on the workflow and job execution simulation environment, wherein the simulation results are used in such a way that the system recursively redefines 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 from the set of fleet objectives corresponding to the job request. A robot fleet management platform characterized by comprising: a job execution plan generator that 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. The robot fleet management platform according to claim 1, characterized in that 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 of the job content suitable for automation by robots.

3. The robot fleet management platform according to claim 2, wherein the job configuration system includes a task definition system that establishes a set of robot tasks, each defining at least a robot type and a task objective, the set of robot tasks being at least partially based on the portion of the job request that is suitable for automation by robots and satisfies a first fleet objective from the set of fleet objectives.

4. The robot fleet management platform according to claim 1, characterized in that the fleet resource configuration data structure defines a set of task associations and a set of robot adaptive instructions, each of the task associations associates at least one robot operating unit with each robot task in the set of robot tasks, and the set of robot adaptive instructions defines a method for adapting one or more robot operating units of a robot fleet to perform each task assigned to the robot.

5. The robot fleet management platform according to claim 4, characterized in that the workflow simulation system generates the simulation results by applying the workflow in the job execution simulation environment, which includes a digital model of the robot operating unit assigned to the robot fleet and a digital model of the task definition.

6. The robot fleet management platform according to claim 1, characterized in that the job configuration system interacts with the fleet intelligence layer to propose alternative tasks that satisfy a second fleet objective.

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

8. The robot fleet management platform according to claim 7, characterized in that the first fleet objective includes a criteria for utilizing fleet resources.

9. The robot fleet management platform according to claim 1, characterized in that the job configuration system receives a specific robot type to be used when executing the robot task from the fleet configuration proxy service.

10. The robot fleet management platform according to claim 9, characterized in that the job configuration system configures a set of robot tasks based on the specific robot type provided by the fleet configuration proxy service.

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

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

13. The robot fleet management platform according to claim 12, characterized in that the job configuration system generates a data structure for each task in the set of tasks and stores the data structure in a library of robot tasks, which is indexed by information indicating the job request and at least one identifier of the robot type and the robot operating unit.

14. The robot fleet management platform according to claim 1, characterized in that the job configuration system matches the requirements of constraints identified in the job request with the capabilities of the robot when identifying the type of robot to satisfy the task objective.

15. The robot fleet management platform according to claim 1, characterized in that the job configuration system generates multiple robot tasks for multiple different robot types in order to achieve task objectives.

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

17. The robot fleet management platform according to claim 16, characterized in that at least one of the fleet objectives is compatible with available robot operating units.

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

19. The robot fleet management platform according to claim 1, characterized in that the job configuration system refers to descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks when defining the set of tasks.

20. The robot fleet management platform according to claim 1, characterized in that the job workflow system refers to descriptive information of a sensor detection package indicating a preferred sequence of sensing tasks when defining the workflow of a robot task.

21. The robot fleet management platform according to claim 1, characterized in that the job workflow system generates the workflow of robot tasks based on the dependency of the second task on the first task to satisfy the objective of the second task.

22. The robot fleet management platform according to claim 1, characterized in that the workflow simulation system operates a digital twin of the tasks in the set of tasks to determine an optimized workflow sequence for the tasks.

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