Systems, methods and kits for edge distributed storage and querying in value chain networks

Abstract

The method and system provide a distributed database configured to leverage the storage and processing power of edge devices and a query language for efficiently querying the distributed database system. The database layer of an application stack can be distributed across nodes of a network, including edge nodes, and vast amounts of data can be stored locally at these nodes to provide access to the data in response to queries. In such a distributed database environment, queries can be received and / or executed by edge distributed node points. In accordance with the present technology, the entire network environment can be viewed as a seamless database, and an edge query language can provide resolution of the queries.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to Indian Application No. 202211008709, filed February 18, 2022. This application claims the benefit of U.S. Provisional Application No. 63 / 302,013, filed January 21, 2022, U.S. Provisional Application No. 63 / 299,710, filed January 14, 2022, U.S. Provisional Application No. 63 / 282,507, filed November 23, 2021, and U.S. Provisional Application No. 63 / 187,325, filed May 11, 2021, the entire disclosures of which are incorporated by reference.

[0002] (Field) The present disclosure relates to information technology methods and systems for managing value chain network entities, including supply chain and demand management entities, and to the field of enterprise management platforms, and more particularly to edge-distributed databases and query languages ​​for storing and searching value chain data. [Background technology]

[0003] (background) Historically, many of the various categories of goods purchased and used by home consumers, businesses, and other customers were primarily supplied in a relatively linear manner, with manufacturers and other suppliers of finished goods, parts, and other items delivering the items to shipping companies, freight forwarders, etc., who then delivered the items to warehouses for temporary storage, to retailers where the customers purchased them, or directly to the customers' locations. Manufacturers and retailers used a variety of sales and marketing activities to stimulate and meet customer demand, including product design, shelf and advertising placement, and pricing.

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

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

[0006] The existence of more data and new types of data offers many opportunities for organizations to gain a competitive advantage, but also presents challenges such as complexity and volume that can overwhelm users and lead them to miss insight opportunities. There is a need for methods and systems that allow companies to not only capture data, but also transform it into insights, and then transform those insights into informed decisions and timely execution of efficient operations.

[0007] It is becoming more common to acquire large data sets from thousands, or potentially millions, of devices (containing numerous sensors) distributed across multiple organizations in a value chain network. For example, in retail stores, RFID (Radio Frequency Identification) tags are prevalent on individual items. In these and other similar situations, the sheer number of data streams can overwhelm the ability to transmit data over the network and to create effective automated, centralized decision-making.

[0008] The proliferation of data-generating devices (e.g., sensors) has created opportunities to manage networks, such as value chain networks, with input from vast numbers of distributed points of semi-intelligent control. However, current approaches often rely on limited, centralized data collection due to bandwidth, storage, processing, and / or other limitations. Summary of the Invention

[0009] (overview) According to some embodiments of the present disclosure, a method for processing queries on data stored in a distributed database is disclosed. The method includes receiving, at an edge device, a query on data stored in the distributed database from a query device. The method further includes storing, by the edge device, the query in a dynamic ledger maintained by the distributed database. The method further includes detecting, by the edge device, that summary data has been saved in the dynamic ledger. The method further includes generating, by the edge device, an approximate response to the query based on the summary data stored in the dynamic ledger. The method further includes transmitting the approximate response to the query device.

[0010] In some embodiments, the query is an EDQL query. In some embodiments, the query specifies a sharding algorithm, which specifies the location of the data stored in the distributed database. In some embodiments, the dynamic ledger is a blockchain.

[0011] In some embodiments, storing the query in the dynamic ledger includes transmitting, by the edge device, the query to an aggregator. In some of these embodiments, the aggregator is a blockchain node.

[0012] In some embodiments, generating an approximate response to the query based on the summarized data stored in the dynamic ledger further includes using the summarized data to generate a probability distribution model for data corresponding to the query and using the probability distribution model to generate the approximate response. In some of these embodiments, the method further includes receiving a second query for data stored in the distributed database and using the probability distribution model to generate an approximate response to the second query without storing the second query in the dynamic ledger. Additionally or alternatively, the probability distribution model is a neural network, and generating the probability distribution model includes training the neural network.

[0013] In some embodiments, the method further includes generating a query plan based on the received query. In some of these embodiments, the query plan includes sending the query to other edge devices, and the method further includes sending the query to the other edge devices. Additionally or alternatively, the query plan includes sending the query to an aggregator, and the method further includes sending the query to the aggregator.

[0014] In some embodiments, the method further includes executing a query against edge storage connected to the edge device to obtain a partial query result. In some of these embodiments, an approximate response to the query is further provided based on the partial query result.

[0015] In some embodiments, the edge device is an edge device / aggregator. In some embodiments, detecting that the summarized data has been stored in the dynamic ledger includes detecting that a threshold percentage of the edge devices have caused the summarized data to be stored in the dynamic ledger.

[0016] In some embodiments, the summarized data is generated based on data stored on other edge devices. In some embodiments, the summarized data includes statistical data. In some embodiments, the summarized data includes outlier data. In some embodiments, the data is sensor data.

[0017] According to some embodiments of the present disclosure, a method for processing queries on data stored in a distributed database is disclosed. The method includes receiving, at an edge device, a query for data stored in the distributed database from a query device, the query being a request for data stored in the edge device and data stored in other edge devices. The method further includes executing, by the edge device, the query to find partial query results that include data stored in the edge device. The method further includes generating, by the edge device, statistical information based on the partial query results. The method further includes determining, by the edge device, a statistical confidence associated with the partial results based on the statistical information. The method further includes generating, by the edge device, an approximate response to the query based on the statistical information. The method further includes transmitting the approximate response to the query device.

[0018] In some embodiments, the query is an EDQL query. In some embodiments, the query specifies a sharding algorithm, and the sharding algorithm specifies a location of the data stored in the distributed database. In some embodiments, the method further includes storing the statistical information in the dynamic ledger.

[0019] In some embodiments, generating an approximate response to the query based on the statistical information further includes using the statistical information to generate a probability distribution model of data corresponding to the query and using the probability distribution model to generate the approximate response. In some of these embodiments, the method further includes receiving a second query for data stored in the distributed database and using the probability distribution model to generate an approximate response to the second query. Additionally or alternatively, the probability distribution model is a neural network, and generating the probability distribution model includes training the neural network.

[0020] In some embodiments, the method further includes generating a query plan based on the received query. In some embodiments, the approximate response to the query is further based on the partial query results. In some embodiments, the edge device is an edge device / aggregator. In some embodiments, the statistical information includes outlier data.

[0021] In some embodiments, the data stored on the edge device includes sensor data. In some of these embodiments, the sensor data is collected from sensors connected to the edge device. Additionally or alternatively, the sensor data is collected from sensors connected to different edge devices.

[0022] In some embodiments, the distributed database comprises a mesh network of edge devices. In some embodiments, the method further includes receiving instructions from the aggregator to replay a subset of the data stored on the edge device to a second edge device, and transmitting the subset of the data to the second edge device.

[0023] In some embodiments, the query is a distributed join query. In some of these embodiments, generating the partial query results includes using a lookup table stored on the edge device. In some of these embodiments, the lookup table is a distributed lookup table. Additionally or alternatively, the distributed join query is performed without network overhead.

[0024] According to some embodiments of the present disclosure, a method for optimizing a distributed database is disclosed. The method includes receiving, at an aggregator, one or more query logs including past queries received by the distributed database. The method further includes generating, by the aggregator, a query prediction model based on the one or more query logs. The method further includes predicting, by the aggregator, future queries using the query prediction model, the future queries predicted to be received by an edge device.

[0025] In some embodiments, the data for responding to the predicted future queries is composed of data stored on another edge device. In some of these embodiments, the method further includes finding data for responding to the predicted future queries using a sharding algorithm. In some of these embodiments, the sharding algorithm is a neural network algorithm. Additionally or alternatively, the sharding algorithm is a genetic algorithm. Additionally or alternatively, the sharding algorithm is a logic algorithm.

[0026] In some embodiments, the data for responding to predicted future queries is summarized data. In some of these embodiments, the summarized data includes statistical data. Additionally or alternatively, the summarized data includes outlier data. Additionally or alternatively, the method further includes instructing, by the aggregator, another edge device to generate the summarized data. Additionally or alternatively, the method further includes storing the summarized data in a dynamic ledger maintained by the aggregator. In some of these embodiments, the dynamic ledger is a blockchain.

[0027] In some embodiments, the data for responding to predicted future queries is a probability distribution model. In some of these embodiments, the method further includes generating the probability distribution model based on data stored on another edge device. In some of these embodiments, the method further includes storing the probability distribution model on a dynamic ledger maintained by the aggregator.

[0028] In some embodiments, the future query is an EDQL query. In some embodiments, the data for responding to the future query comprises sensor data. In some embodiments, the distributed database comprises a mesh network of edge devices.

[0029] In some embodiments, the predicted future queries are distributed join queries. In some of these embodiments, the data for responding to the predicted future queries is a lookup table.

[0030] According to some embodiments of the present disclosure, a method for processing queries for data stored in a distributed database is disclosed. The method includes monitoring, by an edge device, one or more pending data requests stored in a dynamic ledger. The method further includes detecting, by the edge device, a pending data request including a query for data stored in the distributed database, the query being a request for data stored on the edge device and a request for data stored on another edge device. The method further includes executing, by the edge device, the query to find partial query results that include data stored on the edge device. The method further includes storing, by the edge device, summarized data in the dynamic ledger.

[0031] In some embodiments, the summarized data comprises statistical data. In some embodiments, the summarized data comprises outlier data. In some embodiments, the dynamic ledger is a blockchain.

[0032] In some embodiments, storing the summarized data in the dynamic ledger includes transmitting the summarized data to an aggregator responsible for maintaining the dynamic ledger. In some of these embodiments, the aggregator is a blockchain node.

[0033] In some embodiments, the method further includes generating a probability distribution model based on the summarized data and storing the probability distribution model in a dynamic ledger.

[0034] In some embodiments, the query is an EDQL query. In some embodiments, the data stored in the distributed database includes sensor data. In some embodiments, the distributed database comprises a mesh network of edge devices.

[0035] According to some embodiments of the present disclosure, a method for processing queries on data stored in a distributed database is disclosed. The method includes, at an edge device, receiving a query on data stored in the distributed database from a query device, the query including a distributed join that references at least two tables, the at least two tables being distributed across a plurality of edge devices constituting the edge device. The method further includes, by the edge device, obtaining one or more distributed lookup tables. The method further includes, by the edge device, executing a query using the one or more distributed lookup tables to find partial query results that include data stored on the edge device. The method further includes, by the edge device, generating an approximate answer to the query using the partial query results.

[0036] In some embodiments, the query is an EDQL query. In some embodiments, the query specifies a sharding algorithm, which specifies the location of the data stored in the distributed database.

[0037] In some embodiments, the distributed lookup table is stored in a dynamic ledger. In some of these embodiments, the dynamic ledger is a blockchain. Additionally or alternatively, the method further includes storing the query in the dynamic ledger by sending the query to an aggregator.

[0038] In some embodiments, generating an approximate response to the query using the partial query results further comprises: using the partial query results to generate a probability distribution model of data corresponding to the query; and using the probability distribution model to generate the approximate response. In some of these embodiments, the probability distribution model is a neural network, and generating the probability distribution model includes training the neural network.

[0039] In some embodiments, the method further includes generating a query plan based on the received query. In some of these embodiments, the query plan includes sending the query to other edge devices, and the method further includes sending the query to the other edge devices. Additionally or alternatively, the query plan includes sending the query to an aggregator, and the method further includes sending the query to the aggregator.

[0040] In some embodiments, the edge device is an edge device / aggregator.

[0041] In some embodiments, the method further includes generating summary data based on the partial query results. In some of these embodiments, the summary data includes statistical data. Additionally or alternatively, the summary data includes outlier data. Additionally or alternatively, the data is sensor data.

[0042] In some embodiments, the distributed database comprises a mesh network of edge devices. In some embodiments, the distributed database comprises a fully connected network of edge devices. In some embodiments, the method further includes receiving instructions from the aggregator to replay a subset of the data stored on the edge device to a second edge device and transmitting the subset of the data to the second edge device. In some embodiments, the distributed join query is performed without network overhead.

[0043] According to some embodiments of the present disclosure, a method for optimizing a distributed database is disclosed. The method includes receiving, at an aggregator, one or more query logs including past queries received by the distributed database. The method further includes determining, by the aggregator, common queries received by one or more edge devices. The method further includes determining, by the aggregator, that at least one edge device failed to respond to the common queries received by the at least one edge device. The method further includes causing the aggregator to transmit data to the at least one edge device to respond to the common queries.

[0044] In some embodiments, the data for responding to the common query is composed of data stored on another edge device. In some of these embodiments, the method further includes locating the data for responding to the common query using a sharding algorithm. In some of these embodiments, the sharding algorithm is a neural network algorithm. Additionally or alternatively, the sharding algorithm is a genetic algorithm. Additionally or alternatively, the sharding algorithm is a logic algorithm.

[0045] In some embodiments, the data for responding to predicted future queries is summarized data. In some of these embodiments, the summarized data includes statistical data. Additionally or alternatively, the summarized data includes outlier data. Additionally or alternatively, the method further includes instructing, by the aggregator, another edge device to generate the summarized data. Additionally or alternatively, the method further includes storing the summarized data in a dynamic ledger maintained by the aggregator. In some of these embodiments, the dynamic ledger is a blockchain.

[0046] In some embodiments, the data for responding to the common query is a probability distribution model. In some of these embodiments, the method further includes generating the probability distribution model based on data stored on another edge device. In some of these embodiments, the method further includes storing the probability distribution model on a dynamic ledger maintained by the aggregator.

[0047] In some embodiments, the common query is an EDQL query. In some embodiments, the data for responding to the common query comprises sensor data. In some embodiments, the distributed database comprises a mesh network of edge devices. In some embodiments, the common query is a distributed join query. In some of these embodiments, the data for responding to the common query is a lookup table.

[0048] According to some embodiments of the present disclosure, a method for prioritizing predictive model data streams is disclosed. The method includes receiving, by a first device, a plurality of predictive model data streams, each predictive model data stream including a set of model parameters for a corresponding predictive model, each predictive model being trained to predict future data values ​​of a data source. The method further includes prioritizing, by the first device, each of the plurality of predictive model data streams. The method further includes selecting at least one of the predictive model data streams based on the corresponding priority. The method further includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model stream. The method further includes predicting, by the first device, future data values ​​of the data source using the parameterized predictive model.

[0049] In some embodiments, the selected at least one predictive model data stream is associated with a high priority. In some embodiments, selecting includes suppressing non-selected predictive model data streams based on a priority associated with each non-selected predictive model data stream. In some embodiments, assigning a priority to each of the plurality of predictive model data streams includes determining whether each set of model parameters is anomalous. In some embodiments, assigning a priority to each of the plurality of predictive model data streams includes determining whether each set of model parameters has changed from a previous value.

[0050] In some embodiments, the set of model parameters comprises at least one vector.

[0051] In some of these embodiments, the at least one vector includes a motion vector associated with the robot. In some of these embodiments, the future data values ​​include one or more future predicted positions of the robot.

[0052] In some embodiments, the predictive model predicts inventory levels of the item, and the method further includes detecting a future shortage of the item based on the future data values ​​and taking action to avoid out-of-stock of the item. In some embodiments, the predictive model is a behavioral analysis model, and the future data values ​​indicate a predicted behavior of an entity. In some embodiments, the predictive model is an augmentation model, and the future data values ​​correspond to an inoperable sensor. In some embodiments, the predictive model is a classification model, and the future data values ​​indicate a predicted future state of a system including one or more sensor devices. In some embodiments, the sensor is an RFID sensor associated with a cargo, and the future data values ​​indicate a future location of the cargo. In some embodiments, the sensor is a security camera, and the data stream includes motion vectors extracted from video data captured by the security camera. In some embodiments, the sensor is a vibration sensor measuring vibrations generated by a machine, and the future data values ​​indicate a potential need for maintenance on the machine.

[0053] According to some embodiments of the present disclosure, a digital product network system is disclosed. The system includes a set of digital products, each having a product processor, a product memory, and a product network interface. The system further includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and control tower processor collectively include non-transitory instructions that program the digital product network system to generate product-level data at the product processor, transmit the product-level data from the product network interface, receive the product-level data at the control tower network interface, encode the product-level data as a product-level data structure configured to communicate parameters indicated by the product-level data across the set of digital products, and write the product-level data structure to at least one of the product memory and the control memory.

[0054] In some embodiments, the product network control tower is a remotely located server or at least one of the at least one control product of the set of digital products. In some embodiments, the product processor and the control tower processor are further programmed to communicate based on a shared communication system configured to facilitate communication of product-level data from the set of digital products therebetween and with the product control tower. In some embodiments, the set of digital products and the product network control tower have a set of microservices and a microservices architecture. In some embodiments, the system further includes a display associated with the product network control tower or at least one of the set of digital products, wherein the digital product network system is further programmed to generate a graphical user interface with the at least one user interface display, generate parameters of at least one digitally enabled product of the set of digital products in the at least one user interface display, and generate proximity indications of nearby digital products of the set of digital products in the at least one user interface display.

[0055] In some embodiments, generating the proximity display includes generating a proximity display of proximate products that are geographically proximate. In some of these embodiments, the digital product network is further programmed to filter proximate products by at least one of product type, product capability, or product brand. Additionally or alternatively, generating the proximity display includes generating a proximity display of proximate products that are proximate to one of the set of digital products by product type proximity, product capability proximity, or product brand proximity.

[0056] In some embodiments, the digital product network system is further programmed to define a data integration system. In some embodiments, the digital product network system is further programmed to provide edge computation and edge intelligence configured for edge distributed decision making between sets of digital products. In some embodiments, the digital product network system is further programmed to provide edge computation and edge intelligence configured for edge network bandwidth management between or outside of the sets of digital products.

[0057] In some embodiments, the digital product network system is further programmed to have a distributed ledger system. In some of these embodiments, the distributed ledger system is a blockchain ledger. In some embodiments, the digital product network system is further programmed to have a quality management system having a system for capturing product complaints in a collection of digital products. In some embodiments, the digital product network system is further programmed for at least one of identifying a state of the set of digital products, encoding the state as one of the parameters of a product-level data structure, and tracking or monitoring the state across the set of digital products.

[0058] In some embodiments, the digital product network system is further programmed with a smart contract system to enable creation of smart contracts based on the product-level data structure. In some of these embodiments, the digital product network system is further programmed to configure the smart contracts based on a co-location sensitive configuration of conditions, such that the conditions of the smart contracts depend on the proximity of multiple digital products in the set of digital products. In some embodiments, the digital product network system is further programmed with a robotic process automation (RPA) system configured to gamify interactions based on what digital products are in the set of digital products. In some embodiments, the digital product network system includes a robotic process automation (RPA) system and is further programmed to generate an RPA process based on the use of multiple digital products in the set of digital products.

[0059] According to some embodiments of the present disclosure, a computerized method is disclosed for a processor that is at least one of a set of digital products or a product network control tower, where the set of digital products each have a product processor, a product memory, and a product network interface, and the product network control tower has a control tower processor, a control tower memory, and a control tower network interface. The method includes generating product level data in the product processor. The method further includes transmitting the product level data from the product network interface. The method further includes receiving the product level data at the control tower network interface. The method further includes encoding the product level data as a product level data structure configured to communicate parameters indicated by the product level data across the set of digital products. The method further includes writing the product level data structure to at least one of the product memory and the control memory.

[0060] According to some embodiments of the present disclosure, a digital product network system is disclosed. The system includes a set of digital products, each having a product memory, a product network interface, and a product processor programmed with product instructions. The system further includes a product network control tower having a control tower memory, a control tower network interface, and a control tower processor programmed with control tower instructions. The system further includes a digital twin system defined at least in part by at least one of the product instructions or the control tower instructions to encode a set of digital twins representing the set of digital products.

[0061] In some embodiments, the digital twin system is further defined to encode a hierarchical digital twin. In some embodiments, the digital twin system is further defined to encode a set of composite digital twins, each composed of a set of discrete digital twins of the set of digital products. In some embodiments, the digital twin system is further defined to encode a set of digital product digital twins representing multiple digital products of the set of digital products. In some embodiments, the digital twin system is further defined to model traffic of moving elements within the set of digital products. In some embodiments, the digital twin system is further defined to have a playback interface for the set of digital twins that allows a user to play back data of a situation within the digital twin and observe a visual representation of events associated with the situation.

[0062] In some embodiments, the digital twin system generates an adaptive user interface and adapts at least one of the available data, functionality, or visual representations in the adaptive user interface based on at least one of a user's relevance to or proximity to the digital product set of digital products. In some embodiments, the digital twin system is further defined to manage interactions between multiple digital product digital twins of the set of digital twins. In some embodiments, the digital twin system is further defined to generate and update self-expanding digital twins associated with the set of digital products.

[0063] In some embodiments, the digital twin system is further defined to: aggregate performance data from multiple digital twins of the set of digital twins for a common asset type represented in the multiple digital twins; and associate the aggregated performance data as a performance dataset for search. In some embodiments, the digital twin system is further defined to match owners of identical or similar products in a marketplace of digital twin data. In some embodiments, the digital twin system is further defined to lock the set of digital twins upon detection of a security threat in a digital product of the set of digital products.

[0064] In some embodiments, the digital twin system is further defined to have an in-twin marketplace. In some of these embodiments, the in-twin marketplace provides data. In some embodiments, the in-twin marketplace provides services. In some embodiments, the digital twin system is further defined to provide components. In some embodiments, the digital twin system is further defined to include an application program interface (API) between the set of digital twins and a marketplace associated with the set of digital products. In some embodiments, the digital twin system is further defined to have a twin store market system for providing at least one of access to or rights to at least one of the set of digital twins or data associated with the set of digital twins.

[0065] According to some embodiments of the present disclosure, a computerized method is disclosed for a processor that is at least one of a set of digital products or a product network control tower, where the set of digital products each have a product processor, a product memory, and a product network interface, and the product network control tower has a control tower processor, a control tower memory, and a control tower network interface. The method further includes encoding the set of digital twins in a digital twin system, where the set of digital twins represents the set of digital products.

[0066] In some embodiments, the method further includes encoding a set of composite digital twins, each of which is composed of a set of discrete digital twins of the set of digital products.

[0067] According to some embodiments of the present disclosure, a method for performing a quantum computing task is disclosed. The method includes providing a quantum computing system. The method further includes receiving a request from a quantum computing client to perform the quantum computing task via the quantum computing system. The method further includes performing the requested quantum computing task via the quantum computing system. The method further includes returning a response related to the performed quantum computing task to the quantum computing client.

[0068] In some embodiments, the quantum computing system is a quantum annealing computing system. In some embodiments, the quantum computing system supports one or more quantum computing models selected from the set including a quantum circuit model, a quantum Turing machine, a spintronics computing system, an adiabatic quantum computing system, a one-way quantum computer, and a quantum cellular automaton.

[0069] In some embodiments, the quantum computing system is physically implemented using an analog approach. In some of these embodiments, the analog approach may be selected from the list of quantum simulation, quantum annealing, and adiabatic quantum computing. In some embodiments, the quantum computing system is physically implemented using a digital approach. In some embodiments, the quantum computing system is an error-correcting quantum computer. In some embodiments, the quantum computing system applies trapped ions to perform quantum computing tasks.

[0070] In some embodiments, the quantum computing task relates to automatically discovering smart contract configuration opportunities in a value chain network. In some of these embodiments, the quantum established smart contract application is selected from the set of reserving a set of robots from a robot fleet, reserving a smart container from a smart container fleet, and executing a transfer pricing agreement between subsidiaries. In some embodiments, the quantum computing task relates to risk identification or risk mitigation. In some embodiments, the quantum computing task relates to fast sampling from a stochastic process for risk analysis. In some embodiments, the quantum computing task relates to graph clustering analysis for anomaly or fraud detection. In some embodiments, the quantum computing task relates to generating predictions.

[0071] According to some embodiments of the present disclosure, a method for performing a quantum computing optimization task is disclosed. The method includes providing a quantum computing system. The method further includes receiving a request from a quantum computing client to perform the quantum computing optimization task via the quantum computing system. The method further includes performing the requested quantum computing optimization task via the quantum computing system. The method further includes returning a response related to the performed quantum computing optimization task to the quantum computing client.

[0072] In some embodiments, the quantum computing system is a quantum annealing computing system. In some embodiments, the quantum computing system is a quantum annealing computing system. In some embodiments, the quantum computing system supports one or more quantum computing models selected from the set including a quantum circuit model, a quantum Turing machine, a spintronics computing system, an adiabatic quantum computing system, a one-way quantum computer, and a quantum cellular automaton.

[0073] In some embodiments, the quantum computing system is physically implemented using an analog approach. In some of these embodiments, the analog approach may be selected from the list of quantum simulation, quantum annealing, and adiabatic quantum computing. In some embodiments, the quantum computing system is physically implemented using a digital approach. In some embodiments, the quantum computing system is an error-correcting quantum computer. In some embodiments, the quantum computing system applies trapped ions to perform quantum computing tasks.

[0074] In some embodiments, the quantum computing optimization task is a smart container-based freight shipping price optimization task. In some of these embodiments, the quantum computing system is configured to optimize pricing using qubit-based computational methods. In some embodiments, the quantum computing system is configured to optimize the design or configuration of a product, device, vehicle, or service in a value chain network.

[0075] According to some embodiments of the present disclosure, a smart shipping container system is disclosed, the system including a shipping container housing, the system further including an artificial intelligence enabled chipset.

[0076] In some embodiments, the type of smart shipping container system is selected from the set of tank container, general-purpose dry van, rolling floor container, garment container, ventilated container, temperature-controlled container, bulk container, open-top container, open-sided container, log cradle, platform-based container, rotating container, mixed container, aviation container, automobile container, and bioprotective container. In some embodiments, the smart shipping container system is a smart package. In some embodiments, the smart shipping container system includes a mechanism that allows exterior or interior walls, housing elements, or other interior elements to expand or contract, such as to increase or decrease the volume of the container or to change the dimensions of one or more partitions of space within the container. In some embodiments, the smart shipping container system includes a self-assembly mechanism. In some embodiments, the smart shipping container system includes a self-disassembly mechanism. In some embodiments, the smart shipping container shape is selected from the set including a rectangular prism, a cube, a sphere, a cylinder, an organic-like, and a bio-shape. In some embodiments, the smart shipping container material is selected, at least in part, from the following set: corrugated weathering steel, steel alloys, stainless steel, aluminum, cast iron, concrete, ceramic material(s), other alloys, glass, other metals, plastic, plywood, bamboo, cardboard, and wood. In some embodiments, the smart shipping container system is a 3D printed smart container. In some embodiments, the smart shipping container system includes a 3D printer.

[0077] According to some embodiments of the present disclosure, a smart shipping container system is disclosed. The system includes a shipping container housing. The system further includes an artificial intelligence-enabled chipset. The shipping container is configured for autonomous driving.

[0078] In some embodiments, the type of smart shipping container system is selected from the set of: tank container, general-purpose dry van, rolling floor container, garment container, ventilated container, temperature-controlled container, bulk container, open-top container, open-sided container, log cradle, platform-based container, rotating container, mixed container, aviation container, automobile container, and bioprotective container. In some embodiments, the smart shipping container system is a smart package. In some embodiments, the smart shipping container system includes a mechanism that allows exterior or interior walls, housing elements, or other interior elements to expand or contract, such as to increase or decrease the volume of the container or to change the dimensions of one or more partitions of space within the container. In some embodiments, the smart shipping container system includes a self-assembly mechanism. In some embodiments, the smart shipping container system includes a self-disassembly mechanism. In some embodiments, the smart shipping container shape is selected from the set including: cuboid, cube, sphere, cylinder, organic-like, and bio-shaped. In some embodiments, the smart shipping container material is selected, at least in part, from the set of: The set can be used on corrugated weathering steel, steel alloys, stainless steel, aluminum, cast iron, concrete, ceramic material(s), other alloys, glass, other metals, plastic, plywood, bamboo, cardboard, and wood. In some embodiments, the smart shipping container system is a 3D printed smart container. In some embodiments, the smart shipping container system includes a 3D printer.

[0079] According to some embodiments of the present disclosure, a method for updating one or more properties of one or more shipping digital twins is disclosed. The method includes receiving a request to update one or more properties of one or more shipping digital twins. The method further includes retrieving one or more shipping digital twins needed to fulfill the request. The method further includes retrieving one or more dynamic models needed to fulfill the request. The method further includes selecting data sources from a set of available data sources based on one or more inputs of the one or more dynamic models. The method further includes acquiring data from the selected data sources. The method further includes calculating one or more outputs using the acquired data as one or more inputs of the one or more dynamic models. The method further includes updating one or more properties of the one or more shipping digital twins based on outputs of the one or more dynamic models.

[0080] In some embodiments, the digital twin is a digital twin of the smart container. In some embodiments, the digital twin is a digital twin of the shipping environment. In some embodiments, the digital twin is a digital twin of the shipping entity. In some embodiments, the dynamic model incorporates data selected from vibration, temperature, pressure, humidity, wind, precipitation, tides, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, imagery, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, light level, finance, cost, stock market, news, social media, revenue, workers, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration datasets.

[0081] In some embodiments, the data source is selected from the set of an Internet of Things connected device, a machine vision system, an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a three-axis vibration sensor, a single-axis vibration sensor, an optical vibration sensor, and a crosspoint switch. In some embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more characteristics and the respective types of the one or more digital twins indicated in the request. In some embodiments, the one or more dynamic models are identified using a lookup table.

[0082] According to some embodiments of the present disclosure, a robot fleet management platform is disclosed. The platform includes a computer-readable storage system that stores a resource data store that maintains: a robot inventory that indicates a plurality of robots that can be assigned to a robot fleet, and for each robot, indicates a set of baseline characteristics of the robot and the robot's respective status, the robot inventory including a plurality of multi-purpose robots that can be configured for different tasks and different environments; and a component inventory that indicates different components that can be provided to one or more multi-purpose robots, and for each component, indicates a respective set of extended capabilities corresponding to the component and the component's respective status. The platform further includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively receive a request for the robot fleet to execute a job. The set of one or more processors collectively determine a job definition data structure based on the request, the job definition data structure defining a set of tasks to be performed in executing the job. A set of one or more processors collectively determine a robot fleet configuration data structure corresponding to a job based on the set of tasks and the robot inventory, the robot fleet configuration data structure assigning a plurality of robots selected from the robot inventory to the set of tasks defined in the job definition data structure, the plurality of robots including one or more assigned multi-purpose robots. The set of one or more processors collectively determine a configuration for each of the assigned multi-purpose robots based on the respective tasks assigned to the assigned multi-purpose robots and the component inventory. The set of one or more processors collectively configure the one or more assigned multi-purpose robots based on the respective configurations. The set of one or more processors collectively deploy the robot fleet to perform a job.

[0083] In some embodiments, the robot inventory includes special-purpose robots. In some embodiments, determining the robot fleet configuration data structure is further based on a job environment. In some embodiments, determining the robot fleet configuration data structure is further based on a job budget. In some embodiments, determining the robot fleet configuration data structure is further based on a timeline for completing the job. In some embodiments, the robot inventory includes special-purpose robots, and determining the robot fleet configuration data structure is further based on an available inventory of special-purpose robots. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a job environment. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a job budget. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a timeline for completing the job. In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring at least one robot system selected from a list of robot systems including a robot baseline system, a module system, a robot control system, and a robot security system.

[0084] In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring one or more of a software robot module or a hardware robot module, and in some of these embodiments, the hardware robot module is an interchangeable module.

[0085] In some embodiments, configuring the tasks of the one or more assigned multi-purpose robots includes accessing a robot module system via at least one of a physics interface module and a control interface module. In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring one or more modules of a robot baseline system, the one or more modules being selected from a baseline module list including an energy storage and power distribution system, an electromechanical and electrofluidic system, a transportation system, and a vision and sensing system. In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring a 3D printing system to manufacture at least one hardware robot module.

[0086] In some embodiments, configuring the one or more assigned multipurpose robots is based on one or more characteristics of a target operating environment, in some of these embodiments, the target operating environment is one or more of land-based, sea-based, underwater, in-flight, underground, and sub-zero ambient temperatures.

[0087] In some embodiments, configuring one or more assigned multi-purpose robots includes configuring the energy storage and power distribution system to utilize two or more separate power sources based on one aspect of the task and the operating environment. In some of these embodiments, a first separate power source of the two or more separate power sources is a mobile power source of the multi-purpose robot, and a second separate power source of the two or more separate power sources is a fixed-location power source that provides power to the robot via a wireless power signal.

[0088] In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring a propulsion system of the robot to adaptively utilize one or more legs for locomotion. In some embodiments, configuring the one or more assigned multi-purpose robots includes providing the multi-purpose robot with one or more modules identified in a job execution plan. In some of these embodiments, the one or more modules are hardware modules. Additionally or alternatively, the one or more modules are software modules.

[0089] In some embodiments, configuring the one or more assigned multi-purpose robots includes providing the multi-purpose robot with one or more of an appendage, a sensor set, a chip set, and a motion adapter based on at least one task in a set of goal tasks for the robot identified in the job execution plan. In some embodiments, configuring the one or more assigned multi-purpose robots includes analyzing a job execution plan that defines a fleet of robots and configuring at least one multi-purpose robot in the fleet of robots. In some embodiments, configuring the one or more assigned multi-purpose robots includes providing local manager capabilities that enable the multi-purpose robot to control the one or more robots.

[0090] According to some embodiments of the present disclosure, a method for configuring multi-purpose robots of a robot fleet is disclosed. The method includes receiving a request for a robot fleet to perform a job. The method further includes defining a set of tasks to be performed in performing the job. The method further includes assigning a plurality of robots selected from the robot inventory to the set of tasks based on the set of tasks, a robot inventory data structure indicating a plurality of robots assignable to the robot fleet, and, for each robot, a set of baseline characteristics of the robot and a respective status of the robot, where the plurality of robots includes one or more assigned multi-purpose robots configurable for different tasks and different environments. The method further includes determining a respective configuration of each assigned multi-purpose robot based on the respective tasks and components assigned to the assigned multi-purpose robot. The method further includes configuring the one or more assigned multi-purpose robots based on the respective configurations. The method further includes deploying the robot fleet to perform work.

[0091] In some embodiments, the robot inventory includes a special-purpose robot. In some embodiments, allocating the plurality of robots selected from the robot inventory is further based on a job environment. In some embodiments, allocating the plurality of robots selected from the robot inventory is further based on a job budget. In some embodiments, allocating the plurality of robots selected from the robot inventory is further based on a timeline for completing the job. In some embodiments, the robot inventory includes a special-purpose robot, and allocating the plurality of robots selected from the robot inventory is further based on an available inventory of special-purpose robots. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a job environment. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a job budget. In some embodiments, determining a respective configuration for each assigned multi-purpose robot is further based on a job timeline for completing the job. In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring at least one robot system selected from a list of robot systems including a robot baseline system, a module system, a robot control system, and a robot security system. In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring one or more of a software robot module or a hardware robot module. In some embodiments, configuring one or more assigned multipurpose robot tasks includes accessing a robot module system via at least one of a physics interface module and a control interface module.In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring one or more modules of a robot baseline system, wherein the one or more modules are selected from a baseline module list including an energy storage and power distribution system, an electromechanical and electro-fluidic system, a transportation system, and a vision and sensing system. In some embodiments, configuring the one or more assigned multi-purpose robots is based on one or more characteristics of the target operating environment.

[0092] In some embodiments, configuring one or more assigned multi-purpose robots includes configuring the energy storage and power distribution system to utilize two or more separate power sources based on one aspect of the task and the operating environment. In some of these embodiments, a first separate power source of the two or more separate power sources is a mobile power source of the multi-purpose robot, and a second separate power source of the two or more separate power sources is a fixed-location power source that provides power to the robot via a wireless power signal.

[0093] In some embodiments, configuring the one or more assigned multi-purpose robots includes configuring a propulsion system of the robot to adaptively utilize one or more legs for locomotion. In some embodiments, configuring the one or more assigned multi-purpose robots includes providing the multi-purpose robot with one or more modules identified in a job execution plan. In some embodiments, configuring the one or more assigned multi-purpose robots includes providing the multi-purpose robot with one or more appendages, sensor sets, chip sets, and motion adapters based on at least one task of a set of goal tasks for the robot identified in the job execution plan. In some embodiments, configuring the one or more assigned multi-purpose robots includes analyzing a job execution plan defining a fleet of robots and configuring at least one multi-purpose robot in the fleet of robots. In some embodiments, configuring the one or more assigned multi-purpose robots includes providing local manager capabilities that enable the multi-purpose robot to control the one or more robots.

[0094] According to some embodiments of the present disclosure, a robotic fleet management platform is disclosed. The platform includes a computer-readable storage system that stores a fleet resource inventory representing a plurality of fleet resources that can be assigned to a robotic fleet, and a resource data store that maintains maintenance status data for each fleet resource, including maintenance history, predicted maintenance needs, and preventive maintenance schedules. A maintenance management library of fleet resource maintenance requirements that facilitates determining maintenance workflows, service actions, and service parts for at least one fleet resource among the plurality of fleet resources represented in the fleet resource inventory. The platform further includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively calculates predicted maintenance needs for the fleet resource based on predicted component wear and predicted component failures of one or more components of the at least one fleet resource, where the predicted component wear and predicted component failures of the one or more components are derived from machine learning-based analysis of maintenance status data in the fleet resource inventory. A set of one or more processors collectively monitors a health state of a fleet resource, the health state being determined from sensor data received from the fleet resource. The set of one or more processors collectively adapts a preventive maintenance schedule of the fleet resource by indicating a new preventive maintenance schedule for at least one item of maintenance of the fleet resource based on the predicted maintenance need, the health state, and the maintenance requirements of the fleet resource. The set of one or more processors collectively initiates a service action for at least one maintenance item for the fleet resource based on the fleet resource maintenance requirements and the new preventive maintenance schedule.

[0095] In some embodiments, the set of one or more processors further predicts maintenance needs for the fleet resource based on the digital twin-based simulation of the digital twin of the at least one fleet resource. In some embodiments, the at least one fleet resource is a robotic operating unit.

[0096] In some embodiments, the predictive maintenance intelligence service layer predicts at least one of expected component wear or expected component failure by applying a clustering algorithm to identify at least one failure pattern in the set of failure data. In some of these embodiments, the predictive maintenance intelligence service layer correlates the following patterns to create a pre-failure maintenance plan. In some of these embodiments, the predictive maintenance intelligence service layer adjusts a preventive maintenance plan for a robotic fleet resource based on correlated patterns of failures of similar types of robotic fleet resources. Additionally or alternatively, the predictive maintenance intelligence service layer predicts maintenance needs for a fleet resource based on a digital twin-based simulation of a digital twin of at least one fleet resource.

[0097] In some embodiments, adapting the preventive maintenance schedule includes interacting with a fleet configuration system by sharing maintenance knowledge of the fleet resource that affects the job. In some embodiments, triggering the service action includes configuring a set of 3D print requirements to facilitate field maintenance of the fleet resource. In some of these embodiments, the 3D print requirements are configured based on predicted maintenance activities of the fleet resource. In some embodiments, the new preventive maintenance schedule includes scheduled field maintenance of at least one fleet resource.

[0098] In some embodiments, the new preventive maintenance schedule includes scheduled repair depot-based maintenance of the at least one fleet resource. In some of these embodiments, the at least one fleet resource is a smart container operation unit. Additionally or alternatively, the at least one fleet resource is a robotic operation unit. In some embodiments, the platform further includes a mobile maintenance vehicle. In some embodiments, the platform further includes a repair depot. In some embodiments, the platform further includes a third-party maintenance service provider. In some embodiments, adapting the preventive maintenance schedule includes adapting a maintenance schedule for the at least one inactive fleet resource based on an assessment of the maintenance needs of the at least one inactive fleet resource.

[0099] In some embodiments, the set of one or more processors further monitors the status of the at least one fleet resource by monitoring communications of the at least one fleet resource for indications of a need for maintenance. In some of these embodiments, the at least one fleet resource is a robotic operating unit. Additionally or alternatively, the indication of a need for maintenance includes a lack of a heartbeat signal from the fleet resource to a health monitor resource. Additionally or alternatively, the need for maintenance of the at least one fleet resource includes a potential service condition. In some of these embodiments, the potential service condition includes one or more of a power derating, exposure to excessive ambient conditions, or a ground fault.

[0100] In some embodiments, the set of one or more processors further deploys a software-based maintenance monitoring probe in the operating software or monitoring software of the at least one fleet resource. In some of these embodiments, the probe monitors information in a data store of the at least one fleet resource that stores operating condition information. Additionally or alternatively, the probe activates a self-test operating mode of the at least one fleet resource. Furthermore, the probe collects data indicative of a need for maintenance of the at least one fleet resource.

[0101] In some embodiments, the set of one or more processors further deploy one or more maintenance fleet resources within the one or more smart containers. In some embodiments, adapting the preventive maintenance schedule includes deploying one or more maintenance fleet resources within the at least one smart container. In some embodiments, triggering a service action includes automating a maintenance activity of the at least one fleet resource based on operator input regarding a condition of the at least one fleet resource. In some embodiments, triggering a service action includes automating a maintenance activity of the at least one fleet resource. In some embodiments, adapting the preventive maintenance schedule includes adapting a maintenance schedule of the at least one fleet resource based on artificial intelligence-based predictions of maintenance instances.

[0102] In some embodiments, adapting the preventive maintenance schedule includes adapting the maintenance schedule of the at least one fleet resource based on a machine learning system that identifies new opportunities for scheduling and performing maintenance. In some of these embodiments, the machine learning system analyzes performance data of at least one other robot that has been maintained for operation in the particular conditions. In some of these embodiments, a cooling system of the other robot has been maintained prior to operation in the high temperature environment, and the performance data reflects operation of the at least one other robot in the particular conditions.

[0103] In some embodiments, adapting the preventive maintenance schedule includes adapting the maintenance schedule for the at least one fleet resource based on one or more of maintenance rules established for the team, maintenance rules established for the fleet, maintenance rules established by the shipper, and maintenance rules determined by a regulatory body. In some embodiments, adapting the preventive maintenance schedule includes determining one or more of a maintenance workflow, a service action, or a required part for maintaining the at least one fleet resource based on one or more of a relational table, a data set, a database, or a maintenance management library. In some embodiments, triggering the service action includes assigning the maintenance activity to a fleet resource selected from a list of fleet resources including a maintenance smart container, a human technician, and a third-party service provider. In some embodiments, triggering the service action includes deploying a maintenance service to perform maintenance of the at least one fleet resource via a set of self-maintenance protocols for at least one of self-cleaning and calibration of end effector operation. In some embodiments, triggering the service action includes interacting with a fleet configuration system in response to an indication of a compromised capability of the at least one robot, the interaction resulting in a change in allocation of the at least one fleet resource based on the compromised capability. In some embodiments, triggering the service action is based on interacting with a digital twin of the at least one fleet resource operated by a fleet intelligence service that predicts a need for maintenance of the at least one fleet resource. In some embodiments, triggering the service action includes coordinating maintenance activities with job scheduling to ensure preventable disruptions due to lack of maintenance are prevented.

[0104] According to some embodiments of the present disclosure, a robotic fleet resource provisioning system is disclosed. The system includes a computer-readable storage system that stores: a fleet resource data store that maintains a fleet resource inventory that indicates a plurality of fleet resources that can be provided as a set of fleet resources and, for each respective fleet resource, indicates a set of resource characteristics, configuration requirements, and a respective status of the resource; and a set of resource provisioning rules that are accessible to an intelligence layer to ensure that the provided resources comply with the provisioning rules. The system further includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively receives a request for a robotic fleet to perform a job. The set of one or more processors collectively determine a job definition data structure based on the request, where the job definition data structure defines a set of tasks to be performed in performing the job. The set of one or more processors collectively determine a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory, where the robotic fleet configuration data structure assigns a plurality of resources selected from the fleet resource inventory to the set of tasks defined in the job definition data structure. The set of one or more processors collectively determine a respective provisioning configuration for each fleet resource based on a respective task to which the fleet resource is assigned, a set of characteristics of the fleet resource, a configuration requirement of the fleet resource, and a respective status of the fleet resource. The set of one or more processors collectively provision each fleet resource based on the respective provisioning configuration and provisioning rules. The set of one or more processors collectively deploy the robot fleet to perform a job.

[0105] In some embodiments, the status of each of the resources includes the general availability of the resource. In some embodiments, determining the robot fleet configuration data structure is further based on the job's environment. In some embodiments, determining the robot fleet configuration data structure is further based on the job's budget. In some embodiments, determining the robot fleet configuration data structure is further based on a timeline for completing the job. In some embodiments, the fleet resource inventory includes one or more types of robots, and determining the robot fleet configuration data structure is further based on an available inventory of the one or more types of robots. In some embodiments, determining the provision configuration for each fleet resource is further based on the job's environment. In some embodiments, determining the provision configuration for each fleet resource is further based on the job's budget. In some embodiments, determining the provision configuration for each assigned fleet resource is further based on a timeline for completing the job. In some embodiments, the fleet resource inventory includes computing resources selected from a list of computing resources including on-robot computing resources, robotic operating unit-local fleet control computing resources, cloud-based computing resources, computing modules, or computing chips.

[0106] In some embodiments, providing each fleet resource includes providing one or more of a software robotic module or a hardware robotic module, and in some of these embodiments, the hardware robotic module is an interchangeable module.

[0107] In some embodiments, the fleet resource inventory includes a plurality of digital resources. In some of these embodiments, providing each one of the plurality of digital resources includes one or more of a software update push, a resource access credential, or a fleet resource data storage configuration, allocation, or utilization. In some embodiments, providing each fleet resource includes providing a consumable resource sourced from at least one of a specialized supply chain, a job requester resource supply, a fleet-specific stockpile, a job-specific stockpile, or a fleet team-specific stockpile.

[0108] In some embodiments, the provision of each fleet resource is based on one or more characteristics of a target operating environment, which in some of these embodiments is one or more of land, sea, water, in-flight, underground, and sub-freezing ambient temperatures.

[0109] In some embodiments, providing each fleet resource includes 3D printing the respective resource for provision. In some embodiments, providing each fleet resource is based on terms of a smart contract that constrains the provision of the fleet resource. In some embodiments, the fleet resource inventory includes platform resources, and providing each fleet resource includes providing at least one platform resource selected from a list of platform resources including a computing resource, a fleet configuration system, a platform intelligence layer, a platform data processing system, and a fleet security system. In some of these embodiments, determining the robot fleet configuration data structure is further based on a negotiated fee for providing the platform resource. Additionally or alternatively, determining the robot fleet configuration data structure includes a negotiation workflow for accepting a job request.

[0110] In some embodiments, providing the respective fleet resources includes providing one or more fleet resources identified in the job execution plan. In some embodiments, providing the respective fleet resources includes providing one or more of an appendage, a sensor set, a chipset, and a motion adapter to the robot based on at least one task in a set of target tasks for the robot identified in the job execution plan. In some embodiments, providing the respective fleet resources includes analyzing the job execution plan to define resources for the fleet of robots to perform the at least one task. In some embodiments, a set of one or more processors executes a set of computer-readable instructions in cooperation with at least one of a fleet configuration system, a fleet resource scheduling system, a fleet security system, and a fleet utilization system.

[0111] According to some embodiments of the present disclosure, a method for provisioning robotic fleet resources is disclosed. The method includes receiving a request for a robotic fleet to perform a job. The method further includes determining a job definition data structure based on the request, where the job definition data structure defines a set of tasks to be performed in executing the job. The method further includes determining a robotic fleet configuration data structure corresponding to the job based on the set of tasks, a fleet resource inventory indicating a plurality of fleet resources, and, for each respective fleet resource, a set of resource characteristics, a configuration requirement for the resource, and a respective status of the resource. The robotic fleet configuration data structure assigns a plurality of resources selected from the fleet resource inventory to the set of tasks defined in the job definition data structure. The method further includes determining a respective provisioning configuration for each fleet resource based on the respective tasks to which the fleet resource is assigned, the set of fleet resource characteristics, the configuration requirement for the fleet resource, and the respective status of the fleet resource. The method further includes provisioning each fleet resource based on the respective provisioning configuration and a set of resource provisioning rules accessible to an intelligence layer to ensure that the provisioned resources comply with the provisioning rules. The method further includes deploying a fleet of robots to perform the job.

[0112] In some embodiments, the status of each of the resources includes the general availability of the resource. In some embodiments, determining the robot fleet configuration data structure is further based on the job's environment. In some embodiments, determining the robot fleet configuration data structure is further based on the job's budget. In some embodiments, determining the robot fleet configuration data structure is further based on a timeline for completing the job. In some embodiments, the fleet resource inventory includes one or more types of robots, and determining the robot fleet configuration data structure is further based on an available inventory of the one or more types of robots. In some embodiments, determining the provision configuration for each fleet resource is further based on the job's environment. In some embodiments, determining the provision configuration for each fleet resource is further based on the job's budget. In some embodiments, determining the provision configuration for each assigned fleet resource is further based on a timeline for completing the job. In some embodiments, the fleet resource inventory includes computing resources selected from a list of computing resources including on-robot computing resources, robotic operating unit-local fleet control computing resources, cloud-based computing resources, computing modules, or computing chips.

[0113] In some embodiments, providing each fleet resource includes providing one or more of a software robotic module or a hardware robotic module, and in some of these embodiments, the hardware robotic module is an interchangeable module.

[0114] In some embodiments, the fleet resource inventory includes a plurality of digital resources. In some of these embodiments, providing each one of the plurality of digital resources includes one or more of a software update push, a resource access credential, or a fleet resource data storage configuration, allocation, or utilization.

[0115] In some embodiments, providing each fleet resource includes providing a consumable resource sourced from at least one of a specialized supply chain, a job requester resource supply, a fleet-specific stockpile, a job-specific stockpile, or a fleet team-specific stockpile. In some embodiments, providing each fleet resource is based on one or more characteristics of a target operating environment. In some of these embodiments, the target operating environment is one or more of land, sea, water, in-flight, underground, and sub-freezing ambient temperatures. In some embodiments, providing each fleet resource includes 3D printing the respective resource for provision. In some embodiments, providing each fleet resource is based on terms of a smart contract that constrains the provision of the fleet resource.

[0116] In some embodiments, the fleet resource inventory includes platform resources, and providing each fleet resource includes providing at least one platform resource selected from a list of platform resources including a computing resource, a fleet configuration system, a platform intelligence layer, a platform data processing system, and a fleet security system. In some of these embodiments, determining the robot fleet configuration data structure is further based on a negotiated fee for providing the platform resource. In some of these embodiments, determining the robot fleet configuration data structure includes a negotiation workflow for accepting job requests.

[0117] In some embodiments, providing the respective fleet resources includes providing one or more fleet resources identified in the job execution plan. In some embodiments, providing the respective fleet resources includes providing one or more of an appendage, a sensor set, a chipset, and a motion adapter to the robot based on at least one task in a set of target tasks for the robot identified in the job execution plan. In some embodiments, providing the respective fleet resources includes analyzing the job execution plan to define resources for the fleet of robots to perform the at least one task. In some embodiments, the method further includes executing in coordination with at least one of a fleet configuration system, a fleet resource scheduling system, a fleet security system, and a fleet utilization system.

[0118] According to some embodiments of the present disclosure, a robot fleet platform for configuring a robot fleet with additive manufacturing capabilities is disclosed. The platform includes a computer-readable storage system that stores: a fleet resource data store that maintains a fleet resource inventory indicating a plurality of additive manufacturing systems that can be provided by a set of fleet resources, where, for each additive manufacturing system, a set of 3D printing requirements, print instructions defining the configuration of an on-demand production system for 3D printing, and the additive manufacturing system's status; and a set of additive manufacturing system provisioning rules accessible to an intelligence layer for ensuring that the provided additive manufacturing systems comply with the provisioning rules. The platform further includes a set of one or more processors that execute the set of computer-readable instructions. The set of one or more processors collectively receives a request for the robot fleet to execute a job. The set of one or more processors collectively determines a job definition data structure based on the request, where the job definition data structure defines a set of tasks to be performed in executing the job. A set of one or more processors collectively determine a robot fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory, and the robot fleet configuration data structure assigns one or more additive manufacturing systems selected from the fleet resource inventory to one or more of the set of tasks defined in the job definition data structure. The set of one or more processors collectively determine respective provision configurations for each additive manufacturing system based on each task to which the additive manufacturing system is assigned, a set of 3D printing requirements, print instructions, and each status of the additive manufacturing system. The set of one or more processors collectively determine respective provision configurations for each additive manufacturing system. The additive manufacturing system based on the respective provision configurations and provision rules. The set of one or more processors collectively arrange the robot fleet to execute the job based on the robot fleet configuration data structure.

[0119] In some embodiments, providing each additive manufacturing system includes providing a 3D printing capable robot. In some embodiments, each provisioned configuration of each additive manufacturing system includes a set of 3D printing instructions for at least one of a job-specific end effector or adapter based on a task context to which the additive manufacturing system is assigned. In some embodiments, the robot fleet configuration data structure assigns control of at least one transportable 3D printing additive manufacturing system to at least one robotic operational unit.

[0120] In some embodiments, determining the robotic fleet configuration data structure is further based on 3D printing resource availability and job site locality. In some of these embodiments, at least one of the 3D printing resource availability or job site locality is identified by a logistics system of the platform. In some embodiments, determining the robotic fleet configuration data structure includes allocating at least one additive manufacturing system listed in the fleet resource inventory based on proximity to the job site of the requested job.

[0121] In some embodiments, determining each provision configuration for each additive manufacturing system includes using an artificial intelligence system to automate the design for 3D printing of one or more robotic appendages. In some of these embodiments, the artificial intelligence system automates the design for 3D printing based on contextual task recognition. Additionally or alternatively, the artificial intelligence system automates the design for 3D printing based on automated shape recognition capabilities. Additionally or alternatively, providing each additive manufacturing system includes providing 3D printing control capabilities to manufacture end effectors based on visual and sensed analysis of an object for an operation in which the end effector is to be 3D printed.

[0122] In some embodiments, deploying the robot fleet includes using a fleet configuration scheduling resource of the platform to assign each additive manufacturing system to perform a job. In some embodiments, deploying the robot fleet includes deploying 3D printing robots in smart containers for remote, on-demand additive manufacturing. In some embodiments, determining each delivery configuration for each additive manufacturing system is further based on one or more keywords in a job definition data structure indicating an operational state of the each additive manufacturing system. In some embodiments, deploying the robot fleet includes deploying a set of autonomous 3D printing additive manufacturing systems to points of service work indicated in the job definition data structure. In some embodiments, determining each delivery configuration for each additive manufacturing system includes configuring the 3D printing systems to receive tokenized instances of a set of 3D printing instructions associated with a corresponding token on the distributed ledger. In some embodiments, deploying the robot fleet includes deploying each additive manufacturing system as a 3D printing resource shared among multiple tasks.

[0123] According to some embodiments of the present disclosure, a method for configuring a robot fleet with additive manufacturing capabilities is disclosed. The method includes receiving a request for the robot fleet to execute a job. The method further includes determining a job definition data structure based on the request, the job definition data structure defining a set of tasks to be performed in executing the job. The method further includes determining a robot fleet configuration data structure corresponding to the job based on the set of tasks, a fleet resource inventory indicating a plurality of additive manufacturing systems that may comprise the set of fleet resources, and, for each additive manufacturing system, a set of 3D printing requirements, print instructions defining the configuration of an on-demand production system for 3D printing, and a status of the additive manufacturing system, wherein the robot fleet configuration data structure assigns one or more additive manufacturing systems selected from the fleet resource inventory to one or more of the set of tasks defined in the job definition data structure. The method further includes determining a respective serving configuration of each additive manufacturing system based on each task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the print instructions, and the respective status of the additive manufacturing system. The method further includes provisioning each additive manufacturing system based on the respective provisioning configuration and a set of additive manufacturing system provisioning rules accessible to the intelligence layer to ensure that the provisioned additive manufacturing system complies with the provisioning rules. The method further includes deploying a robot fleet to perform a job based on the robot fleet configuration data structure.

[0124] In some embodiments, provisioning each additive manufacturing system includes provisioning a 3D printing capable robot. In some embodiments, each provisioning configuration of each additive manufacturing system includes a set of 3D printing instructions for at least one of a job-specific end effector or adapter based on the context of a task to which the additive manufacturing system is assigned. In some embodiments, the robot fleet configuration data structure assigns control of at least one transportable 3D printing additive manufacturing system to at least one robotic operating unit.

[0125] In some embodiments, determining the robotic fleet configuration data structure is further based on 3D printing resource availability and job site locality. In some of these embodiments, at least one of the 3D printing resource availability or job site locality is identified by a logistics system of the platform. In some embodiments, determining the robotic fleet configuration data structure includes allocating at least one additive manufacturing system listed in the fleet resource inventory based on proximity to the job site of the requested job.

[0126] In some embodiments, determining a respective provisioning configuration for each additive manufacturing system includes using an artificial intelligence system to: automate the design for 3D printing of one or more robot accessories. In some of these embodiments, the artificial intelligence system automates the design for 3D printing based on contextual task recognition. Additionally or alternatively, the artificial intelligence system automates the design for 3D printing based on automated shape recognition capabilities. Additionally or alternatively, providing each additive manufacturing system includes providing 3D printing control capabilities to manufacture an end effector based on visual and sensed analysis of an object for an operation in which the end effector is 3D printed.

[0127] In some embodiments, deploying the robot fleet includes using a fleet configuration scheduling resource of the platform to assign each additive manufacturing system to perform a job. In some embodiments, deploying the robot fleet includes deploying 3D printing robots in smart containers for remote, on-demand additive manufacturing. In some embodiments, determining each delivery configuration for each additive manufacturing system is further based on one or more keywords in a job definition data structure indicating an operational state of each additive manufacturing system. In some embodiments, deploying the robot fleet includes deploying a set of autonomous 3D printing additive manufacturing systems to points of service work indicated in the job definition data structure. In some embodiments, determining each delivery configuration for each additive manufacturing system includes configuring the 3D printing systems to receive tokenized instances of a set of 3D printing instructions associated with a corresponding token on the distributed ledger. In some embodiments, deploying the robot fleet includes deploying each additive manufacturing system as a 3D printing resource shared among multiple tasks.

[0128] In some embodiments, provisioning each additive manufacturing system includes interacting with at least one of a fleet operating system, a fleet configuration system, a fleet resource scheduling system, and a fleet utilization system. In some of these embodiments, interacting includes ensuring that provisioning rules are followed. In some embodiments, the provisioning rules are defined in a governance standard library, and the intelligence service ensures that the provisioned resources comply with the provisioning rules.

[0129] According to some embodiments of the present disclosure, a dynamic vision system for robotic fleet management is disclosed. The system includes an optical assembly including a lens containing a liquid, the lens being deformable to generate a variable focus of the lens, and the optical assembly is configured to capture optical data. The system further includes a robotic fleet management platform having a control system configured to adjust one or more optical parameters, the one or more optical parameters changing the variable focus of the lens while the optical assembly captures current optical data related to the robotic fleet. The system further includes a processing system configured to train a machine learning model to recognize objects related to the robotic fleet using training data generated from the optical data captured by the optical assembly, the optical data including the current optical data related to the robotic fleet.

[0130] In some embodiments, the optical data captured by the optical assembly includes out-of-focus optical data regarding an object being optically captured by the optical assembly. In some embodiments, the recognition of an object regarding the robotic fleet is compared to a stored fleet resource configuration comprised of a plurality of objects. In some of these embodiments, the comparison of the recognized object to the stored fleet resource configuration is quantified as a numerical score, the numerical score representing a degree of match between the recognized object and the location of that object type in the stored fleet resource configuration. In some of these embodiments, the numerical score is compared to a stored numerical score threshold, the numerical score threshold representing a minimum degree of match between the recognized object and the location of that object type in the stored fleet resource configuration. In some of these embodiments, the robotic fleet management platform generates an alert when it detects that the numerical score does not match or exceed the stored numerical score threshold.

[0131] In some embodiments, the robotic fleet management platform pauses the robotic activity of at least one robotic device upon detecting that the numerical score does not meet or exceed the stored numerical score threshold. In some embodiments, the optical parameter deforms the lens from an original state by applying a current to the lens. In some embodiments, the optical parameter adjusts the variable focus of the lens at a predetermined frequency. In some embodiments, the optical parameter adjusts the variable focus of the lens from a first focus state to a second focus state different from the first focus state, the training data includes optical data captured at the first focus state, and the training data incorporates feedback data such that the optical data captured at the first focus state and the second focus state.

[0132] According to some embodiments of the present disclosure, an information technology system for a distributed manufacturing network is disclosed. The system includes an additive manufacturing management platform configured to manage process workflows of a set of distributed manufacturing network entities associated with the distributed manufacturing network, one of the process workflows including a design phase, a modeling phase, a printing phase, and a supply chain phase, the modeling phase including a digital twin modeling system defined at least in part by at least one of product instructions or control tower instructions for encoding a set of digital twins representing products for use by the additive manufacturing management platform. The system further includes an artificial intelligence system executable by a data processing system in communication with the additive manufacturing management platform, the artificial intelligence system being trained to generate process parameters for the process workflow managed by the additive manufacturing management platform using data collected from the distributed manufacturing network entities. The system further includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the distributed manufacturing network entities.

[0133] In some embodiments, the set of distributed manufacturing network entities includes a first additive manufacturing unit configured to perform a first additive manufacturing process and a second additive manufacturing unit configured to perform a second additive manufacturing process, where the first additive manufacturing process is different from the second additive manufacturing process.

[0134] In some embodiments, the training data includes: (i) results, (ii) collected data, and (iii) prior / historical process parameters. In some embodiments, the additive manufacturing process is a hybrid task requiring at least two different types of additive manufacturing units. In some embodiments, the additive manufacturing management platform is cloud-based. In some embodiments, the artificial intelligence system is distributed across multiple distributed manufacturing network entities. In some embodiments, a digital twin representing a product is used by the additive manufacturing management platform to manufacture physical replicas of the digitally represented product. In some embodiments, the artificial intelligence system includes an adaptive intelligence system configured to communicate with multiple sensors and receive current sensor data from the multiple sensors for use in encoding the set of digital twins. In some embodiments, the artificial intelligence system is distributed across multiple distributed manufacturing network entities from a set of distributed manufacturing network entities. In some embodiments, the representation of the product is a simulated future state state of the product.

[0135] The autonomous futures contract orchestration platform includes a set of one or more processors programmed with a set of non-transitory computer-readable instructions that collectively perform the following: receiving product-related indications from a data source, the indications being associated with at least one of entities that purchase or sell the product; forecasting a baseline cost for at least one of purchasing or selling the product at a future time based on the indications; retrieving future costs, at a current time, of futures contracts for obligations to at least one of purchasing or selling the product for at least one of delivery or performance of the product at a future time; executing a smart contract for the futures contract based on the baseline costs and the future costs; and orchestrating at least one of delivery or performance of the product at a future time.

[0136] In other features, the autonomous futures contract orchestration platform includes a risk data structure indicating an amount of risk an entity is willing to accept with respect to a baseline cost and a futures cost. The computer-readable instructions collectively execute the smart contract based on the risk data structure to at least one of manage or mitigate the risk. In other features, the autonomous futures contract orchestration platform includes a robotic process automation system for demand-side planning for orchestrating smart futures contracts. In other features, the autonomous futures contract orchestration platform includes a robotic agent configured to de-risk with respect to the futures contracts and the smart contract. In other features, the autonomous futures contract orchestration platform includes a system that performs circular economy optimization based on futures prices of a commodity. In other features, the computer-readable instructions collectively initialize a robotic process automation system trained to execute the smart contract and execute the smart contract using the robotic process automation system. In other features, the indication is at least one of an occurrence of an event, a physical condition of an item, or an increase in potential demand.

[0137] In such cases, a "futures contract orchestration platform" includes a set of one or more processors programmed with a set of non-transitory computer-readable instructions that collectively perform the following: look up future costs at a present time for futures contracts relating to an obligation to purchase or sell a product for at least one of delivery or performance of the product to an entity at a future time; perform a forecast of a baseline cost to the entity for at least one of purchase or sale of the product at a future time; execute the smart contract of the futures contract based on the baseline cost and the future cost; and orchestrate at least one of delivery or performance of the product to the entity at a future time.

[0138] The method includes receiving product-related instructions from a data source, the instructions being associated with an entity that purchases or sells the product. The method includes forecasting a baseline cost for the purchase or sale of the product at a future time based on the instructions. The method includes retrieving a futures cost, as of a present time, of a futures contract for an obligation to purchase or sell the product for at least one of delivery or performance of the product at a future time. The method includes executing a smart contract for the futures contract based on the baseline cost and the futures cost.

[0139] In other features, the computerized method includes retrieving a risk data structure indicating an amount of risk an entity is willing to accept relative to baseline costs and futures costs, and executing a smart contract based on the risk data structure to at least one of manage or mitigate the risk. In other features, the computerized method includes demand side planning using a robotic process automation system and orchestrating smart futures contracts based on the demand side planning. In other features, the computerized method includes de-risking futures contracts and smart contracts using a robotic agent. In other features, the computerized method includes executing a system for performing circular economy optimization based on futures pricing of a commodity. In other features, the computerized method includes initializing a robotic process automation system trained to execute a smart contract and executing the smart contract using the robotic process automation system. In other features, retrieving instructions includes retrieving at least one of an occurrence of an event, a physical condition of an item, or an increase in potential demand.

[0140] The autonomous futures contract orchestration platform includes a set of one or more processors programmed with a set of non-transitory computer-readable instructions that collectively execute: receiving, from a data source, commodity-related indications associated with at least one of an entity buying or selling the commodity; forecasting a baseline cost for at least one of buying or selling the commodity at a future time based on the indications; generating a risk threshold based on a predefined risk tolerance of the entity representing a difference between the baseline cost and a futures cost; and executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold.

[0141] In other features, the set of one or more processors are further programmed collectively to generate a risk threshold based on at least one of hedging against an adverse event or providing an improved outcome following an adverse event. In other features, the set of one or more processors are further programmed collectively to generate a risk threshold based on at least one of a supply shortage, a supply chain disruption, a change in demand, a change in input price, or a change in market price as an adverse event. In other features, the set of one or more processors are further programmed collectively to forecast a baseline cost based on providing operational efficiencies. In other features, the set of one or more processors are further programmed collectively to forecast a baseline cost based on at least one of an item availability guarantee based on a plan or an item availability guarantee based on an availability forecast as an operational efficiencies.

[0142] In other features, the set of one or more processors are further programmed collectively to execute the smart contract based on improving revenue. In other features, the set of one or more processors are further programmed collectively to execute the smart contract based on obtaining an input at a more favorable price than a baseline cost would indicate. In other features, the set of one or more processors are further programmed collectively to execute the smart contract that interacts with a futures market associated with the futures contract. In other features, the set of one or more processors are further programmed collectively to execute the smart contract involving at least one of a futures or option, including at least one of a commodity, a stock, a currency, or an energy associated with the futures contract.

[0143] The method includes receiving, from a data source, instructions related to a set of items offered by the value chain network or by at least one in the value chain network. The method includes predicting a baseline cost associated with the set of items at a future time point based on the indication. The method includes retrieving, at a current time point, a future cost of a futures contract associated with the set of items. The method includes generating a risk threshold based on a predefined risk tolerance of an entity in the value chain network, the risk threshold indicating a difference between the baseline cost and the future cost. The method includes executing a smart contract for the futures contract based on the baseline cost, the futures cost, and the risk threshold.

[0144] In other features, generating the risk threshold includes generating the risk threshold based on at least one of hedging or providing an improved outcome after an adverse contingency. In other features, generating the risk threshold includes generating the risk threshold based on at least one of a supply shortage, a supply chain disruption, a change in demand, a change in input price, or a change in market price as the adverse contingency. Projecting the baseline cost includes projecting the baseline cost based on a provision of operational efficiencies. In other features, projecting the baseline cost includes projecting the baseline cost based on at least one of ensuring item availability based on a plan or ensuring item availability based on an availability forecast as the operational efficiencies.

[0145] In other features, executing the smart contract includes executing the smart contract based on improving returns. In other features, executing the smart contract includes executing the smart contract based on obtaining inputs at prices more favorable than baseline costs. In other features, executing the smart contract includes executing a smart contract that interacts with a futures market associated with a futures contract. In other features, executing the smart contract includes executing a smart contract involving at least one of futures or options, including at least one of a commodity, stock, currency, or energy associated with the futures contract.

[0146] A system for managing future costs associated with a product includes a future requirements system programmed to estimate the amount of resources required to manufacture, distribute, and sell the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at a future point in time. The system includes a smart contract system programmed to autonomously configure and execute smart futures contracts based on the amount of resources required and the changes in costs to manage future costs associated with the product.

[0147] In other features, the smart contract system is further programmed to execute the smart futures contract based on at least one of hedging or providing an improved outcome following an adverse contingency. In other features, the adverse contingency system is further configured to estimate a probability of at least one of a supply shortage, a supply chain disruption, a change in demand, a change in input price, or a change in market price as the adverse contingency.

[0148] In other features, the adverse contingency system is further configured to estimate a probability of at least one of macroeconomic factors, geopolitical disruptions, weather or climate disruptions, epidemics, pandemics, or counterparty risk as the adverse contingency. In other features, the smart contract system is programmed with a robotic agent that sets the terms of the smart futures contract. In other features, the smart contract system is programmed to set a price, delivery time, and delivery location required to provide a predetermined inventory of the item in response to the adverse contingency. In other features, the smart contract system is programmed to set at least one of parts, components, fuel, or materials required to provide the predetermined inventory of the item as a set of inputs to the robotic agent. In other features, the smart contract system is programmed to train the robotic agent with a training set of interactions of a set of expert procurement experts with the set of inputs.

[0149] In another feature, the smart contract system is programmed to train the robotic agent using at least one of a demand forecast, an inventory forecast, a demand elasticity curve, a forecast of competitive behavior, and a supply chain forecast as the input set of demand planning inputs. In another feature, the smart contract system is programmed to train the robotic agent using interactions within an enterprise demand planning software suite as the set of inputs. In another feature, the smart contract system is programmed to train the robotic agent to interact with a set of demand models that at least one of: forecast demand factors, forecast supply factors, forecast pricing factors, forecast an expected balance between supply and demand, generate an estimate of adequate inventory, generate a recommendation for supply, or generate a recommendation for allocation. In another feature, the smart contract system is further programmed to configure the smart contract to automatically execute to obtain a commitment for supply in response to the discovery of a predefined market condition associated with an adverse contingency.

[0150] A computerized method for managing future costs associated with a product includes estimating the amount of resources required to manufacture, distribute, and sell the product at a future time. The method includes identifying adverse contingencies. The method includes calculating the change in costs associated with obtaining the amount of resources at the future time. The method includes autonomously configuring and executing smart futures contracts based on the amount of resources required and the change in costs to manage the future costs associated with the product.

[0151] In other features, executing the smart contract includes executing a smart futures contract based on at least one of hedging or providing an improved outcome following an adverse contingency. In other features, the computerized method includes estimating a probability of at least one of a supply shortage, a supply chain disruption, a change in demand, a change in input prices, or a change in market prices as the adverse contingency. In other features, the computerized method includes estimating a probability of at least one of a macroeconomic factor, a geopolitical upheaval, a weather or climate disruption, an epidemic, a pandemic, or a counterparty risk as the adverse contingency.

[0152] In other features, the computerized method includes configuring terms of a smart futures contract with the robotic agent. In other features, the computerized method includes configuring at least one of parts, components, fuel, or materials needed to provide the predetermined inventory of items as a set of inputs to the robotic agent. In other features, the computerized method includes training the robotic agent with a training set of expert procurement expert interactions with the set of inputs. In other features, the computerized method includes training the robotic agent to interact with a set of demand models that perform at least one of forecasting demand factors, forecasting supply factors, forecasting pricing factors, and forecasting balance between supply and demand, generating an estimate of adequate inventory, generating a recommendation for supply, or generating a recommendation for distribution.

[0153] The raw material system includes a product manufacturing demand estimating system programmed to calculate a projected demand for the product at a future time. The system includes an environmental detection system configured to identify at least one of an environmental condition or an environmental event. The system includes an environmental detection system programmed to identify at least one of an environmental condition or an environmental event. The system includes a raw material requirements system programmed to calculate a quantity of raw material needed to manufacture the product at a future time based on the projected demand and at least one of the environmental condition or the environmental event. The system includes a raw material procurement system programmed to autonomously configure futures contracts to procure at least a portion of the required raw material in response to the calculation of the required raw material exceeding the raw material availability forecast.

[0154] In other features, the raw material production system is further programmed to estimate a probability that raw material availability will decrease based on an increase in demand exceeding an increase in production. In other features, the raw material requesting system is further programmed to include a demand aggregation service configured to monitor demand response across the plurality of systems. In other features, the demand aggregation service is further configured to monitor the demand response as a change in at least one of supply, price changes, customization, pricing, and advertising. In other features, the raw material system includes a risk tolerance system configured to retrieve a predetermined risk tolerance of an entity sourcing the raw materials. The raw material procurement system is further programmed to autonomously configure a futures contract based at least in part on the predetermined risk tolerance. In other features, the raw material procurement system is further configured to execute a smart contract for the futures contract. In other features, the raw material system includes a digital wallet coupled with the raw material procurement system to enable payments related to the smart contract. In other features, the raw material procurement system is further configured to include a robotic process automation (RPA) service to facilitate automation of the generation and validation of the smart contract. In other features, the RPA service is configured to automate a process based on observation of human interactions with hardware and software elements.

[0155] In other features, the raw material procurement system is further configured to configure the smart contract to interact with a distribution system to secure at least one of delivery, storage, or handling of the raw materials through the distribution system. In other features, the raw material procurement system is further configured to configure the smart contract to interact with a logistics reservation futures system to secure future logistics services. In other features, the raw material procurement system is further configured to configure the smart contract to secure at least one of a port docking reservation, a shipping container reservation, a trucking reservation, a warehouse space rental, or a canal passage rental as the future logistics services. In other features, the raw materials include at least one of copper, steel, iron, or lithium.

[0156] The method includes identifying at least one of an environmental condition or an environmental event. The method includes identifying at least one of an environmental condition or an environmental event. The method includes estimating availability of raw materials at a future time based on expected demand and at least one of the environmental conditions or the environmental event. The method includes calculating an amount of raw materials needed to produce the product. The method includes autonomously structuring a futures contract to procure at least a portion of the required amount of raw materials in response to the calculation of the required amount of raw materials exceeding the estimated raw material availability.

[0157] In other features, the computerized method includes estimating a probability of a decrease in raw material availability based on an increase in demand exceeding an increase in production. In other features, the computerized method includes monitoring a demand response across multiple systems. In other features, monitoring the demand response further includes monitoring the demand response as a change in at least one of supply, price changes, customization, pricing, or advertising. In other features, the computerized method includes retrieving a predetermined risk tolerance of an entity sourcing the raw materials. Autonomously configuring the futures contract is based at least in part on the predetermined risk tolerance. In other features, the computerized method includes executing a smart contract for the futures contract. In other features, the computerized method includes engaging a digital wallet to enable payments related to the smart contract.

[0158] The system for product replacement includes a product logistics system for a product in a product state. The system includes an exposure data collection system configured to collect exposure data indicative of at least one of an event or an environmental condition that may affect the product state of the product. The system includes a replacement decision system programmed to calculate a probability of the need to replace the product based on at least one of the event or the environmental condition. The system includes a replacement procurement system programmed to autonomously configure an option-based futures contract for the product replacement based on the probability of the need for the product replacement.

[0159] In another feature, the system includes a smart contract system programmed to autonomously configure a smart contract to secure replacement of a product based on an option-based futures contract. In another feature, the smart contract system configures the smart contract to have an option term based on an estimate of the time until an actual determination of the need for product replacement based on a physical inspection can be performed. In another feature, the smart contract system configures the smart contract to have an option term further based on a probability of catastrophic loss indicated by the probability of the need to replace the product. In another feature, the system includes a replacement alternative system programmed to configure an alternative smart contract to offer a product replacement alternative to at least one of a purchaser of the product, an owner of the product, or an insurance company holding a security interest in the product. In another feature, the replacement alternative system is programmed to configure an alternative smart contract that offers a refund of the purchase price of the product. In another feature, the replacement alternative system is programmed to configure an alternative smart contract that offers an alternative good or an alternative service. In another feature, the replacement alternative system is programmed to configure an alternative smart contract that offers an incentive to accept a delay in delivery of the product.

[0160] In other features, the system includes a future price renegotiation system programmed to renegotiate the set of future prices based on current market conditions and the exposure data. In other features, the future price renegotiation system is further programmed to renegotiate the set of future prices in response to exposure data indicating a potential widespread disruption to the supply chain of goods or services related to the product. In other features, the system includes an artificial intelligence (AI) system trained on a historical dataset to predict a probability that the product will need to be replaced based on the exposure data. In other features, the AI ​​system is trained to predict an impact of the need for replacement. In other features, the AI ​​system is trained to predict an impact of the need based on at least one of the impact of a delay or a reduction in supply on pricing. In other features, the exposure data collection system is further configured to collect exposure data from sensors located on at least one of the product, packaging for the product, a transportation vehicle in which the product is located, or proximate infrastructure.

[0161] A computerized method for replacing a product's product condition includes collecting exposure data indicative of at least one of an event or an environmental condition that may affect the product condition of the product. The method includes calculating a probability of the need to replace the product based on at least one of the event or the environmental condition. The method includes autonomously configuring an option-based futures contract for the product replacement based on the probability of the need to replace the product.

[0162] In other features, the computerized method includes autonomously configuring a smart contract to secure replacement of the product based on an option-based futures contract. In other features, the computerized method includes estimating a time until an actual determination of the need to replace the product is executed. Configuring the smart contract includes configuring the smart contract to have an option period based on a time until the actual determination is executed. In other features, configuring the smart contract includes configuring the smart contract to further have an option period based on a probability of catastrophic loss indicated by the probability of the need to replace the product. In other features, the computerized method includes configuring an alternative smart contract to provide a replacement of the product to at least one of a purchaser of the product, an owner of the product, or an insurance company holding a security interest in the product. In other features, configuring the alternative smart contract includes configuring an alternative smart contract to provide a refund of the purchase price of the product.

[0163] A more complete understanding of the present disclosure will be obtained from the following description and accompanying drawings, and the appended claims. All documents referenced herein are incorporated by reference. [Brief explanation of the drawings]

[0164] The accompanying drawings, which are included to provide a better understanding of this disclosure, illustrate embodiments of the disclosure and, together with the description, serve to explain many aspects of the disclosure.

[0165] [Figure 1] FIG. 1 is a block diagram illustrating the prior art relationships of various entities and facilities in a supply chain.

[0166] [Figure 2] FIG. 2 is a block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0167] [Figure 3] FIG. 3 is another block diagram illustrating the components and interrelationships of the systems and processes of a value chain network according to the present disclosure.

[0168] [Figure 4] FIG. 4 is a block diagram illustrating the components and interrelationships of the systems and processes of the digital product network of FIGS. 2 and 3 according to the present disclosure.

[0169] [Figure 5] FIG. 5 is a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack according to the present disclosure.

[0170] [Figure 6] FIG. 6 is a block diagram illustrating a platform and relationships for orchestrating the control of various entities in a value chain network in accordance with the present disclosure.

[0171] [Figure 7] FIG. 7 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0172] [Figure 8] FIG. 8 is a block diagram illustrating components and relationships of value chain entities managed by an embodiment of a value chain network management platform according to the present disclosure.

[0173] [Figure 9] FIG. 9 is a block diagram illustrating the network relationships of entities in a value chain network according to the present disclosure.

[0174] [Figure 10]FIG. 10 is a block diagram illustrating a set of applications supported by a unified data handling layer in a value chain network management platform according to the present disclosure.

[0175] [Figure 11] FIG. 11 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0176] [Figure 12] FIG. 12 is a block diagram illustrating components and relationships of a data storage layer in an embodiment of a value chain network management platform according to the present disclosure.

[0177] [Figure 13] FIG. 13 is a block diagram illustrating the components and relationships of the adaptive intelligent system layer in an embodiment of a value chain network management platform according to the present disclosure.

[0178] [Figure 14] FIG. 14 is a block diagram illustrating providing an adaptive intelligence system for coordinated intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0179] [Figure 15] FIG. 15 is a block diagram illustrating a hybrid adaptive intelligence system for collaborative intelligence for a set of supply and demand applications or categories of goods in accordance with the present disclosure.

[0180] [Figure 16] FIG. 16 is a block diagram illustrating providing an adaptive intelligence system for predictive intelligence for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0181] [Figure 17] FIG. 17 is a block diagram illustrating an adaptive intelligence system for providing classification intelligence for a set of supply and demand applications for categories of goods in accordance with the present disclosure.

[0182] [Figure 18] FIG. 18 is a block diagram illustrating providing an adaptive intelligence system to generate automated control signals for a set of supply and demand applications for a category of goods in accordance with the present disclosure.

[0183] [Figure 19] FIG. 19 is a block diagram illustrating training an artificial intelligence / machine learning system to generate intelligent routing recommendations for a selected value chain network in accordance with the present disclosure.

[0184] [Figure 20] FIG. 20 is a block diagram illustrating a semi-perceptual problem recognition system for recognizing pain points / problem situations in a value chain network according to the present disclosure.

[0185] [Figure 21] FIG. 21 is a block diagram illustrating a set of artificial intelligence systems operating on value chain information to enable automatic adjustment of an enterprise's value chain activities in accordance with the present disclosure.

[0186] [Figure 22] FIG. 22 is a block diagram illustrating the components and relationships involved in integrating a set of digital twins in one embodiment of a value chain network management platform according to the present disclosure.

[0187] [Figure 23]FIG. 23 is a block diagram illustrating a set of digital twins involved in an embodiment of a value chain network management platform according to the present disclosure.

[0188] [Figure 24] FIG. 24 is a block diagram illustrating components and relationships of an entity discovery and management system in an embodiment of a value chain network management platform according to the present disclosure.

[0189] [Figure 25] FIG. 25 is a block diagram illustrating components and relationships of a robotic process automation system in an embodiment of a value chain network management platform according to the present disclosure.

[0190] [Figure 26] FIG. 26 is a block diagram illustrating components and relationships of a set of opportunity miners in one embodiment of a value chain network management platform according to the present disclosure.

[0191] [Figure 27] FIG. 27 is a block diagram illustrating components and relationships of a set of edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0192] [Figure 28] FIG. 28 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform according to the present disclosure.

[0193] [Figure 29] FIG. 29 is a block diagram illustrating additional details of components and relationships in an embodiment of a value chain network management platform according to the present disclosure.

[0194] [Figure 30]FIG. 30 is a block diagram illustrating components and relationships in one embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0195] [Figure 31] FIG. 31 is a block diagram illustrating the components and relationships of a unified database in one embodiment of a value chain network management platform according to the present disclosure.

[0196] [Figure 32] FIG. 32 is a block diagram illustrating components and relationships of a set of unified data collection systems in an embodiment of a value chain network management platform according to the present disclosure.

[0197] [Figure 33] FIG. 33 is a block diagram illustrating components and relationships of a set of Internet of Things monitoring systems in an embodiment of a value chain network management platform according to the present disclosure.

[0198] [Figure 34] FIG. 34 is a block diagram illustrating the components and relationships of a machine vision system and a digital twin in an embodiment of a value chain network management platform according to the present disclosure.

[0199] [Figure 35] FIG. 35 is a block diagram illustrating components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0200] [Figure 36] FIG. 36 is a block diagram illustrating additional details of components and relationships of a set of adaptive edge intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0201] [Figure 37] FIG. 37 is a block diagram illustrating components and relationships of a set of unified adaptive intelligence systems in an embodiment of a value chain network management platform according to the present disclosure.

[0202] [Figure 38] FIG. 38 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a value chain system, according to some embodiments of the present disclosure.

[0203] [Figure 39] FIG. 39 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a container fleet management system, according to some embodiments of the present disclosure.

[0204] [Figure 40] FIG. 40 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a logistics design system, according to some embodiments of the present disclosure.

[0205] [Figure 41] FIG. 41 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a packaging design system, according to some embodiments of the present disclosure.

[0206] [Figure 42] FIG. 42 is a schematic diagram of a system configured to use real-world outcome data and a digital twin system to train an artificial system utilized by a waste mitigation system, according to some embodiments of the present disclosure.

[0207] [Figure 43] FIG. 43 is a schematic diagram illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins, according to some embodiments of the present disclosure.

[0208] [Figure 44] FIG. 44 is a block diagram illustrating the components and relationships of a set of intelligent project management equipment in an embodiment of a value chain network management platform according to the present disclosure.

[0209] [Figure 45] FIG. 45 is a block diagram illustrating components and relationships of an intelligent task recommendation system in an embodiment of a value chain network management platform according to the present disclosure.

[0210] [Figure 46] FIG. 46 is a block diagram illustrating components and relationships of a routing system between nodes of a value chain network in an embodiment of a value chain network management platform according to the present disclosure.

[0211] [Figure 47] FIG. 47 is a block diagram illustrating components and relationships of a dashboard for managing a set of digital twins in an embodiment of a value chain network management platform.

[0212] [Figure 48] FIG. 48 is a block diagram illustrating components and relationships in an embodiment of a value chain network management platform using a microservices architecture.

[0213] [Figure 49]FIG. 49 is a block diagram illustrating the components and relationships of an Internet of Things data collection architecture and sensor recommendation system in an embodiment of a value chain network management platform.

[0214] [Figure 50] FIG. 50 is a block diagram illustrating the components and relationships of a social data collection architecture in an embodiment of a value chain network management platform.

[0215] [Figure 51] FIG. 51 is a block diagram illustrating components and relationships of a crowdsourcing data collection architecture in an embodiment of a value chain network management platform.

[0216] [Figure 52] FIG. 52 is a perspective view illustrating an embodiment of a set of value chain network digital twins representing virtual models of a set of value chain network entities according to the present disclosure.

[0217] [Figure 53] FIG. 53 is a perspective view illustrating an embodiment of a warehouse digital twin kit system according to the present disclosure.

[0218] [Figure 54] FIG. 54 is a perspective view illustrating an embodiment of a stress test performed on a value chain network in accordance with the present disclosure.

[0219] [Figure 55] FIG. 55 is a perspective view illustrating an embodiment of a method used by a machine to detect faults and predict future failures of the machine in accordance with the present disclosure.

[0220] [Figure 56]FIG. 56 is a perspective view illustrating an embodiment of a deployment of machine twins to perform predictive maintenance on a set of machines in accordance with the present disclosure.

[0221] [Figure 57] FIG. 57 is a schematic diagram illustrating an example of a portion of a system for a value chain customer digital twin and a customer profile digital twin, according to some embodiments of the present disclosure.

[0222] [Figure 58] FIG. 58 is a schematic diagram illustrating an example of an advertising application interfacing with an adaptive intelligent systems layer in accordance with the present disclosure.

[0223] [Figure 59] FIG. 59 is a schematic diagram illustrating an example of an e-commerce application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0224] [Figure 60] FIG. 60 is a schematic diagram illustrating an example of a demand management application integrated with an adaptive intelligent systems layer in accordance with the present disclosure.

[0225] [Figure 61] FIG. 61 is a schematic diagram illustrating an example of a portion of a system for value chain smart supply component digital twin, according to some embodiments of the present disclosure.

[0226] [Figure 62] FIG. 62 is a schematic diagram illustrating an example of a risk management application interfacing with an adaptive intelligent systems layer in accordance with the present disclosure.

[0227] [Figure 63] FIG. 63 is a perspective view of maritime assets associated with a value chain network management platform including port infrastructure components according to the present disclosure.

[0228] [Figure 64] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure. [Figure 65] 64 and 65 are perspective views of maritime assets associated with a value chain network management platform including a vessel component in accordance with the present disclosure.

[0229] [Figure 66] FIG. 66 is a perspective view of maritime assets associated with a value chain network management platform including a barge component according to the present disclosure.

[0230] [Figure 67] FIG. 67 is a perspective view of maritime assets associated with a value chain network management platform, including those involved in maritime events, legal proceedings, and utilizing geofence parameters, in accordance with the present disclosure.

[0231] [Figure 68] FIG. 68 is a schematic diagram illustrating an example environment of an enterprise and executive control tower and management platform, including data sources in communication therewith, in accordance with some embodiments of the present disclosure.

[0232] [Figure 69] FIG. 69 is a schematic diagram illustrating an example of a component set of an enterprise control tower and management platform according to some embodiments of the present disclosure.

[0233] [Figure 70] FIG. 70 is a schematic diagram illustrating an example of an enterprise data model according to some embodiments of the present disclosure.

[0234] [Figure 71]FIG. 71 is a schematic diagram illustrating examples of different types of enterprise digital twins, including executive digital twins, associated with the data, processing, and application layers of an enterprise digital twin framework according to some embodiments of the present disclosure.

[0235] [Figure 72] FIG. 72 is a schematic diagram illustrating an example of an enterprise and executive control tower and management platform, according to some embodiments of the present disclosure.

[0236] [Figure 73] FIG. 73 is a flowchart illustrating example operations for configuring and servicing an enterprise digital twin.

[0237] [Figure 74] FIG. 74 illustrates a set of operations of a method for constructing a digital twin of an organization.

[0238] [Figure 75] FIG. 75 illustrates an example of the operation of a method for creating an executive digital twin.

[0239] [Figure 76] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 77]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 78] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 79] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 80]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 81] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 82] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 83]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 84] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 85] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 86]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 87] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 88] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 89]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 90] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 91] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 92]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 93] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 94] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 95]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 96] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 97] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 98]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 99] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 100] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 101]76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 102] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 103] 76-103 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure.

[0240] [Figure 104] FIG. 104 is a schematic diagram illustrating an example intelligence service system according to some embodiments of the present disclosure.

[0241] [Figure 105]FIG. 105 is a schematic diagram illustrating an example neural network having multiple layers, according to some embodiments of the present disclosure.

[0242] [Figure 106] FIG. 106 is a schematic diagram illustrating an exemplary convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0243] [Figure 107] FIG. 107 is a schematic diagram illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.

[0244] [Figure 108] FIG. 108 is a schematic diagram illustrating an example of a reinforcement learning-based approach for performing one or more tasks by a mobile system, according to some embodiments of the present disclosure.

[0245] [Figure 109] FIG. 109 is a schematic diagram illustrating an exemplary physical direction determination chip according to some embodiments of the present disclosure.

[0246] [Figure 110] FIG. 110 is a schematic diagram illustrating an example of a network enhancement chip according to some embodiments of the present disclosure.

[0247] [Figure 111] FIG. 111 is a schematic diagram illustrating an exemplary diagnostic chip according to some embodiments of the present disclosure.

[0248] [Figure 112] FIG. 112 is a schematic diagram illustrating an exemplary governance chip according to some embodiments of the present disclosure.

[0249] [Figure 113] FIG. 113 is a schematic diagram illustrating an exemplary prediction, classification, and recommendation chip according to some embodiments of the present disclosure.

[0250] [Figure 114] FIG. 114 is a perspective view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure.

[0251] [Figure 115] FIG. 115 is a schematic diagram illustrating an exemplary implementation of an autonomous additive manufacturing platform for automating and optimizing digital manufacturing workflows for metal additive manufacturing, according to some embodiments of the present disclosure.

[0252] [Figure 116] FIG. 116 is a flow diagram illustrating the optimization of different parameters of an additive manufacturing process according to some embodiments of the present disclosure.

[0253] [Figure 117] FIG. 117 is a schematic diagram illustrating a system for learning with data from an autonomous additive manufacturing platform to train an artificial learning system that uses a digital twin for classification, prediction, and decision-making, according to some embodiments of the present disclosure.

[0254] [Figure 118] FIG. 118 is a schematic diagram illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network, according to some embodiments of the present disclosure.

[0255] [Figure 119] FIG. 119 is a schematic diagram illustrating an exemplary embodiment of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub-processes, including process and material selection, hybrid part workflow, feedstock blending, part design optimization, risk prediction and management, marketing and customer service, in accordance with some embodiments of the present disclosure.

[0256] [Figure 120] FIG. 120 is a perspective view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system, according to some embodiments of the present disclosure.

[0257] [Figure 121] FIG. 121 is a schematic diagram illustrating an example of a distributed manufacturing network in which digital thread data is tokenized and stored in a distributed ledger to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network, according to some embodiments of the present disclosure.

[0258] [Figure 122] FIG. 122 is a perspective view of an example of a conventional computer vision system for creating an image of an object of interest.

[0259] [Figure 123] FIG. 123 is a schematic diagram illustrating an example implementation of a dynamic vision system for dynamically learning object concepts related to objects of interest, according to some embodiments of the present disclosure.

[0260] [Figure 124] FIG. 124 is a schematic diagram illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0261] [Figure 125] FIG. 125 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.

[0262] [Figure 126] FIG. 126 is a schematic diagram illustrating an example implementation of a dynamic vision system for modeling, simulating, and optimizing various optical, mechanical, design, and lighting parameters of the dynamic vision system according to some embodiments of the present disclosure.

[0263] [Figure 127] FIG. 127 is a schematic diagram illustrating an example of a dynamic vision system with detailed views 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.

[0264] [Figure 128] FIG. 128 is a schematic diagram illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0265] [Figure 129] FIG. 129 is a schematic diagram illustrating example configurations of multi-purpose and special-purpose robots according to some embodiments of the present disclosure.

[0266] [Figure 130] FIG. 130 is a schematic diagram illustrating an example of a platform-level intelligence layer of a fleet management platform according to some embodiments of the present disclosure.

[0267] [Figure 131] FIG. 131 is a schematic diagram illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.

[0268] [Figure 132] FIG. 132 is a schematic diagram illustrating an example security framework according to some embodiments of the present disclosure.

[0269] [Figure 133] FIG. 133 is a schematic diagram illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0270] [Figure 134] FIG. 134 is a schematic diagram illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure.

[0271] [Figure 135]FIG. 135 is a schematic diagram illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.

[0272] [Figure 136] FIG. 136 is a schematic diagram illustrating an example job analysis system and task definition system and example data flow thereof, according to some embodiments of the present disclosure.

[0273] [Figure 137] FIG. 137 is a schematic diagram illustrating an example fleet configuration system and its example data flow, according to some embodiments of the present disclosure.

[0274] [Figure 138] FIG. 138 is a schematic diagram illustrating an example workflow definition system and its example data flow, according to some embodiments of the present disclosure.

[0275] [Figure 139] FIG. 139 is a schematic diagram illustrating an example configuration of a multipurpose robot and its components according to some embodiments of the present disclosure.

[0276] [Figure 140] FIG. 140 is a schematic diagram illustrating an example architecture of a robotic control system according to some embodiments of the present disclosure.

[0277] [Figure 141] FIG. 141 is a schematic diagram illustrating an example architecture of a robotic control system 12150 that utilizes data from multiple sensors in a vision and sensing system, according to some embodiments of the present disclosure.

[0278] [Figure 142] FIG. 142 is a schematic diagram illustrating an example of a robotic vision and sensing system according to some embodiments of the present disclosure.

[0279] [Figure 143] FIG. 143 is a schematic diagram illustrating an exemplary process performed by a multipurpose robot to harvest crops, according to some embodiments of the present disclosure.

[0280] [Figure 144] FIG. 144 is a schematic diagram illustrating an example environment for an intermodal smart container system according to some embodiments of the present disclosure.

[0281] [Figure 145] FIG. 145 is a schematic diagram illustrating an example configuration of a smart container according to some embodiments of the present disclosure.

[0282] [Figure 146] FIG. 146 is a schematic diagram illustrating an intelligence service adapted to provide intelligence services to a smart intermodal container system according to some embodiments of the present disclosure.

[0283] [Figure 147] FIG. 147 is a schematic diagram illustrating a digital twin module according to some embodiments of the present disclosure.

[0284] [Figure 148] FIG. 148 illustrates an example embodiment of a method for receiving a request to update one or more properties of a digital twin of a shipping entity and / or an environment.

[0285] [Figure 149] FIG. 149 illustrates an example embodiment of a method for updating a set of downtime cost values ​​in a smart container's digital twin, according to some embodiments of the present disclosure.

[0286] [Figure 150] FIG. 150 is a schematic diagram illustrating an example environment for a digital product network according to some embodiments of the present disclosure.

[0287] [Figure 151] FIG. 151 is a schematic diagram illustrating an example environment for a connected product according to some embodiments of the present disclosure.

[0288] [Figure 152] FIG. 152 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0289] [Figure 153] FIG. 153 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0290] [Fig. 154] FIG. 154 is a flow diagram illustrating a method for using product level data according to some embodiments of the present disclosure.

[0291] [Figure 155] FIG. 155 is a schematic diagram illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0292] [Figure 156] FIG. 156 is a schematic diagram illustrating an example of a smart futures contract system according to some embodiments of the present disclosure.

[0293] [Figure 157] FIG. 157 is a schematic diagram illustrating an example environment of an edge networking system according to some embodiments of the present disclosure.

[0294] [Figure 158] FIG. 158 is a schematic diagram illustrating an example environment of an edge networking system including a VCN bus according to some embodiments of the present disclosure.

[0295] [Figure 159]FIG. 159 is a schematic diagram illustrating an example environment of an edge networking system according to some embodiments of the present disclosure, including a configured device EDNW system.

[0296] [Figure 160] FIG. 160 is a schematic diagram of an example embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0297] [Figure 161] FIG. 161 is a diagram illustrating quantum computing service request processing according to some embodiments of the present disclosure.

[0298] [Figure 162] FIG. 162 is a perspective view illustrating an embodiment of a biology-based value chain network system according to the present disclosure.

[0299] [Figure 163] FIG. 163 is a perspective view showing the thalamic services according to the present disclosure and how they are coordinated within the module.

[0300] [Fig. 164] FIG. 164 is a block diagram illustrating an energy system that may be in communication with similar systems, subsystems, components, and value chain network management platforms, according to some embodiments of the present disclosure.

[0301] [Figure 165] FIG. 165 is a block diagram illustrating an overview of a dual-process artificial neural network system according to some embodiments of the present disclosure.

[0302] [Figure 166A] FIG. 166A is a perspective view illustrating an example environment of a distributed database system according to the present disclosure.

[0303] [Figure 166B]FIG. 166B is a perspective view illustrating an exemplary architecture of a distributed database system according to the present disclosure.

[0304] [Figure 167A] 167A-167B are perspective views showing data storage in a distributed database system according to the present disclosure. [Figure 167B] 167A-167B are perspective views showing data storage in a distributed database system according to the present disclosure.

[0305] [Figure 168A] 168A-168B are perspective views illustrating systems and modules for implementing a distributed database system according to the present disclosure. [Figure 168B] 168A-168B are perspective views illustrating systems and modules for implementing a distributed database system according to the present disclosure.

[0306] [Figure 169A] 169A-169B are process diagrams illustrating an exemplary method for responding to queries received by a distributed database system according to the present disclosure. [Figure 169B] 169A-169B are process diagrams illustrating an exemplary method for responding to queries received by a distributed database system according to the present disclosure.

[0307] [Figure 169C] 169C-169D are process diagrams illustrating an exemplary method for optimizing a dynamic ledger maintained by a distributed database system according to the present disclosure. [Figure 169D] 169C-169D are process diagrams illustrating an exemplary method for optimizing a dynamic ledger maintained by a distributed database system according to the present disclosure.

[0308] [Figure 170A]170A-170B are data flow diagrams illustrating example data table creation queries processed by a distributed database system according to this disclosure. [Figure 170B] 170A-170B are data flow diagrams illustrating example data table creation queries processed by a distributed database system according to this disclosure.

[0309] [Figure 171A] 171A-171B are data flow diagrams illustrating example select queries processed by a distributed database system according to this disclosure. [Figure 171B] 171A-171B are data flow diagrams illustrating example select queries processed by a distributed database system according to this disclosure.

[0310] [Figure 172A] 172A-172C are data flow diagrams illustrating the operation of an example distributed join query in a distributed database system according to this disclosure. [Figure 172B] 172A-172C are data flow diagrams illustrating the operation of an example distributed join query in a distributed database system according to this disclosure. [Figure 172C] 172A-172C are data flow diagrams illustrating the operation of an example distributed join query in a distributed database system according to this disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0311] These include software systems for forecasting and managing customer demand, RFID and asset tracking systems for tracking goods as they move through the supply chain, and navigation routing systems for improving route selection efficiency. However, several major trends are forcing manufacturers, retailers, and other businesses to improve their supply chain performance. First, online and e-commerce operators, particularly Amazon®, have become the largest retail channel for many categories of goods, deploying 112 distribution and fulfillment centers across some regions, such as the United States, and housing hundreds of thousands, or even more, of product categories (SKUs), allowing customers to receive their orders the next day, or even the same day (and in some cases delivered to their doorsteps by drones, robots, and / or autonomous vehicles). For retailers without widely distributed fulfillment centers and warehouses, customer expectations for delivery speed are increasing pressure for supply chain efficiency and optimization. Thus, a need remains for improved supply chain methods and systems.

[0312] Second, agile manufacturing capabilities (especially the use of 3D printing and robotic assembly technologies), customer profiling techniques, and online ratings and reviews are driving customer expectations for product customization and personalization. Therefore, to stay ahead of the competition, manufacturers and retailers need to improve their methods and systems for understanding, predicting, and satisfying customer demand.

[0313] Historically, supply chain management and demand planning and management have been largely separate activities that come together when demand is converted into orders and passed to the supply side for fulfillment in the supply chain. As expectations for speed and personalization increase, there is a need for methods and systems that can provide unified orchestration of demand and supply.

[0314] Paralleling these other major trends is the emergence of the Internet of Things, with several categories of products, particularly smart home products like thermostats, lighting systems, and speakers, increasingly enabling on-board network connectivity and processing power and often including voice-controlled intelligent agents like Alexa® or Siri® to control devices, trigger specific application functions like music playback, or even order products. In some cases, smart products 650 even initiate orders, like a printer ordering refill cartridges. While intelligent products 650 are sometimes involved in connected systems, like an Amazon® Echo® product controlling a television or a sensor-enabled thermostat or security camera connecting to a mobile device, most intelligent products still primarily engage in isolated, application-specific sets of interactions. As artificial intelligence capabilities increase and more computing and networking power moves to network-enabled edge devices and systems that reside in the supply environment 670, the demand environment 672, and all the locations, systems, and facilities along the product's 1510 path from the manufacturer's loading dock to the customer's 662 or retailer's 664 destination 612, there is a need and opportunity for dramatically improved intelligence, control, and automation of all factors involved in supply and demand. [Value Chain Network]

[0315] Referring to FIG. 2, a block diagram illustrating the components and interrelationships of value chain network systems and processes is shown at 200. In an exemplary embodiment, “value chain network,” as used herein, refers to the elements and interconnections of historically separate demand management systems and processes and supply chain management systems and processes, made possible by the development and convergence of numerous diverse technologies. In an exemplary embodiment, a value chain control tower 260 (e.g., sometimes referred to herein as a “value chain network management platform,” “VCNP,” or simply the “system,” or “platform”), a big data center (e.g., big data processing 230), and associated processing capabilities receive data flows, data pools, data streams, and / or other data configurations and transmission modalities received from, for example, a digital product network 21002, directly from customers (e.g., directly connected customers 250), or from other third parties 220. Communications related to market orchestration activities and communications 47 210, analytics 232, or any other type of input may also be utilized by a value chain control tower for demand enrichment 262, synchronized planning 234, intelligent sourcing 238, dynamic fulfillment 240, or any other smart operations informed by collaborative and adaptive intelligence, as described herein.

[0316] 3, another block diagram illustrating the components and interrelationships of value chain network systems and processes, as well as associated use cases, data processing, and related entities is shown. In an exemplary embodiment, a value chain control tower 360 can coordinate market orchestration activities 310, including, but not limited to, demand curve management 352, ecosystem synchronization 348, intelligent sourcing 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In an exemplary embodiment, the value chain control tower 360 can be connected to, communicate with, or otherwise operably coupled to an adaptive data pipeline 302 and processing facilities, which may further connect to external data sources 320 and a data handling stack 330 (e.g., value chain network technologies), which may include an intelligent, user-adaptive interface, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 may also be further connected to, in communication with, or otherwise operably coupled to additional value chain entities, including, but not limited to, the digital product network 21002, customers (e.g., designated connected customers 362), and / or other connected operations 364 and entities of the value chain network. [Digital Product Network (DPN)]

[0317] Referring to FIG. 4, a block diagram illustrating the components and interrelationships of systems and processes of a digital product network at 400 is shown. In an exemplary embodiment, products (including goods and services) can create and send data, such as product-level data, to a communications layer and / or edge data processing facility in a value chain network technology stack. This data can generate enriched product-level data and be combined with third-party data for further processing, modeling, or other adaptive or collaborative intelligence activities, as described herein. This includes, but is not limited to, generating and / or simulating product and value chain use cases, for which the data can be utilized by products, product development processes, product designs, and the like. [Stack view example]

[0318] Referring to FIG. 5, a block diagram illustrating the system and process components and interrelationships of a value chain network technology stack is shown at 500, which may include, but is not limited to, a presentation layer, an intelligence layer, and serverless functionality such as a platform (e.g., a development platform and a hosting platform), a data facility (e.g., data related to IoT and big data), and a data aggregation facility. In an exemplary embodiment, the presentation layer may include, but is not limited to, a user interface, modules for research and discovery, and modules for tracking user experience and engagement. In an exemplary embodiment, the intelligence layer may include, but is not limited to, statistical and computational methods, semantic models, analytical libraries, a development environment for analytics, algorithms, logic and rules, and machine learning. In an exemplary embodiment, the platform or value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device detection. In an exemplary embodiment, the data aggregation facility or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from heterogeneous devices. In an exemplary embodiment, the data facilities or layers may include, but are not limited to, IoT and big data access, control, and collection and replacement. In an exemplary embodiment, the value chain network technology stack may be further associated with additional data sources and / or technology enablers. [Value chain orchestration from a command platform]

[0319] FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (sometimes referred to herein as a “value chain control tower,” “VCNP,” or simply “system,” or “platform”) organizes various factors involved in planning, monitoring, controlling, and optimizing the various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, and logistics and distribution factors. By virtue of the unified platform 604 for monitoring and managing supply and demand factors and status information (e.g., quality and status, planning, ordering and confirmation, and / or tracking and tracing), demand factors can be understood, accounted for, and shared among various entities (e.g., including customers / consumers, distribution such as suppliers, distributors, and production such as producers or production facilities) as orders are generated, fulfilled, and products are created and moved through the supply chain. The value chain network 668 can include not only the intelligent product 1510 but also all of the facilities, infrastructure, personnel, and other entities involved in planning and satisfying demand for it. [Value Chain Network and Value Chain Network Management Platform]

[0320] Referring to FIG. 7 , the value chain network 668 managed by the value chain management platform 604 may include a set of value chain network entities 652 such as, but not limited to: a product 1510, which may be an intelligent product 1510; a set of production facilities 674 involved in the production of the finished product, components, systems, subsystems, materials used in the product, etc.; various entities, activities, and other supply factors 648 involved in the supply environment 670, such as suppliers 642, points of origin 610; various entities, activities, and other supply factors 648 involved in the demand environment 672, such as suppliers 642, points of origin 610; various entities, activities and other demand factors 644, such as customers 662 (including consumers, businesses, and intermediate customers such as value-added resellers and distributors), retailers 664 (including online retailers, mobile retailers, traditional brick-and-mortar retailers, pop-up shops, etc.) located and / or operating in various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and maritime facilities 622, such as port infrastructure facilities 660, floating assets 620, and shipyards 638. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise enables the management (and sometimes autonomous or semi-autonomous operation) of processes, workflows, activities, events, and applications 630 (sometimes collectively referred to as simply "applications 630") of the extensive value chain network 668.

[0321] 7 , a high-level schematic diagram of a value chain network management platform 604 is illustrated. 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 in conjunction to enable intelligent management of a set of value chain entities 652 that occur, operate, transact, etc. within, or that own, operate, support, or enable one or more value chain network processes, workflows, activities, events, and / or applications 630, or products 1510 (which may be any category of product, such as a finished product, software product, hardware product, component product, material, equipment item, consumer packaged goods item, consumer product, food item, beverage product, etc.). The intelligent product 1510 may be a consumer product, food product, beverage product, household product, business product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and / or set of related services that may be part of, integrated with, linked to, or operated by the VCNP 604, and in embodiments may include an intelligent product 1510 that enables a set of capabilities such as, but not limited to, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, acoustic processing systems, data storage, data integration, and / or various Internet of Things capabilities.

[0322] In an embodiment, the management platform 604 may include a set of data processing layers 608, each configured to provide a set of functionality to facilitate the development and deployment of intelligence for a wide variety of value chain network applications and end uses, such as automation, machine learning, the application of artificial intelligence, intelligent transactions, state management, event management, process management, and many others. In an embodiment, the data processing layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and end uses within the platform 604, via a value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or work with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. In an embodiment, the data processing layer 608 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 through a value chain network-oriented data storage system layer 624, sometimes referred to herein for convenience simply as a data storage layer 624 or a storage layer 624. As shown in FIG. 7 , the data processing layer 608 may also include an adaptive intelligent systems layer 614. The adaptive intelligent systems layer 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere in this disclosure. The data processing, artificial intelligence, and computation systems 634 may 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 computational systems 634, in some embodiments, may relate to the use of recurrent networks as adaptive intelligence systems that operate on a blockchain of transactions in a supply chain to determine patterns, use with biological systems, opportunity mining (e.g., artificial intelligence systems may be used to monitor new data sources as opportunities to automatically deploy intelligence), robotic process automation (e.g., automating intelligent agents for various workflows), edge and network intelligence (e.g., involved in monitoring systems that adaptively use available RF spectrum, adaptively use available fixed network spectrum, adaptively store data based on available storage conditions, adaptively sense based on a type of context sensing, etc.), and the like.

[0323] In embodiments, the data handling layer 608 may be depicted in a vertical stack or ribbon in the diagram and may represent many functionalities available to the platform 604, including storage, monitoring, and processing applications and resources, and combinations thereof. In embodiments, the set of capabilities of the data handling layer 608 may include a shared microservices architecture. By these examples, the set of capabilities may be deployed to provide multiple separate services or applications, which may be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or resident in a particular application or process. In some examples, the set of capabilities may include one or more activities marshaled for the benefit of the platform. In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the platform's set of capabilities may be deployed within at least a portion of a common architecture, such as a common architecture supporting a common data schema. In embodiments, one of the platform's set of functions may be deployed within at least a portion of a common architecture that can support common storage. In embodiments, one of the platform's set of capabilities may be deployed within at least a portion of a common architecture that can support a common monitoring system. In embodiments, one or more capability sets of the platform may be deployed within at least a portion of a common architecture that may support one or more common processing frameworks. In embodiments, the set of capabilities of the data processing layer 608 may include examples where storage capabilities support scalable processing capabilities, scalable monitoring systems, digital twin systems, payment interface systems, etc.By these examples, one or more software development kits may be provided by the platform along with deployment interfaces to facilitate connection and use of the capabilities of the data handling layer 608. In a further example, an adaptive intelligence system may analyze, remodel, configure, and reconfigure one or more capabilities of the data handling layer 608. In an embodiment, the platform 604 may include a common data storage schema that provides, for example, shipyard entity-related services 51 and warehousing entity services. There are many other applicable examples and combinations applicable to the aforementioned examples involving many value chain entities disclosed herein. By these examples, the platform 604 may be shown to create connectivity (e.g., capability and information provision) across many value chain entities. In many examples, there are pairings (dual, triple, quadruple, etc.) of similar types of value chain entities that use one or more small capability sets of the data handling layer 608 to deploy (interact, depend, etc.) a common data schema, a common architecture, a common interface, etc. While services and capabilities may be provided to a single value chain entity, the platform can be shown to provide myriad benefits to the value chain and consumers by supporting connectivity across value chain entities and the applications used by the entities. [Platform-managed value chain network entities]

[0324] 8 , the value chain network management platform 604 is illustrated in association with a set of value chain entities 652 that may be managed by, integrated with, or integrated into the platform 604, and / or that may provide inputs to and / or take outputs from the platform 604, 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 others, as involved in various value chain network processes, workflows, activities, events, and applications 630 (collectively “applications 630” or simply “activities”). Connectivity with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in more detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data handling layer, for the platform as a whole, and / or between value chain entities 652.

[0325] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals, or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, including, but not limited to: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loaders, cranes, lifts, carriers, trucks, loaders, unloaders, packers, pickers, and robotic systems, such as physical robots, collaborative robots (e.g., "cobots"), drones, autonomous vehicles, software hotspots, and many others); products 650 (which may be any category of product, such as a finished product, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, household product, business product, consumable product, pharmaceutical product, medical equipment product, technology product, entertainment product, or other type of product and / or set of related services); value chain processes 722 (shipping process, transportation process, maritime process, inspection process, etc.); processes, transportation processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, etc.), border control processes, port-related processes, software processes (including applications, programs, services, etc.), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, trading processes, etc.), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, etc.), wearable and portable devices 720 (e.g., mobile phones, tablets, portable devices dedicated to value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, eyeglasses, hearables, head-worn devices, clothing-integrated devices, wristwatches, bracelets, neck-worn devices, AR / VR devices, headphones, etc.); workers 718 (delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers,fulfillment center delivery workers, warehouse workers, vehicle drivers, operations managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, material handling workers, inspectors, delivery personnel, environmental managers, financial asset managers, process supervisors and workers (of any of the processes mentioned herein), security personnel, safety personnel, and many others; Suppliers 642 (suppliers of goods and related services of any kind, parts suppliers, ingredient suppliers, material suppliers, manufacturers, and many others); Customers 662 (consumers, licensees, including businesses, value-added resellers, other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use categories of goods and / or related services; extensive operational facilities 712 (including loading docks, storage and warehousing facilities 654, storage, distribution facilities 658, and fulfillment centers 628); air travel facilities 740 (including aircraft, airports, hangars, runways, fueling bases, etc.); maritime facilities 622 (including port infrastructure facilities 622 (docks, yards, cranes, roll-on / roll-off facilities, ramps, Containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (ships, barges, boats, and others), facilities and other goods at origin 610 and / or destination 628, transportation facilities 710 (container ships, barges, other floating assets 620, and land vehicles and other delivery systems 632 used to transport goods, such as trucks, trains, and others); items or factors that consider demand (i.e., demand factors 644) (including market factors, events, and many others) , items or factors to consider for supply (i.e., supply factors 648) (market factors, weather, availability of parts and materials, and many others); logistics factors 750 (availability of transportation routes, weather, fuel prices, regulatory factors, availability of space (on vehicles, in containers, in packages, in warehouses, in fulfillment centers, on shelves, and many others); retailers 664 (including online retailers 730, such as in the form of e-commerce sites 730), routes for transportation (waterways 732, roads 734, airways, railroads 738, and many others); robotic systems 744 (mobile robots, cobots,15. The intelligent products 1510 may be embedded in or VCNP 604 may include intelligent products 1510. Intelligent products 1510 may be enabled with a range of capabilities such as, but not limited to, data processing, networking, sensing, autonomous operation, intelligent agents, natural language processing, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computing, artificial intelligence, analog or digital sensors, cameras, acoustic processing systems, data storage, data integration, and / or various Internet of Things capabilities. The intelligent product 1510 may include a form of information technology. The intelligent product 1510 may have a processor, computer random access memory, and a communications module. The intelligent product 1510 may be a passive intelligent product, similar to an RFID-type data structure, where the intelligent product can be pinged or read. The intelligent product 1510 may fit into a value chain network in a connected way, where connectivity is built around the intelligent product 1510 through a sensor, IoT device, tag, or another component.

[0326] 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 control instructions to or take instructions from any of the value chain entities 652, or via various functions of the data processing layer 608 described throughout this disclosure, etc. [Network characteristics of value chain network entities]

[0327] 9 , there is illustrated the orchestration of a set of deeply interconnected value chain network entities 652 in a value chain network 668 by a value chain network management platform 604. Each of the value chain network entities 652 may have a connection to a VCNP 604, a connection to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via the cloud, or other connection), and / or a connection to other value chain network entities 652 via the VCNP 604. The value chain network management platform 604 can manage connections, configure or provide resources to enable connections, and / or manage applications 630 that utilize connections by using information from one set of entities 652 to provide information to applications 630 that include another set of entities 652, by coordinating the activities of a set of entities 652, by providing input to artificial intelligence systems of the VCNP 604 or to artificial intelligence systems of or about the set of entities 652, by interacting with edge computing systems and their environments located on or within the entities 652, etc.

[0328] The entities 652 may be external, such that the VCNP 604 interacts with these entities 652. The VCNP 604 may act as a control tower and establish oversight (e.g., common oversight across multiple entities 652). In one unified platform, there may be an interface where users can view various items, such as user destinations, ports, aviation and rail assets, and orders. A common data schema is then established to enable services to operate across any of these applications. This may involve taking any of the data flowing through or about any of these entities 652 and pulling the data into a framework where other applications across supply and demand can interact with the entities 652. This may be a shared data pipeline coming from IoT systems and other external data sources, fed into the monitoring layer and stored in a common data schema in the storage layer, and then various intelligence may be trained to identify meaning across these entities 652. In an exemplary embodiment, a supplier may go bankrupt or be determined to be bankrupt, causing VCNP 604 to automatically trigger a replacement smart contract to send modified terms to a secondary supplier. There may also be management of various aspects of the supply chain. For example, immediate automatic pricing changes on the demand side in response to confirmation that another supplier has gone bankrupt (e.g., from a bankruptcy announcement). Other similar examples may be used based on what occurs in its automation layer, which may be enabled by VCNP 604. This VCNP 604 interface layer then uses a digital twin to allow users to view all these entities 652, which are not typically displayed together, and monitor what is happening with each of these entities 652, including identifying problem states. For example, after seeing a quarterly bad financial report for a supplier, the report may be flagged for close monitoring, such as for potential future bankruptcy.

[0329] For example, an IoT system deployed at a fulfillment center 628 may interface with an intelligent product 1510 to take customer feedback about the product 1510, and an application 630 for the fulfillment center 628, upon receiving customer feedback via its connection to the intelligent product 1510 about an issue with the product 1510, may initiate a workflow to perform corrective action on similar products 650 before the products 650 are dispatched from the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, may notify a fleet of floating assets 620 (ships, barges, etc.) via its connection to the floating assets 620 that the port is approaching capacity, thereby initiating a negotiation process (which may include automated negotiations based on a set of rules and governed by smart contracts) regarding remaining capacity, allowing some assets 620 to be redirected to an alternative port or holding facility. These and many other connections between value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections between defined groups of entities 652 (such as those controlled by the same owner or operator), are encompassed herein as applications 630 managed by VCNP 604. Platform-managed value chain network activities and applications

[0330] 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604, and / or participating in the set of value chain network entities 652, may include any one or more of a wide variety of types of applications, such as, but not limited to, supply chain management applications 21004 (such as managing the timing, quantity, logistics, shipping, delivery, and other details of orders for goods, parts, and other items); asset management applications 814 (such as managing value chain assets, such as floating assets (ships, boats, barges, floating platforms, etc.), real estate (such as those used for warehouses, ports, shipyards, distribution centers, and other building locations), equipment, machinery, supplies (such as those used for handling containers, cargo, parcels, goods, and other items); financial applications 822 (e.g., for handling financial matters related to value chain entities and assets, including, but not limited to, payments, collateral, bonds, customs, duties, levies, taxes, etc.); for managing risk or liability related to shipments, goods, products, assets, people, floating assets, vehicles, equipment, parts, information technology systems, security systems, security events, cybersecurity systems, property, health conditions, death, fire, flood, weather, failure, negligence, business interruption, personal injury, property damage, business loss, breach of contract, etc.);Demand management applications 824 (such as, but not limited to, 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 applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, etc.) applications, product or service recommendation engines, and the like (including those that use one or more features of the Intelligent Products 1510 or are run using the intelligence features of the Intelligent Products 1510); trading applications 858 (such as, but not limited to, buying applications, selling applications, bidding applications, auction applications, reverse auction applications, bid / ask matching applications, analytical applications for analyzing value chain performance, yields, return on investment, or other metrics, or the like); tax applications 850 (for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, and the like relating to taxes, duties, levies, tariffs, credits, fees, and other government charges, such as taxes, customs duties, value added taxes, sales taxes, income taxes, property taxes, municipal taxes, pollution taxes, renewable energy credits, pollution abatement credits, import duties, export duties, etc.);identity management applications 830 (e.g., for managing one or more identities of entities 652 involved in the value chain, such as, but not limited to, one or more of an identity verification application, a biometric identity verification application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a blacklist application, a whitelist application, a content inspection-based fraud detection application, or other fraud detection application); inventory management applications 820 (e.g., for managing inventory at a fulfillment center, distribution center, warehouse, storage facility, store, port, ship, or other floating asset, or other location); security applications, solutions, or services 834 (referred to herein as security applications, such as, but not limited to, any of the identity management applications 830 described above, as well as physical security systems (e.g., access control systems (e.g., using biometric access control, fingerprint authentication, retinal scans, passwords, and other access controls), safes, vaults, cages, vaults, secure storage facilities, etc.), surveillance systems (e.g., using cameras, surveillance systems (e.g., using cameras, motion sensors, infrared sensors, and other sensors), perimeter security systems, floating security systems for floating assets, cybersecurity systems (e.g., VEMS 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 for detecting, characterizing, or predicting the likelihood and / or extent of an accident or other damage event, including safety management based on any of the data sources, events, or entities noted throughout this disclosure or throughout the documents incorporated by reference herein, such as for improving worker safety, reducing the likelihood of damage to property, reducing accident risk, reducing the likelihood of damage to goods (e.g., cargo), risk management related to insured goods, loan collateral, etc.); blockchain applications 844 (including, but not limited to, a distributed ledger or other blockchain-based application capturing a series of transactions such as debits or credits, purchases or sales, exchanges of value in kind, smart contract events, etc.); facility management applications 850 (e.g., facilities management applications for shipyards, ports, distribution centers, warehouses, docks, stores, fulfillment centers, storage facilities, etc.); information technology systems, such as for managing and designing infrastructure, buildings, systems, real estate, personal property, and other assets that support the value chain, including warehouses and other facilities; robotic / autonomous vehicle systems, packaging systems, packing systems, picking systems, inventory tracking systems, inspection systems, routing systems for mobile robots, workflow systems for human assets, etc.; regulatory applications 852 (such as, but not limited to, applications to regulate any of the applications, services, transactions, activities, workflows, events, entities, or other items described in this specification and the documents incorporated by reference herein, including, but not limited to, regulations governing permitted routes, permitted cargo and goods, permitted transaction parties, required disclosures, privacy, pricing, marketing, product and service offerings, data use (including, but not limited to, data privacy regulations, data retention regulations, etc.), banking, marketing, sales, financial planning, and many other regulations);commerce applications, solutions or services 854 (such as e-commerce site marketplaces, online sites, auction sites or marketplaces, spot marketplaces, advertising marketplaces, reverse auction marketplaces, ad networks, or other marketplaces; vendor management applications 832 (such as, but not limited to, applications for managing a set of vendors or potential vendors and / or for managing the procurement of a set of goods, components, or materials that may be supplied in a value chain, including functionality such as vendor qualification, vendor ratings, requests for proposals, requests for information, bonds or other performance guarantees, contract management, etc.); analytics applications 838 (such as, but not limited to, applications referred to throughout this disclosure or documents incorporated by reference herein; analytics applications, such as big data applications, user behavior applications, forecasting 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 other applications; pricing applications 842 (such as, but not limited to, applications for goods, services (including those referred to throughout this disclosure and documents incorporated by reference herein);and smart contract applications, solutions, or services (collectively referred to herein as smart contract applications 848, including, but not limited to, any of the smart contract types mentioned in this disclosure or the documents incorporated herein by reference, such as, for example, a smart contract for the sale of goods, a smart contract for ordering goods, a smart contract for delivery resources, a smart contract for workers, a smart contract for the delivery of goods, a smart contract for the installation of goods, a smart contract for tokens or cryptocurrency as consideration, a smart contract that attributes rights, options, futures, or interests based on future conditions, a smart contract for securities, commodities, futures, options, derivatives, etc., a smart contract for current or future resources, a smart contract configured to consider or address tax, regulatory, or compliance parameters, a smart contract configured to perform arbitrage transactions, or many other smart contracts). Thus, the value chain management platform 604 may host a wide range of heterogeneous applications 630 (such term including the above and other value chain applications, services, solutions, etc.) to enable interaction between them, such that any set or larger combination or permutation of such services may be improved compared to isolated applications of the same type through shared microservices, shared data infrastructure, and shared intelligence;

[0331] With further reference to FIG. 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involved with the set of value chain network entities 652 may further include, but are not limited to: payment applications 860 (such as for performing payment calculations (including based on situational factors such as taxes, duties, etc. applicable to the geography of the entity 652), fund transfers, payment settlements to parties, etc. for any of the applications 630 noted herein); process management applications 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); and processes, workflows, activities, or other actions described herein. compatibility testing applications 864, such as for assessing compatibility between value chain network entities 652 or activities involved in any of the applications 630 (e.g., to determine compatibility of a container or package with a product 1510, compatibility of a product 1510 with another product 1510 (such as when one is a supply, resupply, replacement part, etc. of the other), such as to determine compatibility of a container or package with a product 1510, compatibility of a product 1510 with a set of customer requirements, compatibility of infrastructure and equipment entities 652 (such as between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a road, between a drone or robot and a package, between a drone, AV, or robot and a delivery destination, and many others); infrastructure testing applications 802 (such as to test the capabilities of infrastructure elements to support a product 1510 or application 630, including but not limited to storage capacity, lifting capacity, movement capacity, memory capacity, network capacity, environmental control capacity, software capacity, security capacity, and many others);and / or an incident management application 910 (for managing events, accidents, and other incidents that may occur in one or more environments involving the value chain network entities 652, such as, but not limited to, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, and combinations thereof), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, abuse incidents, and many others);

[0332] 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604 and / or involved with the set of value chain network entities 652 may further include, but are not limited to: predictive maintenance applications 910 (such as for forecasting, forecasting, and taking actions to manage breakdowns, malfunctions, shutdowns, damage, required maintenance, required repairs, required service, required support, etc., for the set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, etc.); logistics applications 912 (such as for managing logistics for pickup, delivery, transferring items onto material handling equipment, loading, unloading, packaging, picking, shipping, driving, and other activities involved in scheduling and managing the movement of products 650 and other items between origins and destinations via various intermediate points); reverse logistics applications 914 (such as for managing returned products, waste reduction applications 920 (e.g., for reducing packaging waste, solid waste, energy waste, liquid waste, pollution, contaminants, computing resource waste, human resource waste, or other waste involved in a value chain network entity 652 or activity); augmented reality, mixed reality, and / or virtual reality applications 930 (e.g., for visualizing one or more value chain network entities 652 or activities involved in one or more applications 630, such as, but not limited to, the movement of a product 1510, the interior of a facility, the state or condition of an item of goods, one or more environmental conditions, weather conditions, the packaging configuration of a container or set of containers, or many others); demand forecasting applications 940 (e.g., for forecasting demand for a product 1510, a category of product, a potential product, and / or factors contributing to the demand, such as market factors, wealth factors, demographic factors, weather factors, economic factors, etc.);a demand aggregation application 942 (e.g., for aggregating information, orders, and / or commitments (optionally embodied in one or more contracts, which may be smart contracts) for one or more products 650, categories, etc., including current demand for existing products and future demand for products that are not yet available); a customer profiling application 944 (e.g., for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, such as based on past purchasing data, loyalty program data, behavioral tracking data (including data captured in customer interactions with smart products 1510), online clickstream data, interactions with intelligent agents, and other data sources), and / or a component supply application 948 (e.g., for managing a supply chain of components for a set of products 650);

[0333] With further reference to FIG. 10 , the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604, and / or involved with the set of value chain network entities 652 may further include, but are not limited to: a policy management application 868 (e.g., for governance of one or more value chain network entities 652 or applications 630, such as for managing the execution of one or more workflows (which may include configuring policies in the platform 604 for each workflow), such as for deploying one or more policies, rules, etc., for managing compliance with regulations (including maritime, food and drug, healthcare, environmental, health, safety, tax, financial reporting, commercial, and other regulations described throughout this disclosure or as would be understood in the art), for managing the provision of resources (such as connectivity, computing, human, energy, and other resources), including smart contracts, and for managing the provision of resources on the platform 604, such as for managing the execution of one or more workflows (which may include configuring policies in the platform 604 for each workflow), for managing compliance with regulations (including maritime, food and drug, healthcare, environmental, health, safety, tax, financial reporting, commercial, and other regulations), for managing the provision of resources (such as connectivity, computing, human, energy, and other resources), including smart contracts, and for managing the provision of resources on the platform 604, such as ... the platform 604 can automatically deploy governance capabilities to associated entities 652 and applications 630, such as via connectivity facilities 642, managing interactions with other entities (including policies for information sharing and access to resources), managing data access (including privacy data, operational data, status data, and many other data types), managing security access to infrastructure, products, facilities, locations, etc., and many other things; a product configuration application 870 (a product manager and / or an automated product configuration process (optionally robotic process automation) to determine the configuration of the product 1510, including on-the-fly configuration, such as during agile manufacturing, involving in-path configuration or customization (such as by 3D printing one or more features or elements), or involving remote configuration or customization, such as by downloading firmware, configuring field programmable gate arrays, installing software, etc.);Warehouse management and fulfillment applications 872 (for managing warehouses, distribution centers, fulfillment centers, etc., including product selection, configuring product storage locations, determining routes for moving products through facilities by personnel or mobile robots, determining picking and packing schedules, routes, and workflows, managing the operation of robots, drones, conveyors, and other equipment, determining schedules for product delivery to delivery docks, etc., and many other functions); kit configuration and deployment applications 874 (for enabling VCNP users to configure kits, boxes, or otherwise pre-integrated, pre-provisioned, and / or pre-configured systems to allow customers or operators to quickly deploy a subset of the VCNP 604's functionality for specific value chain network entities 652 and / or applications 630); and / or product testing applications 878 for testing products 1510 (for testing for performance, functionality and feature activation, safety, policy or regulatory compliance, quality, service quality, likelihood of failure, and many other factors);

[0334]

[0013] With further reference to FIG. 10, the set of applications 614 provided on, integrated with, and / or managed by or for the VCNP 604, and / or comprising the set of value chain network entities 652, may further include, but are not limited to: a marine fleet management application 880 (for managing a set of marine assets such as container ships, barges, boats, etc., for example, to determine optimal routes for 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 metrics, and many others); a shipping management application 882 (for managing a collection of shipping assets such as trucks, trains, planes, etc., for optimizing financial yields, improving safety, reducing energy consumption, reducing delays, mitigating environmental impacts, and many other purposes); an opportunity matching application 884 (for example, matching one or more demand factors with one or more supply factors, etc.); 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 many others; workforce management applications 888 (such as for managing workers in various workforces, including workforces in or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise 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 (such as for planning, scheduling, routing, and otherwise managing the distribution and delivery of products 650 and other items); and / or enterprise resource planning (ERP) applications 892 (such as for planning the utilization of enterprise resources, including labor resources, financial resources, energy resources, physical assets, digital assets, and other resources). [Core functions and interactions of the data handling layer (adaptive intelligence, monitoring, data storage, and applications)]

[0335] Referring to FIG. 11 , a high-level schematic diagram of one embodiment of a value chain network management platform 604 is shown, which may include systems, applications, or other entities that operate in conjunction to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact, etc. within one or more value chain network processes, workflows, activities, events, and / or applications 630, or that own, operate, support, or enable, or that may otherwise be part of, integrated with, linked to, or otherwise operated by the platform 604. 15. The platform 604 operates in connection with products 1510 (which may be a finished product, a software product, a hardware product, a component product, a material, an equipment item, a consumer packaged good, a consumer product, a food product, a beverage product, a household product, an office product, a consumable product, a pharmaceutical product, a medical device product, a technology product, an entertainment product, or any other type of product or related service, and in embodiments may encompass intelligent products enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities), including a set of applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements. The value chain entities 652 may include any of a wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals, or other entities mentioned throughout this disclosure or in documents incorporated by reference herein that are involved in or are involved in a wide range of value chain activities (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 that are involved in the various value chain network processes, workflows, activities, events, and applications 630).

[0336] In an embodiment, the value chain network management platform 604 may include a set of data processing layers 608, each configured to provide a set of functions that facilitate the development and deployment of intelligence, such as to facilitate automation, machine learning, the application of artificial intelligence, intelligent transactions, intelligent operations, remote control, analysis, monitoring, reporting, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In an embodiment, the data processing layers 608 may include a value chain network monitoring system layer 614, a value chain network entity-oriented data storage system layer 624 (sometimes referred to herein simply as the data storage layer 624 for convenience), an adaptive intelligent system layer 614, and a value chain network management platform 604. The value chain network management platform 604 may include the data processing layers 608 to provide management of the value chain network management platform 604 and / or administration of the value chain network management platform 604. Other layers, such as the value chain network monitoring system layer 614, the value chain network entity-oriented data storage system layer 624 (for example), and the data handling layer 608, may each include various services, programs, applications, workflows, systems, components, and modules, as further described herein and in documents incorporated by reference. In embodiments, each of the data handling layers 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other layers 624 or by other systems (e.g., configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture, for example).For example, the platform 604 may have (or configure and / or provide) a set of connectivity facilities 642, such as network connections (including various configurations, types, and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, microservices, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud functions, etc., that the data handling layer 608 can use to communicate data or information between the data handling layer 608 and other layers, systems, or subsystems of the platform 604, as well as other systems, such as value chain entities 652, or external systems, such as cloud-based or on-premise enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems, etc.). Each of the data handling layers 608 may include a set of services (e.g., microservices) for data handling, including facilities for data extraction, transformation, and loading, facilities for data cleansing and deduplication, facilities for data normalization, facilities for data synchronization, facilities for data security, facilities for computation (e.g., facilities for performing predefined computational operations on data streams and providing output streams), facilities for compression and decompression, facilities for analysis (e.g., facilities for providing automatic generation of data visualizations), etc.

[0337] In an embodiment, each data processing layer 608 has a set of application programming connection facilities 642 for automating data exchange with each of the other data processing layers 608. These may include data integration functions such as for extracting, converting, loading, normalizing, compressing, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information exchanged between layers and / or applications 630, e.g., converting data from one format or protocol to another as needed for one layer to consume the output from another layer. In an embodiment, the data processing layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring system layer 614. The value chain monitoring system layer 614 may include, integrate with, and / or collaborate with various data collection and management systems 640, sometimes conveniently referred to as data collection systems 640, to collect and organize data collected from or about value chain entities 652, as well as data collected from or about various data layers 624 or their services or components. For example, streams of physiological data from wearable devices worn by workers performing work or consumers engaging in activities may be distributed via the monitoring system layer 614 to multiple different applications within the value chain management platform 604, such as those that facilitate monitoring the worker's physiological, psychological, performance level, attention, or other condition and those that facilitate operational efficiency and / or effectiveness. In embodiments, the monitoring system layer 614 facilitates alignment of data collected for one or more value chain network entities 652, such as time synchronization, normalization, etc.For example, one or more video streams or other sensor data collected of or about workers 718 or other entities in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock so that the relative timing of the set of video or other data can be understood by a system that may process the video, such as images in the video, changes between images in different frames of the video, or a machine learning system operating based thereon. In such an example, the monitoring system layer 614 may further align the set of video, camera images, sensor data, etc. with other data, such as streams of data from wearable devices, streams of data generated by value chain network systems (ships, lifts, vehicles, containers, material handling systems, packaging systems, delivery systems, drones / robots, etc.), streams of data collected by mobile data collectors, etc. Configuration of the monitoring system layer 614 as a common platform or set of microservices accessed across many applications may dramatically reduce the number of interconnections needed by an owner or other operator in a value chain network to have a growing set of applications monitoring a growing set of IoT devices and other systems and devices under its control.

[0338] In embodiments, the data processing layer 608 is configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 via a value chain network-oriented data storage system layer 624, sometimes referred to herein for convenience simply as the data storage layer 624 or storage layer 624. For example, various data collected for value chain entities 652, as well as data generated by other data processing layer 608, may be stored in the data storage layer 624 such that any of the various data processing layer 608 services, applications, programs, etc., can access a common data source (which may consist of a single logical data source distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to process the vast amounts of data generated by or for value chain network entities 652 as applications 630 and value chain network uses grow and proliferate. For example, a supply chain or inventory management application of the value chain management platform 604, such as for ordering replacement parts for machinery or equipment, may have access to the same data set regarding which parts have been replaced for a set of machinery, as may a predictive maintenance application used to predict whether a ship component or port equipment is likely to need a replacement part. Similarly, predictions may also be used regarding the resupply of items.

[0339] In an embodiment, the value chain network data objects 1004 may be provided according to an object-oriented data model that defines the classes, objects, attributes, parameters, and other 64 characteristics of the set of data objects (e.g., associated with the value chain network entities 652 and applications 630) handled by the platform 604.

[0340] In embodiments, the data storage system layer 624 can provide an extremely rich environment for the collection of data that can be used to extract features or inputs for intelligent systems, such as 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 throughout this disclosure and the documents incorporated by reference herein. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from data collected or generated by or for each other. In embodiments, the data storage system layer 624 can facilitate the collection of data that can be used to extract features or inputs for intelligent systems, such as development frameworks from artificial intelligence. In examples, the collection of data can be capturing and / or storing event logs (naturally stored or ad hoc, as needed), performing periodic checks of on-board diagnostic data, or the like. In examples, pre-computation of functions can be deployed using, for example, AWS Lambda or various other cloud-based, on-demand computational capabilities, such as pre-computing, multiplexing signals, etc. In many embodiments, there are pairings (dual, triple, quadruple, etc.) of similar types of value chain entities that can use one or more capability sets of the data handling layer 608 to extend connectivity and services across the value chain entities and across the applications used by the entities, even when aggregating hundreds or thousands of data types from relatively heterogeneous entities.In these examples, various pairings of similar value chain entities, using at least in part connectivity and services across value chain entities and applications, can direct information from the connected data pairings to artificial intelligence services, including various neural networks and hybrid combinations thereof, as disclosed herein. In these examples, genetic programming techniques may be deployed to prune some of the input features of the information from the connected data pairings. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the effectiveness of decisions established by the artificial intelligence services. In these examples, information from the connected data pairings may be migrated to other layers on the platform, including those supporting or deploying robotic process automation, forecasting, prediction, and other resources, such that the shared data schema may facilitate the capabilities and resources of platform 604.

[0341] A variety of storage media and data storage types, data architectures 1002, and formats can be used to store a wide range of data types in the storage layer 624, including, but not limited to: asset and facility data 1030, state data 1140 (such as that indicating the state, condition status, or other indicators for any of the value chain network entities 652, any of the applications 630 or their components or workflows, or any of the components or elements of the platform 604), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data, and many other types); event data 1034 (including operational data, transaction data, workflow data, maintenance data, and events that occur within the value chain network 668 or with respect to one or more applications 630) or relating to a wide range of events, including process events, financial events, transactional events, output events, input events, state events, and quality data, including output events, input events, state change events, operational events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, charging events, shipping events, warehousing events, movement of goods, border crossings, movement of cargo, inspection events, supply events, and many other events; claims data 664 (such as claims data for insurance claims, such as business interruption insurance, product liability insurance, insurance for goods, premises, or equipment, flood insurance, insurance for contract-related risks, product liability, general liability, workers' compensation, injury, and other liability claims, and claims data for contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnity claims, delivery requirements, timing requirements, milestones, key performance indicators, etc.);accounting data 730 (such as data regarding the completion of contract requirements, bond satisfaction, payment of duties, etc.), and risk management data 732 (such as data regarding supply items, amounts, pricing, delivery, source of supply, routing, customs information, and many others), and many other data types related to the value chain network entities 652 and applications 630;

[0342] In an embodiment, data processing tier 608 is configured in a topology that facilitates shared adaptive capabilities, which may be provided, managed, mediated, etc. by one or more of a set of services, components, programs, systems, or capabilities in adaptive intelligence systems tier 614, conveniently referred to herein as adaptive intelligence tier 614. Adaptive intelligence systems tier 614 may include a set of data processing, artificial intelligence, and computation systems 634, which are described in more detail elsewhere in this disclosure. Thus, the use of various resources such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for a single device or a peered network), and available cloud infrastructure), data storage resources (such as local storage on a device, storage resources within or on a value chain entity or environment (including on-device storage, storage on an asset tag, local area network storage, etc.), network storage resources, cloud-based storage resources, database resources, etc.), network resources (including cellular network spectrum, wireless network resources, fixed network resources, etc.), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others), etc., can be optimized in a collaborative or shared manner for the benefit of multiple applications, programs, workflows, etc., on behalf of operators, enterprises, etc. For example, the adaptive intelligence layer 614 can manage and provide available network resources for both supply chain management applications and demand planning applications (among many other possibilities), such that low-latency resources are used for the supply chain management application (where quick decisions may be critical) and longer-latency resources are used for the demand planning application.As described in more detail throughout this disclosure and the documents incorporated herein by reference, a wide variety of adaptations may be provided for various services and capabilities across various layers 624, including those based on application requirements, quality of service, on-time delivery, service goals, budgets, costs, pricing, risk factors, operational goals, efficiency goals, optimization parameters, return on investment, profitability, uptime / downtime, worker utilization, and many others.

[0343] The value chain management platform 604, which may be referred to herein as the platform 604 for convenience, enables an operator to manage multiple aspects of a value chain network environment or entity 652 in a common application environment (e.g., utilizing 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). Output from the applications 630 of the platform 604 may be provided to other data handling layers 624. These may include, but are not limited to, state and status information for various objects, entities, processes, flows, etc.; object information such as identity, attribute, and parameter information for various classes of objects of various data types; event and change information for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including regarding yield, engagement, return on investment, profitability, efficiency, timeliness, service quality, product quality, customer satisfaction, etc.). Output from each application 630 may be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614.Thus, the cross-application nature of the platform 604 facilitates convenient configuration of all of the infrastructure elements necessary to add intelligence to any given application, such as by feeding machine learning on cross-application results, providing automation enrichment for a given application through machine learning based on results from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services, and other capabilities that optimize the control, automation, or one or more performance characteristics of one or more value chain network entities 652, or that may generally improve either the outputs and outcomes 1040 of processes and applications pursued through use of the platform 604. In some examples, the outputs and results 1040 from the various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, and the like involved in the steps of the process intended to be automated. [Data Storage Layer Details - Alternative Data Architectures]

[0344] 12 , additional details, components, subsystems, and other elements of optional embodiments of the data storage layer 624 of the platform 604 are illustrated. A variety of data architectures may be used, including traditional relational and object-oriented data architectures, a blockchain architecture 1180, an asset tag data storage architecture 1178, a local storage architecture 1190, a network storage architecture 1174, a multi-tenant architecture 1132, a distributed data architecture 1002, a value chain network (VCN) data object architecture 1004, a cluster-based architecture 1128, an event database architecture 1034, a state database architecture 1140, a graph database architecture 1124, a self-organizing architecture 1134, and other data architectures 1002.

[0345] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adapters 1110 to facilitate data storage, search access, query management, loading, extraction, normalization, and / or transformation to enable the use of various other data storage architectures 1002, such as to enable extraction from a database of one format and loading into a data system using a different protocol or data structure.

[0346] In embodiments, the value chain network-oriented data storage systems layer 624 may include, but is not limited to, physical storage systems, virtual storage systems, local storage systems (e.g., part of local storage architecture 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of network storage architecture 1174 such as those using NVME, storage-attached networks, and other network storage systems), and many others.

[0347] In an embodiment, the storage layer 624 may store data in one or more knowledge graphs (such as directed acyclic graphs, data maps, data hierarchies, data clusters with links and nodes, self-organizing maps, etc.) within the graph database architecture 1124. In an exemplary embodiment, a knowledge graph may be a powerful example of when graph databases and graph database architectures may be used. In some examples, a knowledge graph may be used to graph a workflow. For linear workflows, a directed acyclic graph may be used. For contingent workflows, a cyclic graph may be used. A graph database (e.g., the graph database architecture 1124) may include a knowledge graph, or a knowledge graph may be an example of a graph database. In an exemplary embodiment, the knowledge graph may include ontologies and connections (e.g., relationships) between the ontologies in the knowledge graph. In one example, a knowledge graph may be used to capture a human expert's articulation of a knowledge domain so that there may be identification of opportunities to design and build robotic process automation or other intelligence that can replicate this knowledge set. The platform can be used to recognize that types of experts use this factual knowledge base (from the knowledge graph) combined with capabilities replicable by artificial intelligence, which may vary depending on the type of expert involved. For example, artificial intelligence such as convolutional neural networks might be used for spatiotemporal aspects used in diagnosing problems or packing boxes in a warehouse. Meanwhile, the platform can use a different type of knowledge graph for a self-organizing map of experts whose primary task is segmenting 68 customers into customer segmentation groups. In some examples, the knowledge graph may be constructed from various data, such as job qualifications, job listings, and analysis output deliverables. In embodiments, the data storage layer 624 may store data in a digital thread, ledger, or the like, such as for maintaining a serial or other record of entities 652 over time, including any of the entities described herein.In an embodiment, the data storage layer 624 may use and enable asset tags 1178, which may include data structures associated with assets and accessible and managed, such as by using access controls, with data storage and retrieval optionally linked to local processes, but also optionally open to remote retrieval and storage options. In an embodiment, the storage layer 624 may include one or more blockchains 1180, such as those storing identity data, transaction data, past interaction data, etc., with access controls that may be role-based or based on credentials associated with value chain entities 652, services, or one or more applications 630. 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 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 associated with, generated by, or generated for any of the value chain entities and activities described herein and in the documents incorporated by reference. [Adaptive Intelligent Systems and Monitoring Layer]

[0348] 13 , which illustrates additional details, components, subsystems, and other elements of optional embodiments of platform 604. Management platform 604, in various optional embodiments, may include a set of applications 614 that enable 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 value chain network entity 652, such as any of the elements noted above and associated throughout this disclosure.

[0349] In an embodiment, the adaptive intelligent systems layer 614 may include a set of systems, components, services, and other capabilities that collectively facilitate the collaborative development and deployment of intelligent systems, such as those that can enhance one or more of the applications 630 in the application platform 604; those that can improve the performance of one or more of the components, or the overall performance of the connectivity facility 642 (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors); those that can improve other capabilities within the adaptive intelligent systems layer 614; those that improve the performance (e.g., speed / latency, energy utilization, storage capacity, storage efficiency, reliability, security, etc.) or overall performance of one or more components of the value chain network-oriented data storage system 624; those that optimize the control, automation, or one or more performance characteristics of one or more value chain network entities 652; or those that generally improve any of the outputs and results 1040 of the processes and applications pursued through use of the platform 604.

[0350] 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 set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (e.g., for setting up, provisioning, configuring, and otherwise managing sets of interactions between and among sets of value chain network entities 652 within a value chain network 668), and other systems.

[0351] In embodiments, the value chain monitoring system layer 614 and its data collection systems 640 may include a wide range of systems for data collection, including, but not limited to, real-time monitoring systems 1520 (such as on-board monitoring systems on ships and other floating assets, on delivery vehicles, on trucks and other transport assets, and event and status reporting systems at shipyards, ports, warehouses, distribution centers, and other locations; on-board diagnostics (OBD) and telematics systems on floating assets, vehicles, and equipment; systems that provide diagnostic codes and events via event buses, communication ports, or other communication systems); monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, people tracking systems, and ambient sensing systems installed in various environments where value chain activities and other events occur); systems), as well as removable and interchangeable monitoring systems such as portable and mobile data collectors, RFID and other tag readers, smartphones, tablets, and other mobile devices capable of data collection; software interaction observation systems 1500 (e.g., for recording and tracking events involving user interaction with software user interfaces, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigational actions, eye movements, finger movements, gestures, menu selections, and the like, as well as software interactions that occur as a result of other programs, such as through APIs);Mobile data collectors 1170 (as broadly described herein and in documents incorporated by reference), visual monitoring systems 1930 (such as using video and still imaging systems, LIDAR, IR, and other systems that enable visualization of entities 652 items, people, materials, components, machinery, equipment, personnel, gestures, facial expressions, location, configuration, and other factors or parameters, as well as inspection systems that monitor processes, worker activities, etc.), interaction point systems 1530 (such as dashboards, user interfaces, and control systems for value chain entities); physical process observation systems 1510 (such as physical activities of operators, workers, customers, etc.; physical activities of individuals (shippers, delivery workers, packers, pickers, assemblers, customers, merchants, vendors, distributors, etc.); physical interactions with workers, such as to track interactions between workers and physical entities such as machines and equipment, and interactions between physical entities and other physical entities, including, but not limited to, through the use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR, and other sensor sets), robotic motion tracking systems (such as tracking the movement of systems attached to humans or physical entities), and many others; machine condition monitoring systems 1940 (conditions, states, operating parameters, or other measurements of the state of any value chain entity, such as on-board or external monitors, clients, servers, cloud resources, control systems, display screens, sensors, cameras, vehicles, robots, or other machines, or their components);Sensors and cameras 1950 and other IoT data collection systems 1172 (such as on-board sensors, sensors or other data collectors (including click tracking sensors) in or about the value chain environment (e.g., but not limited to, origin locations, loading docks, vehicles or floating assets used to transport goods, containers, ports, distribution centers, storage facilities, warehouses, delivery vehicles, etc.), and destination locations), cameras for monitoring the overall environment, dedicated cameras for specific machines, processes, workers, etc., wearable cameras, portable cameras, cameras located on mobile robots, cameras on portable devices such as smartphones and tablets, and many other sensor types (including any of the many sensor types disclosed throughout this disclosure or in documents incorporated by reference herein); indoor location monitoring systems 1532 (such as cameras, IR systems, motion detection systems, beacons, RFID readers, smartphones, etc.) user feedback systems 1534 (including survey systems, touchpads, voice-based feedback systems, rating systems, facial expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavior monitoring systems 1538 (such as for monitoring movements, shopping behavior, purchasing behavior, clicking behavior, behavior indicative of fraud or deception, user interface interaction, product return behavior, behavior indicative of interest, attention, boredom, etc., behavior indicative of mood (such as fidgeting, staying still, moving closer, or changing posture), and many others); and any of a wide variety of Internet of Things (IoT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated herein by reference;

[0352] In an embodiment, the value chain monitoring system layer 614 and its data collection system 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or subsystems for searching for entities within the value chain network 668, by device identifier, by network location, by geolocation (e.g., by geofence), by indoor location (e.g., by proximity of IoT-enabled devices to known resources such as infrastructure, Wifi routers, switches, etc.), by cellular location (e.g., proximity to cellular towers), by identity management system (e.g., where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise, by an identifier assigned and / or managed by the platform 604), etc. The entity discovery 1900 may initiate a handshake between a set of devices, such as to initiate interactions that provide various applications 630 or other functionality of the platform 604.

[0353] 14, a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, etc.) for a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the artificial intelligence 1160 through a set of data processing, artificial intelligence, and computation systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in collaboration with a set of value chain applications (e.g., demand management applications 824 and supply chain applications 812). The adaptive intelligence systems 614 may include artificial intelligence, including any of various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0354] In an embodiment, the user interface may include an interface for configuring the artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and feed them to a neural network, artificial intelligence system 1160, or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated by reference herein, to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of the category of commodity 3010. In an embodiment, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as outcomes related to the value chain, category of commodity 3010, etc.

[0355] In an embodiment, providing coordinated intelligence may include providing artificial intelligence functionality, such as artificial intelligence system 1160. The artificial intelligence system may facilitate collaborative intelligence for the set of demand management applications 824 or the set of supply chain applications 812, or both, by processing data available in any of the value chain data sources, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, product design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, product lifecycle management (PLM) systems, etc.

[0356] In embodiments, user interface 3020 may provide access to, among other things, artificial intelligence capabilities, applications, systems, etc. for tailoring intelligence for value chain applications, particularly value chain applications for the category of commodity 3010. User interface 3020 may be adapted to receive information describing the category of commodity 3010 and configure user access to responsive artificial intelligence capabilities, whereby a user is guided via the user interface to artificial intelligence capabilities suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the category of commodity 3010. User interface 3020 may facilitate providing tailored intelligence comprised of artificial intelligence capabilities that provide tailored intelligence for specific operators and / or companies participating in the supply chain for the category of commodity.

[0357] In embodiments, the user interface 3020 may be configured to facilitate a user's selection and / or configuration of multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that receive, process, and generate output that may be shared between the applications. The type of artificial intelligence system 1160 may be indicated in the user interface 3020 in response to the set of connected applications or their data elements being indicated in the user interface, such as by a user hovering a pointer near the connected set of applications. In embodiments, the user interface 3020 may provide a set of functionality that facilitates access to the set of adaptive intelligence systems and facilitates the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0358] The adaptive intelligence system 614 may be configured with data processing, artificial intelligence, and computation systems 634 that may cooperate to provide collaborative intelligence, such as when the artificial intelligence systems 1160 operate on or respond to data collected or generated by other systems in the adaptive intelligence system 614, such as data processing systems. In an embodiment, providing collaborative intelligence may include employing one or more types of neural networks described herein and in the documents incorporated by reference to operate part of a set of artificial intelligence systems 1160 that process either demand management application outputs and supply chain application outputs to provide collaborative intelligence.

[0359] In an embodiment, providing collaborative intelligence for the set of demand management applications 824 includes configuring (e.g., through a user interface 3020, etc.) at least one of the adaptive intelligence systems 614 for at least one or more demand management applications 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, 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, product or service recommendation engines, etc.

[0360] Similarly, providing collaborative intelligence for a set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order of goods management application, a parts management application, and the like.

[0361] In embodiments, the management platform 102 may facilitate access, such as via a user interface 3020, to a set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence. In such embodiments, a user may seek to align supply and demand while ensuring profitability, etc. of a value chain for a category of goods 3010. By providing access to artificial intelligence capabilities 1160, the management platform allows a user to focus on the 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.

[0362] In embodiments, management platform 102 may provide, such as through user interface 3020, a set of adaptive intelligence systems 614 that provide artificial intelligence 1160 tailored to the set of demand management applications 824 and supply chain applications 812 for a category of goods 3020, for example, by (automatically) determining relationships between the demand management applications and the supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes. Artificial intelligence 1160 may be tailored by, for example, a set of data processing, artificial intelligence, and computation systems 634 available through adaptive intelligence system 614.

[0363] 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 provides coordinated intelligence for the set of demand management applications 824 and supply chain applications 812 for the category of goods 3010. The set of artificial intelligence systems 1160 can provide the coordinated intelligence such that at least one supply chain application in the set of supply chain applications 812 generates results that address at least one aspect of supply for at least one of the goods in the category of goods, as determined by at least one demand management application in the set of demand management applications 824. In an example, the behavioral tracking demand management application can generate results regarding usage behavior of goods in the category of goods 3010. The artificial intelligence system 1160 can process the behavioral data and conclude that there is a recognized need for expanded consumer access to a second good in the category of goods 3010. This tailored intelligence can be applied, optionally automatically, to a set of supply chain applications 812, for example, to allocate production resources or other resources in a commodity category's value chain to the second product. As an example, a distributor that stocks retail store shelves may receive a new inventory plan that allocates more retail store shelf space to the second product, such as by taking space away from lower-margin products.

[0364] In embodiments, the set of artificial intelligence systems 1160, etc., can provide coordinated intelligence to the set of supply chain and demand management applications, for example, by determining optional temporal prioritization of demand management application outputs that influence control of the supply chain applications so that optional temporal demand for at least one product in the category of products 3010 can be met. Seasonal adjustments in the prioritization of demand request results are an example of a temporal change. Prioritization adjustments can also be made locally. For example, a large college football team may play in its home stadium, temporarily adjusting the local supply of tailgating equipment, even though the demand management application results indicate that there is currently no demand for small propane stoves in the wider area.

[0365] The set of adaptive intelligence systems 614 that provide collaborative intelligence, such as by providing artificial intelligence capabilities 1160, may also facilitate the development and deployment of intelligence for at least one function selected from the list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. The set of adaptive intelligence systems 614 may be configured as a layer within a platform, wherein the artificial intelligence systems may operate on or respond to data collected by and / or generated by other systems (e.g., data processing systems, expert systems, machine learning systems, etc.) in the adaptive intelligence systems layer.

[0366] In addition to providing tailored intelligence configured for a particular category of goods, tailored intelligence may be provided for particular value chain entities 652, such as supply chain operators, businesses, enterprises, etc., participating in the supply chain for a category of goods.

[0367] Providing tailored intelligence can include employing a neural network to process at least one of the inputs and outputs of a set of demand management and supply chain applications. The neural network can be used in demand applications such as demand planning applications, demand forecasting applications, sales applications, future demand aggregation applications, marketing applications, advertising applications, e-commerce applications, marketing analytics applications, customer relationship management applications, search engine optimization applications, sales management applications, advertising network applications, behavior tracking applications, marketing analytics applications, location-based product or service targeting applications, collaborative filtering applications, and product or service recommendation engines. The neural network can also be used in supply chain applications such as product timing management applications, product quantity management applications, logistics management applications, shipping applications, delivery applications, ordering of goods management applications, and parts management applications. The neural network may provide coordinated intelligence by processing data available in any of multiple value chain data sources for a category of goods, including, but not limited to, processes, bills of materials, weather, traffic, design specifications, customer complaint logs, customer reviews, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, customer experience management (CEM) systems, service lifecycle management (SLM) systems, product lifecycle management (PLM) systems, etc. Neural networks configured to provide collaborative intelligence may share their adaptive capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that facilitates such shared adaptation.In embodiments, the neural network may facilitate providing available value chain / supply chain network resources to both a set of demand management applications and a set of supply chain applications. In embodiments, the neural network may provide coordinated intelligence to improve at least one of a list of outputs consisting of process outputs, application outputs, process results, application results, etc.

[0368] Referring to FIG. 15 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provides coordinated intelligence through the application of artificial intelligence, such as through the application of a hybrid artificial intelligence system 3060, and optionally through one or more expert systems, machine learning systems, etc., for use with a set of demand management applications 824 and a set of supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The hybrid adaptive intelligence system 614 can provide two types of artificial intelligence systems, Type A 3052 and Type B 3054, through a set of data processing, artificial intelligence, and computation systems 634. In an embodiment, the hybrid adaptive intelligence system 614 is selectable and / or configurable via the user interface 3020 such that one or more of the hybrid adaptive intelligence system 614 can operate on or in cooperation with a set of supply chain applications (e.g., demand management application 824 and supply chain application 812). The hybrid adaptive intelligence system 614 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities, including any of various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The hybrid adaptive intelligence system 614 may facilitate applying a first type of artificial intelligence system 1160 to a set of demand management applications 824 and a second type of artificial intelligence system 1160 to a set of supply chain applications 812, where each of the first type and second type of artificial intelligence systems 1160 may operate independently, operate cooperatively, and optionally coordinate operation to provide coordinated intelligence for the operation of a value chain that produces at least one commodity within a category of commodity 3010.

[0369] In an embodiment, user interface 3020 may include an interface for configuring hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by set of demand management applications 824 and / or set of supply chain applications 812) and feed them into at least one of two types of artificial intelligence systems of hybrid artificial intelligence system 3060, the types of which are described throughout this disclosure and in documents incorporated by reference herein, to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of category of commodity 3010. In an embodiment, the selected data sources of the value chain may be applied as inputs for classification or prediction, or as outcomes related to the value chain, category of commodity 3010, etc.

[0370] In an embodiment, the hybrid adaptive intelligence system 614 provides a plurality of separate artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof. In an embodiment, any of the plurality of separate artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification adaptive neural network, a predictive adaptive neural network, etc. As an example of the hybrid adaptive intelligence system 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824, and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. As an example of the hybrid artificial intelligence system 3060, the hybrid adaptive intelligence system 614 may provide a hybrid artificial intelligence system 3060 that may include a first type of artificial intelligence applied to the demand management applications 824 and that is different from a second type of artificial intelligence applied to the supply chain applications 812. The hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems, including a plurality of first type artificial intelligences (e.g., neural networks) and at least one second type artificial intelligence (e.g., an expert system), etc. In an embodiment, the hybrid artificial intelligence system may configure a hybrid neural network that applies a first type of neural network for demand management applications 824 and a second type of neural network for supply chain applications 812. Additionally, the hybrid artificial intelligence system 3060 may provide two types of artificial intelligence for different applications, such as different demand management applications 824 (e.g., a sales management application and a demand forecasting application) or different supply chain applications 812 (e.g., a logistics management application and a production quality control application).

[0371] In embodiments, the hybrid adaptive intelligence system 614 may be applied as separate artificial intelligence capabilities to separate demand management applications 824. By way of example, collaborative intelligence via hybrid artificial intelligence capabilities may be provided to demand planning applications by feedforward neural networks, to demand forecasting applications by machine learning systems, to sales applications by self-organizing neural networks, to future demand aggregation applications by radial basis function neural networks, to marketing applications by convolutional neural networks, to advertising applications by recurrent neural networks, to e-commerce applications by hierarchical neural networks, to marketing analytics applications by probabilistic neural networks, to customer relationship management applications by associative neural networks, etc.

[0372] 16 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of forecasts 3070. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of forecasts 3070 through the application of artificial intelligence, such as through the application of artificial intelligence systems 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the set of forecasts 3070 through a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of coordinated value chain applications. The adaptive intelligence systems 614 may include artificial intelligence systems that provide artificial intelligence capabilities known to be associated with artificial intelligence, including any of a variety of expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The adaptive intelligence system 614 may facilitate applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a forecast set 3070 that may facilitate coordinating the two sets of value chain applications, or at least may facilitate coordinating at least one demand management application and at least one supply chain application from each set.

[0373] In an embodiment, the forecast set 3070 includes at least one forecast of an impact on a supply chain application based on the current state of an adjusted demand management application, such as a forecast that demand for a good will decrease more quickly than previously predicted. In an embodiment, the set of forecasts 3070 is a set of forecasts of adjustments in supply needed to meet demand. Other forecasts include at least one forecast of a change in demand that will affect supply. Further, other forecasts in the forecast set predict changes in supply that will affect at least one of the set of demand management applications, such as a promotion application for at least one product in a category of products. A forecast in the forecast set may be as simple as setting the likelihood that the supply of a product in a category of products will not meet the demand set by a demand setting application.

[0374] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing a set of predictions for a coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict fault or problem conditions in demand management applications, such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict problem conditions based on a collection of machine operation information and machine preventive maintenance information for machines that perform value chain operations (e.g., production machines, automated handling machines, packaging machines, shipping machines, etc.).

[0375] In an embodiment, the set of predictions 3070 may be provided directly by the management platform 102 through a set of adaptive artificial intelligence systems.

[0376] In an embodiment, the forecast set 3070 may be provided for a tuned set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities to tune the set of demand management applications and supply chain applications.

[0377] In an embodiment, the forecast set 3070 may be a forecast of outcomes for operating a value chain using an adjusted set of demand management applications and supply chain applications for a category of goods, and a user may run test cases of the adjusted sets of demand management applications and supply chain applications to determine which sets are likely to produce desirable outcomes (viable candidates for an adjusted set of applications) and which sets are likely to produce undesirable outcomes.

[0378] 17 , a management platform for an information technology system, such as a management platform for a goods and / or services value chain, is depicted as a block diagram of functional elements and representative interconnections for providing a set of taxonomies 3080. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide the set of taxonomies 3080 through the application of artificial intelligence, such as by the application of artificial intelligence systems 1160, and optionally through one or more expert systems, machine learning systems, etc., for use with a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced, sold, resold, rented, leased, transferred, serviced, recycled, renewed, enhanced, etc., throughout the value chain. The adaptive intelligence systems 614 can provide the set of taxonomies 3080 through a set of data processing, artificial intelligence, and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the set of coordinated value chain applications. The adaptive intelligence systems 614 may include artificial intelligence systems that provide classification capabilities through, among other things, any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.The adaptive intelligence system 614 may facilitate applying adaptive intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812, such as by generating a set of classifications 3080 that may facilitate coordinating the two sets of value chain applications, or at least may facilitate coordinating at least one demand management application and at least one supply chain application from each set.

[0379] In an embodiment, the set of categories 3080 includes at least one classification of the current state of a supply chain application for use by the adjusted demand management application, such as a classification of a problem condition that may affect the operation of a demand management application, such as a marketing application. Such classifications are useful in determining how to adjust market expectations for a commodity that is likely to have a lower yield than previously expected. The converse may also be true in that the set of categories 3080 includes at least one classification of the current state of a demand management application and its relationship to the adjusted supply chain application. In an embodiment, the set of categories 3080 is a set of classifications of adjustments in supply needed to meet demand; for example, an adjustment to production labor needs would be classified differently from an adjustment to a third-party logistics provider. Another classification may include at least one classification of a perceived change in demand and the resulting potential impact on supply management. Additionally, another classification in the set of categories may include the impact of a supply chain application on at least one of the set of demand management applications, such as a promotional application for at least one commodity in a commodity category. A classification in the set of categories may be as simple as classifying the likelihood that the supply of a commodity in a commodity category will not meet the demand set by a demand setting application.

[0380] In embodiments, the adaptive intelligence system 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 for a coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to classify fault or problem conditions of demand management applications, such as classifying a lack of sufficient validated feedback. The probabilistic neural network may be used to classify problem conditions of machines that perform value chain operations (e.g., production machines, automated processing machines, packaging machines, shipping machines, etc.) as related to at least one of machine operational information and preventive maintenance information.

[0381] In an embodiment, the set of classifications 3080 may be provided directly by management platform 102 through a set of adaptive artificial intelligence systems. Additionally, the set of classifications 3080 may be provided to a coordinated set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities to coordinate the set of demand management applications and supply chain applications.

[0382] In an embodiment, the set of classifications 3080 may be a classification of results for operating a value chain using a tailored set of demand management applications and supply chain applications for a category of goods, allowing a user to run test cases of the tailored set of demand management applications and supply chain applications to determine which sets may produce results classified as desirable (e.g., viable candidates for the tailored set of applications) and results classified as undesirable.

[0383] In embodiments, the set of classifications may be comprised of a set of adaptive intelligence functions, such as neural networks, that may be adapted to classify information related to categories of goods. As an example, the neural network may be a multi-layer feed-forward neural network.

[0384] In an embodiment, performing the classification may include classifying the discovered value chain entities as one of demand centric and supply centric.

[0385] In an embodiment, the set of classifications 3080 may be achieved through the use of the artificial intelligence system 1160 to coordinate the set of coordinated demand management applications and supply chain applications. The artificial intelligence system may configure and generate the set of classifications 3080 as a means by which the demand management applications and supply chain applications can be coordinated. In one example, classifications of information flows throughout a value chain may be classified as relevant to both demand management applications and supply chain applications, and this common association may be a point of coordination between the applications. In an embodiment, the set of classifications may be artificial intelligence-generated classifications resulting from operating a supply chain that relies on the coordinated demand management applications 824 and supply chain applications 812.

[0386] Referring to FIG. 18 , a management platform for an information technology system, such as a management platform for a goods and / or service value chain, is depicted as a block diagram of functional elements and representative interconnections for achieving automated control intelligence. The management platform includes, among other things, a user interface 3020 that provides a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide automated control signaling 3092 to a coordinated set of demand management applications 824 and supply chain applications 812 for categories of goods 3010 that may be produced and sold through the value chain. The adaptive intelligence systems 614 can provide the automated control signals 3092 via a set of data processing, artificial intelligence, and computational systems 634. In an embodiment, the adaptive intelligence systems 614 are selectable and / or configurable via the user interface 3020 such that one or more of the adaptive intelligence systems 614 can automatically control a set of supply chain applications (e.g., the demand management applications 824 and the supply chain applications 812). The adaptive intelligence system 614 may include artificial intelligence, including any of a variety of 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.

[0387] In embodiments, the user interface 3020 may include an interface for configuring the adaptive intelligence system 614 to take inputs from selected data sources of the value chain 3094 (such as data sources used by the coordinated set of demand management applications 824 and / or the set of supply chain applications 812) and feed them to a neural network, or the like, to the artificial intelligence system 1160, or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated by reference herein, to generate automated control signals 3092, for example, to enhance, control, improve, optimize, configure, adapt, or otherwise influence the value chain of the category of goods 3010. In embodiments, the selected data sources of the value chain may be used to determine aspects of the automated control signals, such as temporal adjustments to control outcomes associated with the value chain for at least the category of goods 3010.

[0388] In one example, the set of automated control signals can include at least one control signal for automating the execution of a supply chain application, such as a production start, automated material ordering, inventory check, or billing application, in a coordinated set of demand management applications and supply chain applications. In yet another example of automated control signal generation, the set of automated control signals can include at least one control signal for automating the execution of a demand management application, such as a product recall application, an email distribution application, or the like, in a coordinated set of demand management applications and supply chain applications. In yet another example, the automated control signal can control the timing of a demand management application based on commodity supply conditions.

[0389] In embodiments, the adaptive intelligence system 614 can apply machine learning to the results of supply chain management to automatically adapt the set of demand management application control signals. Similarly, the adaptive intelligence system 614 can apply machine learning to the results of demand management to automatically adapt the set of supply chain application control signals. The adaptive intelligence system 614 can provide further processing for automatic control signal generation, such as by applying artificial intelligence to determine aspects of the value chain that affect the automated control of the coordinated set of demand management and supply chain applications for a category of goods. 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 targeted demand management and supply chain applications.

[0390] For example, automated controls of a supply chain application may be limited by policies, operational limits, safety constraints, etc. A set of adaptive intelligence systems may determine ranges of supply chain application control values ​​within which the controls may be automated. In embodiments, the ranges may be associated with supply rates, supply timing rates, mix of products within a category of products, etc.

[0391] Embodiments are described herein for using artificial intelligence systems or capabilities to identify, configure, and adjust automated control signals. Such embodiments may further include a closed loop of feedback (e.g., status information, output information, outcomes, etc.) from a coordinated set of demand management and supply chain applications, optionally processed using machine learning, and used to adapt the automated control signals for at least one commodity within a category of commodities. The automated control signals may be adapted based on an indication of feedback from the supply chain applications that, for example, the yield ...

Claims

1. A robot fleet resource provision system, comprising:

1. A computer readable storage system, comprising: a fleet resource data store that maintains a fleet resource inventory that indicates a number of fleet resources that can be provided as a set of fleet resources, and for each fleet resource, indicates a set of resource characteristics, a configuration requirement for the resource, and a respective status of the resource; and a storage system for storing a set of resource provisioning rules accessible to the intelligence layer to verify that the provided resources comply with the provisioning rules; A set of one or more processors that execute a set of computer readable instructions, Request a job from the robot fleet, determining a job definition data structure based on the request, the job definition data structure defining a set of tasks to be performed in executing the job; determining a robot fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory, the robot fleet configuration data structure assigning a plurality of resources selected from the fleet resource inventory to the set of tasks defined in the job definition data structure; determining a respective provisioning configuration for each fleet resource based on each task to which the fleet resource is assigned, a set of characteristics of the fleet resource, configuration requirements of the fleet resource, and a respective status of the fleet resource; Provisioning each of the fleet resources based on their provisioning settings and provisioning rules; Deploying a fleet of robots to perform the work; A system including: a set of processors, the set of one or more processors collectively executing.

2. The robot fleet resource provision system of claim 1 , wherein each status of a resource includes a general availability of the resource.

3. 2. The robot fleet resource provision system of claim 1, wherein determining the robot fleet configuration data structure is further based on a job environment.

4. 2. The robot fleet resource provision system of claim 1, wherein determining the robot fleet configuration data structure is further performed based on a job budget.

5. 2. The robot fleet resource provisioning system of claim 1, wherein determining the robot fleet configuration data structure is further based on a timeline for completing a job.

6. 2. The robot fleet resource provision system of claim 1, wherein the fleet resource inventory includes one or more types of robots, and determining the robot fleet configuration data structure is further based on an available inventory of one or more types of robots.

7. 2. The robotic fleet resource provisioning system of claim 1, wherein determining a provisioning configuration for each respective fleet resource is further based on a job environment.

8. 2. The robot fleet resource provision system of claim 1, wherein determining a provision configuration for each respective fleet resource is further performed based on a job budget.

9. 2. The robotic fleet resource provisioning system of claim 1, wherein determining a provisioning configuration for each assigned fleet resource is further based on a timeline for completing a job.

10. 2. The robotic fleet resource provision system of claim 1, wherein the fleet resource inventory includes a computing resource selected from a list of computing resources including an on-robot computing resource, a robotic operating unit-local fleet control computing resource, a cloud-based computing resource, a computing module, or a computing chip.

11. The robot fleet resource provision system of claim 1 , wherein providing each fleet resource includes providing one or more of a software robot module or a hardware robot module.

12. A robot fleet resource provision system as described in claim 11, wherein the hardware robot module is a replaceable module.

13. The robot fleet resource provision system of claim 1 , wherein the fleet resource inventory includes a plurality of digital resources.

14. 14. The robotic fleet resource provision system of claim 13, wherein providing each one of the plurality of digital resources includes one or more of a software update push, a resource access credential, or a fleet resource data storage configuration, allocation, or utilization.

15. 2. The robotic fleet resource provision system of claim 1, wherein providing each fleet resource includes providing consumable resources sourced from at least one of a specialized supply chain, a job requester resource supply, a fleet-specific stockpile, a job-specific stockpile, or a fleet team-specific stockpile.

16. 10. The robotic fleet resource provisioning system of claim 1, wherein provision of each fleet resource is based on one or more characteristics of a target operating environment.

17. 20. The robotic fleet resource provisioning system of claim 16, wherein the target operating environment is one or more of land, sea, underwater, in flight, underground, and sub-freezing ambient temperatures.

18. The robotic fleet resource provision system of claim 1 , wherein providing each fleet resource includes 3D printing each resource for provision.

19. The robot fleet resource provision system of claim 1 , wherein the provision of each fleet resource is based on the terms of a smart contract that constrains the provision of the fleet resource.

20. 2. The robot fleet resource provision system of claim 1, wherein the fleet resource inventory includes platform resources, and providing each fleet resource includes providing at least one platform resource selected from a list of platform resources including a computing resource, a fleet configuration system, a platform intelligence layer, a platform data processing system, and a fleet security system.

21. 21. The robot fleet resource provisioning system of claim 20, wherein determining the robot fleet configuration data structure is further based on a negotiated fee for providing the platform resource.

22. The robot fleet resource provision system of claim 20 , wherein determining the robot fleet configuration data structure includes a negotiation workflow for accepting a job request.

23. 2. The robotic fleet resource provision system of claim 1, wherein providing each fleet resource includes providing one or more fleet resources identified in a job execution plan.

24. 2. The robotic fleet resource provisioning system of claim 1, wherein providing each fleet resource includes providing the robot with one or more of an attachment, a sensor set, a chip set, and a motion adapter based on at least one task in a set of target tasks for the robot identified in the job execution plan.

25. 2. The robot fleet resource provision system of claim 1, wherein providing each fleet resource includes analyzing a job execution plan that defines resources for the fleet of robots to perform at least one task.

26. 2. The robotic fleet resource provision system of claim 1, wherein the set of one or more processors cooperate with at least one of a fleet configuration system, a fleet resource scheduling system, a fleet security system, and a fleet utilization system to execute the set of computer readable instructions.

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