Methods and systems for improving machines and systems that automate the execution of distributed ledger and other transactions in spot and futures markets for energy, computers, storage, and other resources.

Abstract

The transaction-enabling system includes a production equipment having a core task that is a production task. The system includes a controller having an equipment description circuit configured to interpret a number of past equipment parameter values ​​and a corresponding number of past equipment outcome values, and an equipment prediction circuit operating an adaptive learning system configured to train an equipment production predictor responsive to the plurality of equipment parameter values ​​and the corresponding plurality of equipment outcome values. The equipment description circuit further interprets a number of current-state equipment parameter values, and the equipment prediction circuit further operates the adaptive learning system to predict a current-state equipment outcome value responsive to the plurality of current-state equipment parameter values.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the benefit of priority of the following U.S. Provisional Patent Applications: "Methods and Systems for Improving Machines and Systems that Automate the Execution of Distributed Ledgers and Other Transactions in Spot and Futures Markets for Energy, Computers, Storage, and Other Resources" filed on December 31, 2018, Serial No. 62 / 787,206 (Attorney Docket No. SFTX - 0001 - P01); "Methods and Systems for Improving Machines and Systems that Automate the Execution of Distributed Ledgers and Other Transactions in Spot and Futures Markets for Energy, Computers, Storage, and Other Resources" filed on May 6, 2018, Serial No. 62 / 667,550 (Attorney Docket No. SFTX - 0002 - P01); and "Methods and Systems for Improving Machines and Systems that Automate the Execution of Distributed Ledgers and Other Transactions in Spot and Futures Markets for Energy, Computers, Storage, and Other Resources" filed on October 29, 2018, Serial No. 62 / 751,713 (Attorney Docket No. SFTX - 0003 - P01).

[0002] Each of the foregoing applications is hereby incorporated by reference in its entirety.

Background Art

[0003] There is an increasing number of machines and automated agents involved in market activities, including data collection, prediction, planning, execution of transactions, and other activities. This includes increasingly high - performance systems such as those used in high - speed trading. There is a need for methods and systems to improve the machines enabling the market, including improvements in efficiency, speed, reliability, etc. for participants in such markets.

[0004] Many markets are becoming more decentralized rather than centralized, with distributed ledgers like blockchain, peer-to-peer interaction models, and microtransactions replacing or complementing traditional models involving centralized authorities and intermediaries. There is a need for improved machines that can handle distributed transactions at scale among a large number of participants, including human participants and automated agents.

[0005] Operations on blockchains, such as those involving cryptocurrencies, are increasingly involving energy-intensive computations, such as calculating extremely large hash functions for the ever-growing chain of blocks. Furthermore, in systems utilizing proof-of-work and proof-of-stake, "mining," which involves deploying massive amounts of computer processing power, is becoming commonplace to perform calculations that support the collective trust of transactions recorded on the blockchain.

[0006] Many applications of artificial intelligence also require energy-intensive computing operations, such as very large neural networks with a vast number of interconnections performing calculations on numerous inputs to generate one or more outputs, including prediction, classification, optimization, and control outputs.

[0007] Furthermore, the growth of the Internet of Things and cloud computing platforms has led to a proliferation of devices, applications, and the connections between them, and data centers housing servers and other IT components consume a significant portion of energy in the United States and other developed countries.

[0008] As a result, energy consumption has become a major factor in the utilization of computing resources, and energy resources and computing resources (or simply "energy and computing") are beginning to converge from various perspectives, including re-procurement, purchasing, provisioning, configuration, and management of inputs, activities, and outputs. For example, there are projects underway to place large-scale computing resource facilities, such as Bitcoin® and other cryptocurrency mining operations, near large hydroelectric power plants like Niagara Falls.

[0009] A major challenge for facility owners and operators is the uncertainty surrounding facility optimization, stemming from volatility in the cost and availability of inputs (especially when involving unreliable renewable resources), fluctuations in the cost and availability of computing and network resources (such as when network performance varies), and volatility and uncertainty in the various end markets to which energy and computing resources are applied (e.g., cryptocurrency volatility, energy market volatility, pricing volatility in various other markets, and uncertainty in the usefulness of artificial intelligence in a wide range of applications).

[0010] For example, there is a need for flexible and intelligent energy and computing equipment, as well as intelligent energy and computing resource management systems, that adapt to uncertainty and volatility, including the ability to collect, store, and process data; automatically configure inputs, resources, and outputs; and train artificial intelligence / machine learning systems to learn from a training set of equipment results, equipment parameters, and data collected from data sources, and to optimize various relevant parameters for such equipment. [Overview of the project]

[0011] Machine learning will develop understanding based on IoT data, social network data, and other non-traditional data sources, potentially enabling automated markets or machines that interact with automated markets to execute transactions based on predictions, such as participating in futures markets for energy, computing, and advertising. Blockchain and cryptocurrencies have the potential to support a variety of automated transactions, and the intersection of blockchain and AI potentially enables a fundamentally different transaction infrastructure. As energy used for computing increases, machines will be able to efficiently allocate available energy sources between storage, computing, and base tasks. These and other concepts will be addressed by the methods and systems disclosed herein.

[0012] This disclosure describes a transaction-enabling system including a smart contract wrapper, and a contract wrapper in one non-limiting embodiment disclosed herein may be configured to access a distributed ledger including a plurality of embedded contract conditions and a plurality of data values, interpret access requests for the plurality of data values, and, in accordance with the access requests, provide access to at least some of the plurality of data values, and commit the entity providing the access requests to at least one of the plurality of embedded contract conditions.

[0013] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple data values ​​include intellectual property data corresponding to multiple intellectual property (IP) assets, or situations in which multiple embedded contract terms include multiple intellectual property license terms corresponding to multiple intellectual property assets.

[0014] Further embodiments of any of the embodiments described above in this disclosure may further include a situation in which the smart contract wrapper is configured to commit an entity providing an access request value to the IP license terms corresponding to the one accessed among multiple IP assets.

[0015] Further embodiments of any of the embodiments described above in this disclosure may further include a situation in which a smart contract wrapper is configured to interpret IP description values ​​and IP append requests and to append additional IP data to a plurality of data values ​​in response to those IP description values ​​and IP append requests, wherein the additional IP data includes IP data corresponding to additional IP assets.

[0016] Further embodiments of any of the embodiments described above in this disclosure may further include situations in which multiple data values ​​further include multiple owning entities corresponding to multiple IP assets, or situations in which a smart contract wrapper is configured to allocate royalties from multiple IP assets to multiple owning entities in accordance with the corresponding IP licensing terms.

[0017] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the smart contract wrapper is further configured as follows: interprets IP description values, IP add requests, and IP add entities; adds additional IP data to multiple data values ​​in accordance with the IP description values ​​and IP add requests; commits IP add entities to IP license terms; and further allocates royalties from multiple IP assets to multiple ownership entities in accordance with the additional IP data and IP add entities.

[0018] A method for executing a smart contract wrapper for a distributed ledger may include accessing a distributed ledger containing multiple embedded contract conditions and multiple data values, interpreting access request values ​​for the multiple data values, and, depending on the access request values, providing access to at least some of the multiple data values ​​and committing the entity providing the access request values ​​to at least one of the multiple embedded contract conditions.

[0019] Further embodiments of any of the embodiments described above in this disclosure may further include situations in which an entity providing an access request value is provided with a user interface including a contract acceptance input, and in which the provision of access and the commit of the entity is performed in response to user input on the user interface.

[0020] Further embodiments of any of the embodiments described above in this disclosure may further include providing access options to a user interface and adjusting at least one of the provided access and committed contract conditions in response to user input on the user interface in response to the access options.

[0021] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple data values ​​include intellectual property (IP) data corresponding to multiple IP assets, or situations in which multiple embedded contract terms include multiple intellectual property license terms corresponding to multiple intellectual property assets.

[0022] Further embodiments of any of the embodiments described above in this disclosure may further include committing an entity providing an access request value to the corresponding IP license terms for the accessed IP asset among a plurality of IP assets.

[0023] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting IP description values ​​and IP addition requests, and adding additional IP data to a plurality of data values ​​in response to IP description values ​​and IP addition requests, wherein the additional IP data includes IP data corresponding to additional IP assets.

[0024] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which a plurality of data values ​​further include a plurality of owning entities corresponding to a plurality of IP assets, and the method further includes allocating royalties from the plurality of IP assets to the plurality of owning entities in accordance with the corresponding IP license terms.

[0025] A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting IP description values, IP addition requests, and IP addition entities, adding additional IP data to a plurality of data values according to the IP description values and IP addition requests, committing the IP addition entities to IP license conditions, and further distributing royalties from a plurality of IP assets to a plurality of ownership entities according to the additional IP data and IP addition entities.

[0026] A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating an evaluation for at least one of a plurality of IP assets, and updating an assignment of royalties from the plurality of IP assets according to the update of the evaluation for at least one of the plurality of IP assets.

[0027] A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that at least one of a plurality of IP assets has expired, and updating an assignment of royalties from the plurality of IP assets according to the determination that at least one of the plurality of IP assets has expired.

[0028] A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that an ownership entity corresponding to at least one of a plurality of IP assets has changed, and updating an assignment of royalties from the plurality of IP assets according to the change of the ownership entity.

[0029] A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a user interface to a new ownership entity of at least one IP asset whose ownership has changed among the plurality of IP assets, and committing IP license conditions to the new ownership entity according to user input on the user interface.

[0030] The present disclosure describes a transactionalization system including a smart contract trapper, and a smart contract trapper according to a disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a plurality of intellectual property (IP) license conditions corresponding to a plurality of IP assets (where the plurality of IP assets includes an IP stack), interpret IP description values and IP addition requests, and add IP assets to the IP stack according to the IP addition requests and IP description values.

[0031] A further embodiment of any of the foregoing embodiments of the present disclosure may further include a situation where the smart contract trapper is further configured to interpret an IP license value corresponding to the IP description value and add the IP license value to the plurality of IP license conditions according to the IP description value and the IP addition request.

[0032] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the smart contract trapper is further configured to associate at least one of the plurality of IP license conditions with the added IP assets.

[0033] A further embodiment of any of the foregoing embodiments of the present disclosure may further include a data store having at least one copy of the IP assets stored therein, where the IP stack further includes a reference to the data store for at least one of the IP assets.

[0034] A method according to a disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a plurality of intellectual property (IP) license conditions corresponding to a plurality of IP assets (where the plurality of IP assets includes an IP stack), interpreting IP description values and IP addition requests, and adding IP assets to the IP stack according to the IP addition requests and IP description values.

[0035] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting IP license values ​​corresponding to IP description values ​​and adding IP license values ​​to a plurality of IP license conditions in response to IP description values ​​and IP addition requests.

[0036] Further embodiments of any of the embodiments described above in this disclosure may further include associating an additional IP asset with at least one of a plurality of IP license terms.

[0037] Further embodiments of any of the embodiments described above in this disclosure may further include storing at least one IP asset in a data store, wherein the IP collection stack includes a reference to at least one IP asset stored in the data store.

[0038] Further embodiments of any of the embodiments described above in this disclosure may further include allocating royalties from multiple IP assets to multiple owning entities corresponding to a collective stack, depending on the corresponding IP licensing terms.

[0039] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting IP addition entities corresponding to IP addition requests and IP description values, and committing IP addition entities to IP license terms.

[0040] Further embodiments of any of the embodiments described above in this disclosure may further include allocating royalties from multiple IP assets to multiple owning entities, depending on the additional IP entity.

[0041] Further embodiments of any of the embodiments described above in this disclosure may further include updating the valuation of at least one of the multiple IP assets, and updating the royalty allocation from the multiple IP assets in response to the update of the valuation of at least one of the multiple IP assets.

[0042] Further embodiments of any of the embodiments described above in this disclosure may further include determining that at least one of a plurality of IP assets is expired, and, in response to the determination that at least one of the plurality of IP assets is expired, renewing the royalty allocation from the plurality of IP assets.

[0043] Further embodiments of any of the embodiments described above in this disclosure may further include determining that the ownership entity corresponding to at least one of a plurality of IP assets has changed, and updating the royalty allocation from the plurality of IP assets in accordance with the change in ownership entity.

[0044] Further embodiments of any of the embodiments described above in this disclosure may further include providing a user interface to the new owning entity of at least one IP asset whose ownership has been changed among a plurality of IP assets, and committing IP license terms to the new owning entity in response to user input on the user interface.

[0045] This disclosure describes a transaction-enabling system including a controller, the controller in one non-exclusive embodiment disclosed herein may be configured as follows: accessing a distributed ledger including an instruction set, tokenizing the instruction set, interpreting instruction set access requests, and providing certifiable access to the instruction set in response to instruction set access requests.

[0046] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes an instruction set for a coating process.

[0047] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for the operation of a 3D printer.

[0048] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a semiconductor manufacturing process.

[0049] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes an instruction set for a field-programmable gate array (FPGA).

[0050] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes a food preparation instruction set.

[0051] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for polymer manufacturing.

[0052] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes a set of instructions for chemical synthesis.

[0053] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes an instruction set for biological production.

[0054] Further embodiments of any of the aforementioned embodiments of this disclosure may include situations in which the instruction set includes an instruction set for a crystal fabrication system.

[0055] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to interpret execution operations of an instruction set and to record transactions on a distributed ledger in response to the execution operations.

[0056] The method may include accessing a distributed ledger containing the instruction set, tokenizing the instruction set, interpreting instruction set access requests, and providing certifiable access to the instruction set in response to instruction set access requests.

[0057] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes an instruction set for a coating process.

[0058] Further embodiments of any of the above embodiments of the present disclosure may further include providing commands to a production tool for a coating process in response to an access request for an instruction set.

[0059] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0060] Further embodiments of any of the aforementioned embodiments of this disclosure may include situations in which the instruction set includes an instruction set for a 3D printing process.

[0061] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing commands to production tools for a 3D printing process in response to an instruction set access request.

[0062] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0063] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a semiconductor manufacturing process.

[0064] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing commands to production tools for a semiconductor manufacturing process in response to an instruction set access request.

[0065] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0066] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a field-programmable gate array (FPGA).

[0067] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting execution operations of the FPGA instruction set and recording transactions in a distributed ledger in response to the execution operations.

[0068] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a food preparation process.

[0069] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing commands to a production tool for a food preparation process in response to an instruction set access request.

[0070] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0071] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a polymer manufacturing process.

[0072] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing commands to a production tool for a polymer manufacturing process in response to an instruction set access request.

[0073] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0074] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a chemical synthesis process.

[0075] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing commands to a production tool for a chemical synthesis process in response to an instruction set access request.

[0076] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0077] Further embodiments of any of the embodiments described herein may include situations in which the instruction set includes an instruction set for a biological production process.

[0078] Further embodiments of any of the aforementioned embodiments of the present disclosure may further include providing commands to a production tool for a biological production process in response to an instruction set access request.

[0079] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0080] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the instruction set includes an instruction set for a crystal manufacturing process.

[0081] Further embodiments of any of the above-described embodiments of the present disclosure may further include providing commands to a production tool for a crystal manufacturing process in response to an instruction set access request.

[0082] Further embodiments of any of the embodiments described above in this disclosure may further include recording transactions on a distributed ledger in response to the provision of commands to a production tool.

[0083] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting execution operations of an instruction set and recording transactions on a distributed ledger in response to the execution operations.

[0084] This disclosure describes a transaction-enabling system including a controller, the controller in one non-exclusive embodiment disclosed herein, which may be configured to access a distributed ledger including executable algorithmic logic, to tokenize executable algorithmic logic, to interpret access requests to executable algorithmic logic, and to provide certifiable access to the executable algorithmic logic in response to access requests.

[0085] Further embodiments of any of the above embodiments of the present disclosure may include a scenario in which the controller is further configured to provide executable algorithmic logic as a black box, wherein the instruction set further includes an interface description for the executable algorithmic logic.

[0086] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to interpret execution operations of executable algorithmic logic and to record transactions in a distributed ledger in response to the execution operations.

[0087] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the executable algorithm logic further includes an application programming interface (API) for the executable algorithm logic.

[0088] This disclosure describes a method, and such method, according to one non-exclusive embodiment disclosed herein, may include accessing a distributed ledger containing executable algorithmic logic, tokenizing the executable algorithmic logic, interpreting access requests to the executable algorithmic logic, and providing provable access to the executable algorithmic logic in response to access requests.

[0089] Further embodiments of any of the above embodiments of the present disclosure may further include providing an interface description for executable algorithmic logic.

[0090] Further embodiments of any of the above embodiments of the present disclosure may further include providing an application programming interface (API) for executable algorithmic logic.

[0091] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting execution operations of executable algorithmic logic and recording transactions on a distributed ledger in response to the execution operations.

[0092] This disclosure describes a transaction-enabling system including a controller, the controller in one non-limiting embodiment disclosed herein, which may be configured to access a distributed ledger including firmware data values, to tokenize such firmware data values, to interpret access requests to the firmware data values, and to provide certifiable access to the firmware corresponding to the firmware data values ​​in response to access requests.

[0093] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to provide a notification to an accessor of a firmware data value in response to an update of the firmware data value.

[0094] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller interprets either a download operation or an installation operation of a firmware asset corresponding to a firmware data value, and records a transaction in a distributed ledger in response to either the download operation or the installation operation.

[0095] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the firmware data value includes firmware for a component of a production process.

[0096] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the components of the production process include production tools.

[0097] Further embodiments of any of the above embodiments of the present disclosure may include situations in which the production tool includes a production tool for a process selected from the following processes: coating processes, 3D printing processes, semiconductor manufacturing processes, food preparation processes, polymer manufacturing processes, chemical synthesis processes, biological production processes, and crystal manufacturing processes.

[0098] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the firmware data value includes firmware for either computing resources or network resources.

[0099] This disclosure describes a method, and such method, according to one non-limiting embodiment disclosed herein, may include accessing a distributed ledger containing firmware data values, tokenizing such firmware data values, interpreting access requests to the firmware data values, and providing certifiable access to the firmware corresponding to the firmware data values ​​in response to access requests.

[0100] Further embodiments of any of the embodiments described above in this disclosure may further include providing a notification to the accessor of the firmware data value in response to an update of the firmware data value.

[0101] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting a download operation of a firmware asset corresponding to a firmware data value, and recording a transaction on a distributed ledger in response to the download operation.

[0102] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting the installation operation of a firmware asset corresponding to a firmware data value, and recording a transaction on a distributed ledger in response to the installation operation.

[0103] This disclosure describes a transaction-enabling system including a controller, the controller in one non-exclusive embodiment disclosed herein, which may be configured to access a distributed ledger including serverless code logic, to tokenize the serverless code logic, to interpret access requests to the serverless code logic, and to provide certifiable access to the serverless code logic in response to access requests.

[0104] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the controller is further configured to provide serverless code logic as a black box, wherein the serverless code logic further includes an interface description for the serverless code logic.

[0105] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to interpret execution operations of serverless code logic and to record transactions on a distributed ledger in response to the execution operations.

[0106] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to interpret execution operations of serverless code logic and to record transactions on a distributed ledger in response to the execution operations.

[0107] Further embodiments of any of the embodiments described herein may include a scenario in which the instruction set further includes an application programming interface (API) for serverless code logic.

[0108] This disclosure describes a method, and such method, according to one non-limiting embodiment disclosed herein, may include accessing a distributed ledger containing serverless code logic, tokenizing the serverless code logic, interpreting access requests to the serverless code logic, and providing certifiable access to the serverless code logic in response to access requests.

[0109] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing an interface description for serverless code logic.

[0110] Further embodiments of any of the aforementioned embodiments of this disclosure may further include providing an application programming interface (API) for serverless code logic.

[0111] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting execution operations of serverless code logic and recording transactions on a distributed ledger in response to the execution operations.

[0112] This disclosure describes a transaction-enabling system including a controller, which, in one non-exclusive embodiment disclosed herein, may be configured to interpret access requests to an aggregated dataset in order to access a distributed ledger containing an aggregated dataset, and to provide certifiable access to the aggregated dataset in response to such access requests, wherein certifiable access includes at least one of which party has accessed the aggregated dataset and how many parties have accessed the aggregated dataset.

[0113] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the distributed ledger includes a blockchain, or in which the aggregated dataset includes either a trade secret or proprietary information.

[0114] Further embodiments of any of the above embodiments of the present disclosure may further include an expert wrapper for a distributed ledger, wherein the expert wrapper is configured to tokenize an aggregated dataset and to verify one of the following: trade secrets and proprietary information.

[0115] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the distributed ledger includes a set of instructions, and a controller is further configured to interpret instruction update values ​​and update the instruction set in response to access requests and instruction update values.

[0116] Further embodiments of any of the above-described embodiments of the present disclosure may further include a smart wrapper for a distributed ledger, which is configured to assign a plurality of instruction subsets to the distributed ledger as an aggregated dataset and to manage access to the plurality of instruction subsets in response to access requests.

[0117] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to interpret an access to one of a subset of instructions and to record a transaction on a distributed ledger in response to that access.

[0118] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the controller is further configured to interpret an execution operation of one of a subset of instructions and to record the transaction in a distributed ledger as it is accessed.

[0119] This disclosure describes a method, which, in accordance with one non-exclusive embodiment disclosed herein, includes accessing a distributed ledger containing an aggregated dataset, interpreting access requests to the aggregated dataset, and providing certifiable access to the aggregated dataset in response to access requests, wherein such certifiable access includes at least one of which party accessed the aggregated dataset and how many parties accessed the aggregated dataset.

[0120] Further embodiments of any of the above embodiments of the present disclosure may further include operating an expert wrapper for a distributed ledger, wherein the expert wrapper is configured to tokenize an aggregated dataset and to verify at least one of the trade secrets or proprietary information of the aggregated dataset.

[0121] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the distributed ledger further includes a set of instructions, and the method further includes interpreting an instruction update value and updating the instruction set in response to an access request and the instruction update value.

[0122] Further embodiments of any of the embodiments described above in this disclosure may further include allocating multiple instruction subsets as aggregated datasets to a distributed ledger, and managing access to the multiple instruction subsets in response to access requests.

[0123] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting an access to one of a plurality of instruction subsets and recording a transaction on a distributed ledger in response to that access.

[0124] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting an execution operation of one of a plurality of instruction subsets and recording a transaction on a distributed ledger in response to access.

[0125] This disclosure describes a transaction-enabling system including a controller, which, according to one non-exclusive embodiment disclosed herein, accesses a distributed ledger containing multiple intellectual property (IP) data corresponding to multiple IP assets, wherein the multiple IP assets include a stack of IPs, tokenizes the IP data, interprets a distributed ledger operation corresponding to at least one of the multiple IP assets, determines analytical results in accordance with the distributed ledger operation and the tokenized IP data, and provides a report of such analytical results.

[0126] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a distributed ledger operation includes at least one operation selected from a plurality of operations, which include accessing IP data corresponding to one of a plurality of IP assets, performing a process that utilizes IP data corresponding to one of a plurality of IP assets, adding IP data corresponding to an additional IP asset to the IP aggregate stack, and deleting IP data corresponding to one of a plurality of IP assets.

[0127] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the analysis result value includes at least one result value selected from a plurality of result values, which consist of the number of access events corresponding to at least one of a plurality of IP assets, statistical information corresponding to the access events for the plurality of IP assets, the distribution of the plurality of IP assets according to the access event rate, one of the access time or processing time corresponding to at least one of the plurality of IP assets, and access events of unique entities corresponding to at least one of the plurality of IP assets.

[0128] This disclosure describes a method, and such method, according to one non-limiting embodiment disclosed herein, may include accessing a distributed ledger containing multiple IP data corresponding to multiple IP assets (wherein multiple IP assets include a stack of IPs), tokenizing the IP data, interpreting a distributed ledger operation corresponding to at least one of the multiple IP assets, identifying analytical results values ​​in relation to the distributed ledger operation and the tokenized IP data, and providing a report of such analytical results values.

[0129] Further embodiments of any of the embodiments described above in this disclosure may include situations in which identifying the analysis result value includes identifying the number of access events corresponding to at least one of a plurality of IP assets.

[0130] Further embodiments of any of the embodiments described above in this disclosure may include situations in which identifying the analysis result value includes identifying one of either an access time or a processing time corresponding to at least one of a plurality of IP assets.

[0131] Further embodiments of any of the embodiments described above in this disclosure may include situations in which identifying the analysis result value includes identifying the number of access events for a unique entity corresponding to at least one of a plurality of IP assets.

[0132] This disclosure describes a transaction-enabled system including a controller, the controller in one non-limiting embodiment disclosed herein, which may be configured to interpret resource utilization requirements for a task system having at least one of a compute task, a network task, or a core task; to interpret a plurality of external data sources, the plurality of external data sources including at least one data source located outside the task system; to operate an expert system to predict futures market prices for resources in accordance with the resource utilization requirements and the plurality of external data sources; and to execute transactions in the resource market in accordance with the predicted futures market prices.

[0133] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which multiple external data sources include data sources from the Internet of Things (IoT).

[0134] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0135] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectrum resource.

[0136] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple external data sources include social media data sources.

[0137] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0138] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0139] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, wherein the futures market price resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0140] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to operate an expert system to identify an alternative cost for a second resource and to execute a transaction in the resource market in accordance with that alternative cost for the second resource.

[0141] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the expert system is further configured to identify at least a portion of the replacement cost of the second resource as the operational change cost of the task system.

[0142] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0143] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include: interpreting resource utilization requirements for a task system having at least one of a computation task, a network task, or a core task; interpreting a plurality of external data sources, wherein the plurality of external data sources include at least one data source located outside the task system; operating an expert system to predict futures market prices for resources in accordance with the resource utilization requirements and the plurality of external data sources; and executing transactions in the resource market in accordance with the predicted futures market prices.

[0144] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which multiple external data sources include data sources from the Internet of Things (IoT).

[0145] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0146] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0147] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple external data sources include social media data sources.

[0148] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0149] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0150] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, wherein the futures market price resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0151] Further embodiments of any of the embodiments described above in this disclosure may further include operating an expert system to identify an alternative cost for a second resource, and executing a transaction in the resource market in accordance with the alternative cost for the second resource.

[0152] Further embodiments of any of the embodiments described above in this disclosure may further include identifying at least a portion of the replacement costs of the second resource as operational change costs for the task system.

[0153] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0154] This disclosure describes a transaction-enabled system including a controller, the controller in one non-limiting embodiment disclosed herein, which may be configured to interpret resource utilization requirements for a task system having at least one of a compute task, a network task, or a core task; interpret behavioral data sources; operate a machine to predict futures market values ​​of resources in accordance with resource utilization requirements and behavioral data sources; and adjust the operation of the task system or execute a transaction in accordance with the prediction of futures market values ​​of resources.

[0155] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for energy prices.

[0156] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an automated agent.

[0157] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0158] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0159] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for spectral resources.

[0160] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0161] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0162] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0163] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for computing resources.

[0164] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0165] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0166] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0167] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the futures market value of a resource includes a futures market for energy credit resources.

[0168] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0169] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0170] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0171] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, wherein the futures market value resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0172] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the controller is further configured to operate a machine to identify the alternative cost of a second resource and, in accordance with the alternative cost of the second resource, to adjust the operation of the task system or to execute a transaction.

[0173] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the machine is further configured to identify at least a portion of the alternative cost of the second resource as the operational change cost of the task system.

[0174] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the implementation of the above involves performing a transaction, wherein the transaction involves either purchasing or selling either the first resource or the second resource in at least one of the markets for the first resource or the second resource.

[0175] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0176] Further embodiments of any of the embodiments described above in this disclosure may include situations in which such implementation includes coordinating the operation of a task system, wherein the coordinating further includes at least one operation selected from a plurality of operations: coordinating the operation of the task system to increase or decrease resource utilization requirements; coordinating the operation of the task system to time-shift at least a portion of resource utilization requirements; coordinating the operation of the task system to replace the use of a first resource with the use of a second resource; and accessing an external provider to provide at least a portion of at least one of computing tasks, networking tasks, or core tasks.

[0177] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the implementation of the above involves performing a transaction, wherein the transaction includes either buying or selling a resource on the market for that resource.

[0178] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the market for resources includes a futures market for resources.

[0179] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the market for resources includes a spot market for resources.

[0180] This disclosure describes a method, and such method, according to one non-limiting embodiment disclosed herein, may include: interpreting resource utilization requirements for a task system having at least one of a computational task, a networking task, or a core task; interpreting behavioral data sources; operating a machine to predict futures market values ​​of resources in accordance with the resource utilization requirements and behavioral data sources; and adjusting the operation of the task system or executing a transaction in accordance with the prediction of futures market values ​​of resources.

[0181] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for energy prices.

[0182] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0183] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0184] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0185] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for spectral resources.

[0186] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0187] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0188] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0189] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market value of a resource includes a futures market for computing resources.

[0190] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0191] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0192] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0193] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the futures market value of a resource includes a futures market for energy credit resources.

[0194] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source for an automated agent.

[0195] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a human behavioral data source.

[0196] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the behavioral data source includes a behavioral data source of an entity.

[0197] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, and the futures market value resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0198] Further embodiments of any of the embodiments described above in this disclosure may further include operating the machine to identify the alternative cost of the second resource, and, depending on the alternative cost of the second resource, either adjusting the operation of the task system or executing a transaction.

[0199] Further embodiments of any of the embodiments described above in this disclosure may further include identifying at least a portion of the replacement costs of the second resource as operational change costs for the task system.

[0200] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the implementation of the above involves performing a transaction, wherein the transaction involves either purchasing or selling either the first resource or the second resource in at least one of the markets for the first resource or the second resource.

[0201] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0202] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the implementation of the above involves coordinating the operation of the task system, the coordinating further including at least one operation selected from a plurality of operations: coordinating the operation of the task system to increase or decrease resource utilization requirements; coordinating the operation of the task system to time-shift at least a portion of resource utilization requirements; coordinating the operation of the task system to replace the use of a first resource with the use of a second resource; and accessing an external provider to provide at least a portion of at least one of computing tasks, networking tasks, or core tasks.

[0203] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the implementation of the above involves performing a transaction, wherein the transaction includes either buying or selling a resource on the market for that resource.

[0204] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resource market includes a resource futures market.

[0205] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resource market includes a spot market for resources.

[0206] This disclosure describes a transaction-enabling system including a controller, the controller in one non-limiting embodiment disclosed herein, which may be configured to interpret resource utilization requirements of a task system having at least one of a compute task, a network task, or a core task; to interpret a plurality of external data sources, wherein the plurality of external data sources include at least one data source located outside the task system; to operate an expert system to predict futures market prices of resources in accordance with the resource utilization requirements and the plurality of external data sources; and to execute cryptocurrency transactions in the resource market in accordance with the predicted futures market prices.

[0207] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which multiple external data sources include data sources from the Internet of Things (IoT).

[0208] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0209] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0210] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple external data sources include social media data sources.

[0211] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0212] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0213] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, and the futures market price resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0214] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the controller is further configured to operate an expert system to identify an alternative cost for a second resource, and to execute a cryptocurrency transaction in the resource market, further in accordance with the alternative cost for the second resource.

[0215] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the expert system is further configured to identify at least a portion of the replacement cost of the second resource as the operational change cost of the task system.

[0216] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0217] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include: interpreting resource utilization requirements for a task system having at least one of a computing task, a networking task, or a core task; interpreting a plurality of external data sources, wherein the plurality of external data sources include at least one data source outside the task system; operating an expert system to predict futures market prices for resources in accordance with the resource utilization requirements and the plurality of external data sources; and executing cryptocurrency transactions in the resource market in accordance with the predicted futures market prices.

[0218] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which multiple external data sources include data sources from the Internet of Things (IoT).

[0219] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0220] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0221] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple external data sources include social media data sources.

[0222] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of network bandwidth resources.

[0223] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures market price includes the futures market price of a spectral resource.

[0224] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, and the futures market price resource includes at least one of the first resource and a second resource that can substitute for the first resource.

[0225] Further embodiments of any of the embodiments described above in this disclosure may further include operating an expert system to identify an alternative cost for a second resource, and executing a cryptocurrency transaction in the resource market in accordance with the alternative cost for the second resource.

[0226] Further embodiments of any of the embodiments described above in this disclosure may further include identifying at least a portion of the replacement costs of the second resource as operational change costs for the task system.

[0227] Further embodiments of any of the embodiments described above in this disclosure may include situations in which resource utilization requirements include at least one resource selected from a plurality of resources, comprising computing resources, network bandwidth resources, spectral resources, data storage resources, energy resources, and energy credit resources.

[0228] Further embodiments of any of the embodiments described above in this disclosure may further include operating an expert system to predict futures market prices across multiple futures market timeframes.

[0229] Further embodiments of any of the embodiments described above in this disclosure may include situations in which executing cryptocurrency transactions in resource markets in accordance with predicted futures market prices provides an improvement in the operational costs of the task system.

[0230] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource utilization requirement includes a first resource, the method further includes identifying a second resource that can substitute for the first resource, and the operation of the expert system to predict futures market prices for multiple futures market timeframes includes predicting futures market prices for both the first and second resources.

[0231] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which executing cryptocurrency transactions in resource markets in accordance with predicted futures market prices provides an improvement in the operational costs of the task system.

[0232] Further embodiments of any of the embodiments described above in this disclosure may include situations in which providing improved operational costs for a task system further includes identifying a resource utilization profile, wherein the resource utilization profile includes the utilization rates of a first resource and a second resource, respectively.

[0233] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a machine having at least one of computing task requirements, network task requirements, and energy consumption task requirements, and a controller, the controller including a resource requirements circuit configured to determine the amount of resources for the machine to service at least one of the computing task requirements, network task requirements, and energy consumption task requirements; a futures resource market circuit configured to access a futures resource market; a resource market circuit configured to access a resource market; and a resource distribution circuit configured to execute resource transactions in at least one of the resource market or futures resource market according to the determined amount of resources.

[0234] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve at least one of the machine's output or the machine's resource utilization rate.

[0235] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0236] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include computing resources.

[0237] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy resources.

[0238] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy credit resources.

[0239] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource requirements circuit is further configured to determine a second amount of a second resource for a machine to address at least one of the computation task requirements, network task requirements, and energy consumption task requirements, and the resource allocation circuit is further configured to execute a first transaction for a first resource in either a resource market or a futures resource market, and a second transaction for a second resource in either a resource market or a futures resource market.

[0240] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the second resource includes a substitute resource for the first resource for at least a portion of the time the machine is operating.

[0241] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the futures resource market includes a futures market for resources on a first time scale, and that resource market includes either a spot market for resources or a futures market for resources on a second time scale.

[0242] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a transaction includes at least one transaction type selected from a plurality of transaction types, which include the sale of a resource, the purchase of a resource, a short sale of a resource, a call option for a resource, a put option for a resource, and any of the aforementioned transactions relating to at least one of an alternative or correlated resource.

[0243] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to determine at least one of the alternative or correlated resources and to further execute at least one transaction of at least one of the alternative or correlated resources.

[0244] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to execute at least one transaction of at least one of the alternative resources or correlated resources as a substitute for a resource transaction.

[0245] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to perform at least one transaction of at least one of the alternative or correlated resources in cooperation with the resource transaction.

[0246] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include: determining a quantity of a first resource for a machine to address at least one of a computing task requirement, a network task requirement, and an energy consumption task requirement; accessing a futures resource market; accessing a resource market; and, depending on the determined quantity of the first resource, executing a transaction for the first resource in at least one of the resource market or futures resource market.

[0247] Further embodiments of any of the embodiments described above in this disclosure may further include: determining a second amount of a second resource for a machine to address at least one of a computation task requirement, a network task requirement, and an energy consumption task requirement; and executing a first transaction of a first resource in either a resource market or a futures resource market, and executing a second transaction of a second resource in either the other of the resource market or the futures resource market.

[0248] Further embodiments of any of the embodiments described above in this disclosure may further include determining at least one of the alternative or correlated resources, and performing at least one transaction on at least one of the alternative or correlated resources.

[0249] Further embodiments of any of the embodiments described above in this disclosure may further include executing at least one transaction of at least one of the alternative or correlated resources as a substitute for the transaction of the resource.

[0250] Further embodiments of any of the embodiments described above in this disclosure may further include executing at least one transaction of at least one alternative resource or correlated resource in conjunction with the resource transaction.

[0251] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which determining a correlated resource involves at least one operation selected from a plurality of operations: determining a correlated resource of a machine as a resource to address an alternative task that provides an acceptable function to the machine; determining a correlated resource as a resource that is expected to correlate with the resource in respect to at least one of price or availability; and determining a correlated resource as a resource that is expected to have price changes corresponding to the resource, such that subsequent sales of the correlated resource, combined with purchase of the resource on the spot market, provide a planned economic outcome.

[0252] This disclosure describes a transaction-enabling system, which, according to one non-limiting embodiment disclosed herein, may include: a transaction detection circuit configured to interpret a transaction request value, wherein the transaction request value includes a transaction description of one of a proposed transaction or an imminent transaction, wherein the transaction description includes a cryptocurrency type value and a transaction amount value; a transaction locator circuit configured to determine a transaction location parameter in accordance with the transaction request value, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value; and a transaction execution circuit configured to provide an implementation command in accordance with the transaction location parameter.

[0253] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction locator circuit is further configured to determine transaction location parameters based on the tax treatment of either the proposed transaction or an imminent transaction.

[0254] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the transaction locator circuit is further configured to select one of several available geographical or jurisdictional values ​​that provides improved tax treatment compared to one of the nominal of those multiple available geographical or jurisdictional values.

[0255] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction locator circuit is further configured to determine a transaction location parameter in accordance with a cryptocurrency type value and at least one tax treatment of any of the proposed transaction or imminent transaction types.

[0256] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction locator circuit is further configured to provide a transaction location parameter as a transaction location value in response to determining that the tax processing of either the proposed transaction or an imminent transaction satisfies a threshold of the tax processing value.

[0257] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the transaction locator circuit is further configured to operate an expert system configured to use machine learning to continuously improve the determination of transaction location parameters related to the tax processing of transactions processed by the controller.

[0258] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the transaction locator circuit is further configured to operate an expert system, which is configured to aggregate regulatory information on cryptocurrency transactions from multiple jurisdictions and to continuously improve the determination of transaction location parameters based on the aggregated regulatory information.

[0259] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the expert system is further configured to use machine learning to continuously improve its determination of transaction location parameters related to secondary jurisdictional costs for cryptocurrency transactions.

[0260] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the expert system is further configured to use machine learning to continuously improve the determination of transaction location parameters related to the transaction speed of cryptocurrency transactions.

[0261] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the expert system is further configured to use machine learning to continuously improve the determination of transaction location parameters related to the tax treatment of cryptocurrency transactions.

[0262] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the expert system is further configured to use machine learning to continuously improve its determination of transaction location parameters related to the favorability of contract terms relating to cryptocurrency transactions.

[0263] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the expert system is further configured to use machine learning to continuously improve the determination of transaction location parameters related to compliance of cryptocurrency transactions within aggregated regulatory information.

[0264] Further embodiments of any of the embodiments described above in this disclosure may further include a transaction engine that responds to transaction execution commands.

[0265] This disclosure describes a method, and such method, according to one disclosed non-limiting embodiment of this disclosure, may include interpreting a transaction request value, wherein the transaction request value includes a transaction description for one of the proposed or imminent transactions, wherein the transaction description includes a cryptocurrency type value and a transaction amount value; determining transaction location parameters in accordance with the transaction request value, wherein the transaction location parameters include at least one of a transaction geographic value or a transaction jurisdiction value; and providing transaction execution commands in accordance with the transaction location parameters.

[0266] Further embodiments of any of the embodiments described above in this disclosure may further include determining transaction location parameters based on the tax treatment of either the proposed transaction or an imminent transaction.

[0267] Further embodiments of any of the embodiments described above in this disclosure may further include selecting at least one transactional geographical or transactional jurisdictional value from a plurality of available geographical or jurisdictional values ​​that provides improved tax treatment compared to one nominal of the plurality of available geographical or jurisdictional values.

[0268] Further embodiments of any of the embodiments described above in this disclosure may further include determining a transaction location parameter in accordance with a cryptocurrency type value and at least one tax treatment of either a proposed transaction or an imminent transaction.

[0269] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction request value further includes a transaction location value, and the method may further include providing the transaction location parameter as a transaction location value in response to determining that the tax treatment of either the proposed transaction or an imminent transaction satisfies a threshold of the tax treatment value.

[0270] Further embodiments of any of the embodiments described above in this disclosure may further include aggregating regulatory information for cryptocurrency transactions from multiple jurisdictions and continuously improving the determination of transaction location parameters based on the aggregated regulatory information.

[0271] Further embodiments of any of the embodiments described above in this disclosure may further include applying machine learning to continuously improve the determination of transaction location parameters related to at least one parameter selected from a plurality of parameters, consisting of secondary jurisdiction costs associated with the transaction, transaction speed, tax treatment of the transaction, favorability of contract terms associated with the transaction, and compliance of the transaction within aggregated regulatory information.

[0272] This disclosure describes a method, and such method, according to one disclosed non-limiting embodiment of this disclosure, may include interpreting a transaction request value, wherein the transaction request value includes a transaction description for one of the proposed or imminent transactions, wherein the transaction description includes a cryptocurrency type value and a transaction amount value; determining transaction location parameters in accordance with the transaction request value, wherein the transaction location parameters include at least one of a transaction geographic value or a transaction jurisdiction value; and executing a transaction in accordance with the transaction location parameters.

[0273] Further embodiments of any of the embodiments described above in this disclosure may further include determining transaction location parameters based on the tax treatment of either the proposed transaction or an imminent transaction.

[0274] Further embodiments of any of the embodiments described above in this disclosure may further include selecting one of a set of available geographical or jurisdictional values ​​that provides improved tax treatment compared to one of the nominal geographical or jurisdictional values.

[0275] Further embodiments of any of the embodiments described above in this disclosure may further include aggregating regulatory information for cryptocurrency transactions from multiple jurisdictions and continuously improving the determination of transaction location parameters based on the aggregated regulatory information.

[0276] Further embodiments of any of the embodiments described above in this disclosure may further include applying machine learning to continuously improve the determination of transaction location parameters related to at least one parameter selected from a plurality of parameters, consisting of secondary jurisdiction costs associated with the transaction, transaction speed of the transaction, tax treatment of the transaction, favorability of contract terms associated with the transaction, and compliance of the transaction within aggregated regulatory information.

[0277] This disclosure describes a transaction-enabling system, and a system according to one disclosed non-limiting embodiment of this disclosure may include a controller. The controller includes a smart wrapper configured to interpret a transaction request value from a user, the transaction request value including a transaction description for an incoming transaction, the transaction description including at least one of a transaction amount value, a cryptocurrency type value, and a transaction location value; and a transaction locator circuit configured to determine a transaction location parameter in accordance with the transaction request value and further in accordance with a plurality of tax treatment values ​​corresponding to a plurality of transaction locations, the transaction location parameter including at least one of a transaction geographic value or a transaction jurisdiction value, wherein the smart wrapper is further configured to instruct the execution of the incoming transaction in accordance with the transaction location parameter.

[0278] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction locator circuit is further configured to select one of several available transaction locations having a favorable tax treatment value.

[0279] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the transaction location value includes at least one location value corresponding to the location of the purchaser of the transaction, the location of the seller of the transaction, the location of delivery of the product or service of the transaction, the location of the supplier of the product or service of the transaction, the residential location of any of the purchaser, seller, or supplier of the transaction, and the legally available location of the transaction.

[0280] This disclosure describes a method, which, according to one disclosed non-limiting embodiment of this disclosure, may include interpreting a transaction request value from a user, wherein the transaction request value includes a transaction description for an incoming transaction, wherein the transaction description includes at least one of a transaction amount value, a cryptocurrency type value, and a transaction location value; determining a transaction location parameter in accordance with the transaction request value and further in accordance with a plurality of tax treatment values ​​corresponding to a plurality of transaction locations, wherein the transaction location parameter includes at least one of a transaction geographic value or a transaction jurisdiction value; and directing the execution of the incoming transaction in accordance with the location parameter.

[0281] Further embodiments of any of the embodiments described above in this disclosure may further include selecting an available transaction location having a favorable tax treatment value.

[0282] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a transaction location value involves selecting a transaction location value from a list of locations consisting of the location of the purchaser of the transaction, the location of the seller of the transaction, the location of delivery of the product or service of the transaction, the location of the supplier of the product or service of the transaction, the residential location of any of the purchaser, seller, or supplier of the transaction, and the legally available location of the transaction.

[0283] This disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of this disclosure, which may include a controller. The controller according to one disclosed non-limiting embodiment of this disclosure may include a transaction detection circuit configured to interpret a plurality of transaction request values, each transaction request value including a transaction description for one of proposed or imminent transactions, the transaction description including a cryptocurrency type value and a transaction amount value; a transaction support circuit configured to interpret a support resource description including at least one support resource for a plurality of transactions; and a support utilization circuit configured to operate an expert system, the support utilization circuit configured to use machine learning to continuously improve at least one execution parameter of a plurality of transactions in relation to a support resource description; and a transaction execution circuit configured to instruct the execution of a plurality of transactions in accordance with the improved at least one execution parameter.

[0284] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the support resource description includes an energy price description for an energy source available to power the execution of multiple transactions.

[0285] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the energy price description includes at least one of futures price forecasts and spot prices for an energy source.

[0286] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the support resource description includes multiple energy sources that are available to power the execution of multiple transactions.

[0287] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the support resource description includes at least one of a charge state and a charge cycle cost description for an energy storage source available to power the execution of multiple transactions.

[0288] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the energy storage source includes a battery, in which case the expert system may be further configured to use machine learning to improve at least one parameter selected from a plurality of parameters, consisting of a battery energy transfer efficiency value, a battery life value, and a lifetime utilization cost value.

[0289] This disclosure describes a method, and such method, according to one disclosed non-limiting embodiment of this disclosure, may include interpreting a plurality of transaction request values, each transaction request value including a transaction description for one of the proposed or imminent transactions, wherein the transaction description includes a cryptocurrency type value and a transaction amount value; interpreting a support resource description including at least one support resource for the plurality of transactions; operating an expert system, wherein the expert system is configured to use machine learning to continuously improve at least one execution parameter for the plurality of transactions in relation to the support resource description; and instructing the execution of the plurality of transactions in accordance with the improved at least one execution parameter.

[0290] Further embodiments of any of the embodiments described above in this disclosure may include situations in which ordering execution involves utilizing at least one execution parameter that has been continuously improved.

[0291] Further embodiments of any of the embodiments described above in this disclosure may further include ordering the execution of a first transaction in accordance with at least one execution parameter, wherein continuously improving the at least one execution parameter includes updating the at least one execution parameter, and the method further includes ordering the execution of a second transaction using the updated at least one execution parameter.

[0292] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the support resource description includes an energy price description for an energy source available to power the execution of multiple transactions.

[0293] Further embodiments of any of the embodiments described herein may include situations in which the energy price description includes at least one of futures price forecasts and spot prices for an energy source.

[0294] This disclosure describes a transaction-enabled system, and the system according to one disclosed non-limiting embodiment of this disclosure may include a controller. The controller according to one disclosed non-limiting embodiment of this disclosure may include: an attention-market access circuit configured to interpret a plurality of attention-related resources available in the attention market; an intelligent agent circuit configured to determine the acquisition value of an attention-related resource based on a cost parameter of at least one of the plurality of attention-related resources; and an attention acquisition circuit configured to solicit an attention-related resource according to its acquisition value.

[0295] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the acquisition circuit for attention performs at least one operation selected from a plurality of operations including purchasing attention-related resources from the attention market, selling attention-related resources to the attention market, offering to sell attention-related resources to a second intelligent agent, and offering to purchase attention-related resources from the second intelligent agent, so as to execute the claim of the attention-related resources.

[0296] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where a plurality of attention-related resources include at least one resource selected from a list consisting of advertisement placement, search list, keyword list, banner advertisement, video advertisement, embedded video advertisement, panel activity participation, survey activity participation, trial activity participation, and pilot activity placement or participation.

[0297] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the attention market includes at least one spot market among the plurality of attention-related resources.

[0298] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where at least one cost parameter among the plurality of attention-related resources includes at least one future predicted cost of the plurality of attention-related resources, and the intelligent agent circuit is further configured to determine the acquisition price of the attention-related resources according to the comparison between the first cost in the spot market and the cost parameter.

[0299] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the attention market includes a futures market for at least one of the plurality of attention-related resources, and at least one cost parameter of the plurality of attention-related resources includes the predicted future cost.

[0300] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where at least one cost parameter of a plurality of attention-related resources includes at least one future predicted cost of the plurality of attention-related resources, and the intelligent agent circuit is further configured to determine the acquisition price of the attention-related resources according to a comparison between the cost in the futures market and the cost parameter.

[0301] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the intelligent agent circuit is further configured to determine the acquisition price of the attention-related resources according to a cost parameter that is at least one cost parameter of a plurality of attention-related resources and has a value outside the predicted cost range for at least one of the plurality of attention-related resources.

[0302] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the intelligent agent circuit is further configured to determine the acquisition price of the attention-related resources according to a function of at least one cost parameter of a plurality of attention-related resources and at least one effectiveness parameter of the plurality of attention-related resources.

[0303] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the controller further includes an external data circuit configured to interpret a data source of social media, and the intelligent agent circuit is further configured to determine at least one of at least one future predicted cost of a plurality of attention-related resources and at least one effectiveness parameter of the plurality of attention-related resources for use as a cost parameter according to the data source of social media.

[0304] This disclosure describes a system, the system according to one disclosed non-limiting embodiment of this disclosure, which includes a fleet of machines, each of which includes a task system having a core task and at least one of a computation task or a network task, the system may further include a controller, the controller including: an attention market access circuit configured to interpret a plurality of attention-related resources available in the attention market; an intelligent agent circuit configured to determine the acquisition price of an attention-related resource based on a cost parameter of at least one of the plurality of attention-related resources, and further based on the core task for the corresponding machine in the fleet of machines; an attention purchase aggregation circuit configured to determine the aggregate attention-related resource purchase value in accordance with the acquisition prices of the plurality of attention-related resources from each intelligent agent circuit corresponding to each machine in the fleet of machines; and an attention acquisition circuit configured to purchase an attention-related resource in accordance with the aggregate purchase price of the attention-related resource.

[0305] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the interest purchase aggregation circuit is located at a location selected from a plurality of locations, which include locations at least partially distributed on a plurality of controllers corresponding to machines in a group of machines, locations on a corresponding selected controller of one machine in the group of machines, and locations on a system controller that is communicably coupled to a plurality of controllers corresponding to machines in the group of machines.

[0306] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the purchase acquisition circuit of interest is located at a location selected from a plurality of locations, which include locations at least partially distributed on a plurality of controllers corresponding to machines in a group of machines, locations on a corresponding selected controller of one machine in the group of machines, and locations on a system controller that is communicably coupled to a plurality of controllers corresponding to machines in the group of machines.

[0307] This disclosure describes a method, and such method, in accordance with one disclosed non-limiting embodiment of this disclosure, may include interpreting a plurality of focus-related resources available in a focus market, determining the acquisition price of the focus-related resources based on a cost parameter of at least one of the plurality of focus-related resources, and billing for the focus-related resources in accordance with their acquisition price.

[0308] Further embodiments of any of the embodiments described above in this disclosure may further include performing a claim for a focus-related resource by performing at least one operation selected from a plurality of operations, which include purchasing the focus-related resource from a focus market, selling the focus-related resource to a focus market, making an offer to sell the focus-related resource to a second intelligent agent, and making an offer to purchase the focus-related resource to the second intelligent agent.

[0309] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one cost parameter of a plurality of focus-related resources includes a projected future cost of at least one of the plurality of focus-related resources, and the method further includes determining the acquisition price of the focus-related resource in accordance with a comparison of the cost in the focus market with the cost parameter.

[0310] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting a social media data source and determining, depending on the social media data source, at least one of the following: a projected future cost of at least one of a plurality of attention-related resources and at least one effectiveness parameter of the plurality of attention-related resources, wherein, if a projected future cost is determined, it is used as a cost parameter, and the determination of the acquisition price of the attention-related resource is further based on at least one of the projected future cost or the effectiveness parameter.

[0311] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include: interpreting a plurality of attention-related resources available in a attention market; determining the acquisition price of the attention-related resources for each machine in a group of machines based on at least one cost parameter of the plurality of attention-related resources, and further based on the core tasks for each corresponding machine in the group of machines; determining the total purchase price of the attention-related resources in accordance with the acquisition prices of the plurality of attention-related resources corresponding to each machine in the group of machines; and configuring a attention acquisition circuit to purchase the attention-related resources in accordance with the total purchase price of the attention-related resources.

[0312] Further embodiments of any of the embodiments described above in this disclosure may further include a situation in which at least one cost parameter of a plurality of interest-related resources includes a projected future cost of at least one of the plurality of interest-related resources, and the method determines the acquisition price of each of the interest-related resources in accordance with a comparison of the spot market cost of the interest-related resource with the cost parameter.

[0313] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting a social media data source and determining, depending on the social media data source, at least one of the following: a projected future cost of at least one of a plurality of attention-related resources and at least one effectiveness parameter of the plurality of attention-related resources, wherein, if a projected future cost is determined, it is used as a cost parameter, and the determination of the acquisition price of the attention-related resource is further based on at least one of the projected future cost or the effectiveness parameter.

[0314] This disclosure describes a transaction-enabled system, the system according to one disclosed non-limiting embodiment of this disclosure, which may include a production facility including a core task, the core task including a production task, and the system may further include a controller, the controller including a facility description circuit configured to interpret a plurality of historical facility parameter values ​​and a plurality of corresponding historical facility outcome values; and a facility prediction circuit configured to operate an adaptive learning system, the adaptive learning system configured to train a facility production predictor in accordance with a plurality of historical facility parameter values ​​and a plurality of corresponding historical facility outcome values; the facility description circuit further configured to interpret a plurality of current facility parameter values, and the facility prediction circuit further configured to operate the adaptive learning system to predict current facility outcome values ​​in accordance with a plurality of current facility parameter values.

[0315] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the current equipment performance values ​​include equipment production performance.

[0316] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the current equipment performance values ​​include a probability distribution of equipment production performance.

[0317] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the current equipment output value includes at least one value selected from a plurality of values ​​consisting of a description of the production volume of a production task, a description of the production quality of a production task, a description of the utilization of equipment resources, a description of the utilization of input resources, and a description of the production timing of a production task.

[0318] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the equipment description circuit is further configured to interpret historical external data from at least one external data source, and the adaptive learning system is further configured to train an equipment production forecaster in accordance with the historical external data.

[0319] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where at least one external data source is selected from a plurality of data sources including a social media data source, a behavioral data source, a spot market price of an energy source, and a futures market price of an energy source.

[0320] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the equipment description circuit is further configured to interpret current external data from at least one external data source, and the adaptive learning system is further configured to predict the current equipment performance value according to the current external data.

[0321] The present disclosure describes a method, and the method according to one disclosed non-limiting embodiment of the present disclosure includes: interpreting a plurality of past equipment parameter values and corresponding plurality of past equipment performance values; operating an adaptive learning system, thereby training an equipment production predictor according to the plurality of past equipment parameter values and corresponding plurality of past equipment performance values; interpreting a plurality of current equipment parameter values; and operating the adaptive learning system to predict the current equipment performance value according to the plurality of current equipment parameter values.

[0322] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the current equipment performance value includes equipment production performance.

[0323] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the current equipment performance value includes a probability distribution of equipment production performance.

[0324] A further embodiment of any of the foregoing embodiments of the present disclosure may include a situation where the current equipment performance value includes at least one value selected from a plurality of values including a description of the production volume of a production task, a description of the production quality of a production task, a description of equipment resource utilization, a description of input resource utilization, and a description of the production timing of a production task.

[0325] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting historical external data from at least one external data source and operating an adaptive learning system to further train an equipment production forecaster in accordance with the historical external data.

[0326] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0327] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting current external data from at least one external data source and operating an adaptive learning system to predict current equipment performance values ​​in accordance with the current external data.

[0328] This disclosure describes a transaction-enabled system, which, according to one disclosed non-limiting embodiment of this disclosure, may include a facility and a controller, the controller including a facility description circuit configured to interpret a plurality of historical facility parameter values ​​and a plurality of corresponding historical facility outcome values, and a facility prediction circuit configured to operate an adaptive learning system, the adaptive learning system configured to train a facility resource allocation circuit in accordance with the plurality of historical facility parameter values ​​and a plurality of corresponding historical facility outcome values, the facility description circuit further configured to interpret a plurality of current facility parameter values, and the trained facility resource allocation circuit further configured to adjust a plurality of facility resource values ​​in accordance with the plurality of current facility parameter values.

[0329] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which multiple equipment resource values ​​include provisioning and allocation of equipment energy resources and provisioning and allocation of equipment computing resources.

[0330] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the trained equipment resource allocation circuit adjusts a number of equipment resource values ​​by either generating a favorable equipment resource utilization profile or selecting from a set of available equipment resource utilization profiles.

[0331] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the trained equipment resource distribution circuit adjusts a number of equipment resource values ​​by either generating a favorable equipment resource output selection or by selecting from a set of available equipment resource output values.

[0332] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the trained equipment resource distribution circuit adjusts a plurality of equipment resource values ​​by either generating a favorable equipment resource input profile or selecting from a set of available equipment resource input profiles.

[0333] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the trained equipment resource distribution circuit is further configured to adjust a number of equipment resource values ​​by either generating a favorable equipment resource configuration profile or selecting from a set of available equipment resource configuration profiles.

[0334] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the equipment description circuit is further configured to interpret historical external data from at least one external data source, and the adaptive learning system is further configured to train the equipment resource distribution circuit in accordance with the historical external data.

[0335] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0336] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the equipment description circuit is further configured to interpret current external data from at least one external data source, and the trained equipment resource allocation circuit is further configured to adjust a plurality of equipment resource values ​​in response to the current external data.

[0337] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include: interpreting a plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; operating an adaptive learning system to thereby train an equipment resource allocation circuit in accordance with the plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; interpreting a plurality of current equipment parameter values; and adjusting a plurality of equipment resource values ​​in accordance with the plurality of current equipment parameter values.

[0338] Further embodiments of any of the embodiments described above in this disclosure may include situations in which multiple equipment resource values ​​include provisioning and allocation of equipment energy resources and provisioning and allocation of equipment computing resources.

[0339] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by selecting a favorable equipment resource utilization profile from a set of available equipment resource utilization profiles.

[0340] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by generating a favorable equipment resource utilization profile for a set of available equipment resource utilization profiles.

[0341] Further embodiments of any of the embodiments described above in this disclosure may further include updating a set of available equipment resource utilization profiles in accordance with a plurality of equipment resource values.

[0342] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by selecting a favorable equipment resource output selection from a set of available equipment resource output values.

[0343] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting a set of equipment resource values ​​by generating an equipment resource output selection for a set of available equipment resource output values.

[0344] Further embodiments of any of the embodiments described above in this disclosure may further include updating a set of available equipment resource output values ​​in accordance with a plurality of equipment resource values.

[0345] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by selecting a favorable equipment resource input profile from a set of available equipment resource input profiles.

[0346] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by generating an equipment resource input profile for a set of available equipment resource input profiles.

[0347] Further embodiments of any of the embodiments described above in this disclosure may further include updating a set of available equipment resource input profiles in accordance with a plurality of equipment resource values.

[0348] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by selecting a favorable equipment resource configuration profile from a set of available equipment resource configuration profiles.

[0349] Further embodiments of any of the embodiments described above in this disclosure may further include adjusting multiple equipment resource values ​​by generating an equipment resource configuration profile for a set of available equipment resource configuration profiles.

[0350] Further embodiments of any of the embodiments described above in this disclosure may further include updating the set of available equipment resource configuration profiles in accordance with a plurality of equipment resource values.

[0351] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting historical external data from at least one external data source and operating the adaptive learning system to further train the equipment resource distribution circuit in accordance with the historical external data.

[0352] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0353] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting current external data from at least one external data source and further adjusting a plurality of equipment resource values ​​in accordance with the current external data.

[0354] This disclosure describes a transaction-enabled system, the system according to one disclosed non-limiting embodiment of this disclosure, which may include equipment including a core task, the core task including customer-related output, and the system may further include a controller, the controller including equipment description circuit configured to interpret a plurality of historical equipment parameter values ​​and a plurality of corresponding historical equipment outcome values, and equipment forecast circuit configured to operate an adaptive learning system, the adaptive learning system configured to train an equipment production forecaster in accordance with a plurality of historical equipment parameter values ​​and a plurality of corresponding historical equipment outcome values, the equipment description circuit further configured to interpret a plurality of current equipment parameter values, the trained equipment production forecaster configured to determine a customer contact indicator in accordance with a plurality of current equipment parameter values, and the controller further includes a customer notification circuit configured to provide notifications to customers in accordance with the customer contact indicator.

[0355] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the customer includes either current customers or future customers.

[0356] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves performing at least one operation selected from a plurality of operations: determining whether a customer-related output satisfies a volume request from a customer; determining whether a customer-related output satisfies a quality request from a customer; determining whether a customer-related output satisfies a timing request from a customer; and determining whether a customer-related output satisfies any request from a customer.

[0357] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the equipment description circuit is further configured to interpret historical external data from at least one external data source, and the adaptive learning system is further configured to train an equipment production forecaster in accordance with the historical external data.

[0358] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0359] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment description circuit is further configured to interpret current external data from at least one external data source, and the trained equipment production forecaster is further configured to determine a customer contact indicator in response to the current external data.

[0360] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include: interpreting a plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; activating an adaptive learning system to thereby train an equipment production forecaster in accordance with the plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; interpreting a plurality of current equipment parameter values; activating the trained equipment production forecaster to determine a customer contact indicator in accordance with the plurality of current equipment parameter values; and providing a notification to a customer in accordance with the customer contact indicator.

[0361] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the customer includes either current customers or future customers.

[0362] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator includes determining whether a customer-related output satisfies a volume request from a customer.

[0363] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves determining whether a customer-related output meets a customer's quality requirements.

[0364] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator includes determining whether a customer-related output satisfies a timing request from the customer.

[0365] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves determining whether a customer-related output satisfies any request from the customer.

[0366] Further embodiments of any of the above-described embodiments of the present disclosure may further include interpreting historical external data from at least one external data source and operating an adaptive learning system to further train an equipment production forecaster in accordance with the historical external data.

[0367] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0368] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting current external data from at least one external data source and operating a trained equipment production forecaster to further determine customer contact indicators in accordance with the current external data.

[0369] This disclosure describes a transaction-enabled system, the system according to one disclosed non-limiting embodiment of this disclosure, which may include equipment including a core task, the core task including customer-related output, and the system may further include a controller, the controller including equipment description circuit configured to interpret a plurality of historical equipment parameter values ​​and a plurality of corresponding historical equipment outcome values, and equipment forecast circuit configured to operate an adaptive learning system, the adaptive learning system configured to train an equipment production forecaster in accordance with a plurality of historical equipment parameter values ​​and a plurality of corresponding historical equipment outcome values, the equipment description circuit further configured to interpret a plurality of current equipment parameter values, the trained equipment production forecaster configured to determine a customer contact indicator in accordance with a plurality of current equipment parameter values, and the controller further includes a customer notification circuit configured to provide notifications to customers in accordance with the customer contact indicator.

[0370] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the customer includes either current customers or future customers.

[0371] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves performing at least one operation selected from a plurality of operations: determining whether a customer-related output satisfies a volume request from a customer; determining whether a customer-related output satisfies a quality request from a customer; determining whether a customer-related output satisfies a timing request from a customer; and determining whether a customer-related output satisfies any request from a customer.

[0372] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the equipment description circuit is further configured to interpret historical external data from at least one external data source, and the adaptive learning system is further configured to train an equipment production forecaster in accordance with the historical external data.

[0373] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0374] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment description circuit is further configured to interpret current external data from at least one external data source, and the trained equipment production forecaster is further configured to determine a customer contact indicator in response to the current external data.

[0375] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include: interpreting a plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; activating an adaptive learning system to thereby train an equipment production forecaster in accordance with the plurality of historical equipment parameter values ​​and corresponding plurality of historical equipment outcome values; interpreting a plurality of current equipment parameter values; activating the trained equipment production forecaster to determine a customer contact indicator in accordance with the plurality of current equipment parameter values; and providing a notification to a customer in accordance with the customer contact indicator.

[0376] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the customer includes either current customers or future customers.

[0377] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator includes determining whether a customer-related output satisfies a volume request from a customer.

[0378] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves determining whether a customer-related output meets a customer's quality requirements.

[0379] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator includes determining whether a customer-related output satisfies a timing request from the customer.

[0380] Further embodiments of any of the embodiments described above in this disclosure may include situations in which determining a customer contact indicator involves determining whether a customer-related output satisfies any request from the customer.

[0381] Further embodiments of any of the above-described embodiments of the present disclosure may further include interpreting historical external data from at least one external data source and operating an adaptive learning system to further train an equipment production forecaster in accordance with the historical external data.

[0382] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices for energy sources, and futures market prices for energy sources.

[0383] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting current external data from at least one external data source and operating a trained equipment production forecaster to further determine customer contact indicators in accordance with the current external data.

[0384] This disclosure describes a transaction-enabled system, the system which, according to one disclosed non-limiting embodiment of this disclosure, may include an energy and computing facility, the energy and computing facility including at least one computing task or computing resource and at least one energy source or energy utilization requirement, the system may further include a controller, the controller including a facility description circuit configured to interpret detected conditions, the detected conditions including at least one condition selected from a plurality of conditions consisting of input resources for the facility, facility resources, output parameters for the facility, and external conditions related to the output of the facility, the controller further includes a facility configuration circuit configured to operate an adaptive learning system, the adaptive learning system configured to adjust the facility configuration based on the detected conditions.

[0385] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0386] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0387] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the spot market for network bandwidth or the futures market for network bandwidth; and executing buy or sell transactions in either the spot market for spectrum or the futures market for spectrum.

[0388] Further embodiments of any of the embodiments described above in this disclosure may include situations in which adjusting the equipment configuration further includes adjusting at least one task of the equipment to reduce energy utilization requirements.

[0389] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include interpreting conditions detected in relation to a facility, wherein the detected conditions include at least one condition selected from a plurality of conditions consisting of input resources for the facility, facility resources, output parameters for the facility, and external conditions related to the output of the facility, and operating an adaptive learning system to adjust the facility configuration based on the detected conditions.

[0390] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either an energy spot market or an energy futures market.

[0391] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes executing buy or sell transactions in either the spot market for computing resources or the futures market for computing resources.

[0392] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market for energy credits or the futures market for energy credits.

[0393] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market or the futures market for network bandwidth.

[0394] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market or the futures market of Spectrum.

[0395] This disclosure describes a transaction-enabled system, which, according to one disclosed non-limiting embodiment of this disclosure, may include an energy and computing facility comprising at least one computing task or computing resource and at least one energy source or energy utilization requirement, and the system may further include a controller, which includes a facility description circuit configured to interpret detected conditions, wherein the detected conditions relate to a set of input resources for the facility, and a facility configuration circuit configured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust the facility configuration based on the detected conditions.

[0396] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0397] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0398] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the spot market for network bandwidth or the futures market for network bandwidth; and executing buy or sell transactions in either the spot market for spectrum or the futures market for spectrum.

[0399] Further embodiments of any of the embodiments described above in this disclosure may include situations in which adjusting the equipment configuration further includes adjusting at least one of the equipment's resource tasks or configurations such that it changes the input resource requirements for the equipment.

[0400] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include interpreting conditions detected in relation to a facility, wherein the detected conditions relate to a set of input resources for the facility, and operating an adaptive learning system to thereby adjust the facility configuration based on the detected conditions.

[0401] Further embodiments of any of the embodiments described above in this disclosure may include situations in which coordination involves executing a buy or sell transaction in either an energy spot market or an energy futures market.

[0402] Further embodiments of any of the embodiments described above in this disclosure may include situations in which coordination involves executing a buy or sell transaction in either the spot market for computing resources or the futures market for computing resources.

[0403] Further embodiments of any of the embodiments described above in this disclosure may include situations in which coordination involves executing a buy or sell transaction in either the spot market or the futures market for energy credits.

[0404] Further embodiments of any of the embodiments described above in this disclosure may include situations in which coordination involves executing a buy or sell transaction in either a spot market for network bandwidth or a futures market for network bandwidth.

[0405] Further embodiments of any of the embodiments described above in this disclosure may include situations in which coordination involves executing buy or sell transactions in either the spot market or the futures market of the spectrum.

[0406] This disclosure describes a transaction-enabled system, which, according to one disclosed non-limiting embodiment of this disclosure, may include an energy and computing facility comprising at least one computing task or computing resource and at least one energy source or energy utilization requirement, and the system may further include a controller, which includes a facility description circuit configured to interpret detected conditions such that the detected conditions relate to at least one resource of the facility, and a facility configuration circuit configured to operate an adaptive learning system such that the adaptive learning system is configured to adjust the facility configuration based on the detected conditions.

[0407] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0408] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0409] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which consist of executing buy or sell transactions in either a spot market for network bandwidth or a futures market for network bandwidth, and executing buy or sell transactions in either a spot market for spectrum or a futures market for spectrum.

[0410] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the facility further includes at least one additional facility resource, and the adjustment of the facility configuration further includes adjusting the utilization of computing resources and at least one additional facility resource, wherein the at least one additional facility resource includes at least one of network resources, data storage resources, or spectral resources.

[0411] This disclosure describes a method, and such method, according to one disclosed non-limiting embodiment of this disclosure, may include interpreting conditions detected in relation to a facility, wherein the detected conditions relate to at least one resource of the facility, and activating an adaptive learning system to thereby adjust the facility configuration based on the detected conditions.

[0412] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either an energy spot market or an energy futures market.

[0413] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market or the futures market of Spectrum.

[0414] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market for computing resources or the futures market for computing resources.

[0415] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market or the futures market for energy credits.

[0416] This disclosure describes a transaction-enabled system, which, according to one disclosed non-limiting embodiment of this disclosure, may include an energy and computing facility, which includes at least one computing task or computing resource and at least one energy source or energy utilization requirement, and the system may further include a controller, which includes a facility description circuit configured to interpret detected conditions, the detected conditions including output parameters for the facility, and a facility configuration circuit configured to operate an adaptive learning system, the adaptive learning system configured to adjust the facility configuration based on the detected conditions.

[0417] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0418] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0419] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which consist of executing buy or sell transactions in either a spot market for network bandwidth or a futures market for network bandwidth, and executing buy or sell transactions in either a spot market for spectrum or a futures market for spectrum.

[0420] Further embodiments of any of the embodiments described above in this disclosure may further include situations in which the equipment configuration is adjusted to adjust one task of the equipment to provide at least one of the following: an increased equipment output, an increased equipment quality value, or an adjusted equipment output time value.

[0421] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include interpreting conditions detected in relation to equipment, wherein the detected conditions include output parameters for equipment, and operating an adaptive learning system to adjust the equipment configuration based on the detected conditions.

[0422] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either an energy spot market or an energy futures market.

[0423] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market or the futures market of Spectrum.

[0424] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market for computing resources or the futures market for computing resources.

[0425] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market or the futures market for energy credits.

[0426] This disclosure describes a transaction-enabled system, which, according to one disclosed non-limiting embodiment of this disclosure, may include an energy and computing facility, which includes at least one computing task or computing resource and at least one energy source or energy utilization requirement, and the system may further include a controller, which includes a facility description circuit configured to interpret detected conditions, wherein the detected conditions include utilization parameters for the facility's output, and a facility configuration circuit configured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust the facility configuration based on the detected conditions.

[0427] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0428] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0429] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which consist of executing buy or sell transactions in either a spot market for network bandwidth or a futures market for network bandwidth, and executing buy or sell transactions in either a spot market for spectrum or a futures market for spectrum.

[0430] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment configuration is adjusted to adjust at least one task of the equipment such that the utilization parameter for the equipment is reduced.

[0431] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include interpreting conditions detected in relation to equipment, wherein the detected conditions include utilization parameters for the equipment's output, and operating an adaptive learning system to thereby adjust the equipment configuration based on the detected conditions.

[0432] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either an energy spot market or an energy futures market.

[0433] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market or the futures market of Spectrum.

[0434] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market for computing resources or the futures market for computing resources.

[0435] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market for energy credits or the futures market for energy credits.

[0436] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include an energy and computing facility comprising at least one of a computing task or computing resource and at least one of an energy source or energy utilization requirement, and the system may further include a controller, which includes a facility model circuit configured to operate a digital twin of the facility, a facility description circuit configured to interpret a set of parameters from the digital twin of the facility, and a facility configuration circuit configured to operate an adaptive learning system, the adaptive learning system configured to adjust the facility configuration based on a set of parameters from the digital twin of the facility.

[0437] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the adaptive learning system includes at least one of a machine learning system and an artificial intelligence (AI) system.

[0438] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the configuration of the equipment further includes at least one operation selected from a plurality of operations, which include: executing buy or sell transactions in either the energy spot market or the energy futures market; executing buy or sell transactions in either the computing resource spot market or the computing resource futures market; and executing buy or sell transactions in either the energy credit spot market or the energy credit futures market.

[0439] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment further includes network tasks, and the adjustment of the equipment configuration further includes at least one operation selected from a plurality of operations, which consist of executing buy or sell transactions in either a spot market for network bandwidth or a futures market for network bandwidth, and executing buy or sell transactions in either a spot market for spectrum or a futures market for spectrum.

[0440] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the equipment description circuit is further configured to interpret a detected condition, the detected condition includes at least one condition selected from a plurality of conditions consisting of equipment input resources, equipment resources, equipment output parameters, and external conditions related to the equipment output, and the equipment model circuit is further configured to update a digital twin of the equipment in response to the detected condition.

[0441] This disclosure describes a method, and that method according to one disclosed non-limiting embodiment of this disclosure may include operating a model including a digital twin of a facility, interpreting a set of parameters from the digital twin of the facility, and operating an adaptive learning system to thereby adjust the facility configuration based on the set of parameters from the digital twin of the facility.

[0442] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either an energy spot market or an energy futures market.

[0443] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market or the futures market of Spectrum.

[0444] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute buy or sell transactions in either the spot market for computing resources or the futures market for computing resources.

[0445] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market or the futures market for energy credits.

[0446] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the equipment configuration is adjusted to execute a buy or sell transaction in either the spot market or the futures market for network bandwidth.

[0447] Further embodiments of any of the above-described embodiments of the present disclosure may further include interpreting conditions detected in relation to a facility, wherein the detected conditions include at least one condition selected from a plurality of conditions consisting of input resources for the facility, facility resources, output parameters for the facility, and external conditions related to the output of the facility, and operating an adaptive learning system to update a digital twin of the facility in accordance with the detected conditions.

[0448] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment of this disclosure, may include a machine having associated renewable energy equipment, the machine having at least one requirement of a computing task, a networking task, and an energy consumption task, and the system may further include a controller, the controller including an energy requirements circuit configured to determine the amount of energy for the machine to correspond to at least one of the computing task, networking task, and energy consumption task, in accordance with the requirement of at least one of the computing task, networking task, and energy consumption task, and an energy distribution circuit configured to adaptively improve the supply of energy generated by the associated renewable energy equipment between at least one of the computing task, networking task, and energy consumption task.

[0449] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the energy consumption task includes a core task.

[0450] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the controller further includes an energy market circuit configured to access an energy market, and the energy distribution circuit is further configured to adaptively improve the energy supply of energy generated by relevant renewable energy facilities between computing tasks, networking tasks, energy consumption tasks, and the sale of energy generated in the energy market.

[0451] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the energy market includes at least one of a spot market or a futures market.

[0452] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the energy distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0453] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include determining the amount of energy for a machine to address at least one of a computation task, a network task, and an energy consumption task, depending on the computation task requirements, network task requirements, and energy consumption task requirements, and adaptively improving the energy supply between the computation task, network task, and energy consumption task, wherein the energy supply is energy generated by the machine's renewable energy equipment.

[0454] Further embodiments of any of the embodiments described above in this disclosure may further include accessing energy markets and adaptively improving the energy supply of energy generated by renewable energy facilities between computing tasks, networking tasks, energy consumption tasks and the sale of energy generated in energy markets.

[0455] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a machine having at least one of computing task requirements, network task requirements, and energy consumption task requirements, and a controller, the controller including a resource requirements circuit configured to determine the amount of resources for the machine to address at least one of the computing task requirements, network task requirements, and energy consumption task requirements; a futures resource market circuit configured to access a futures resource market; and a resource allocation circuit configured to execute resource transactions in the futures resource market according to the determined amount of resources.

[0456] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include computing resources and the futures resource market includes a futures market for computing resources.

[0457] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0458] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0459] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0460] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include data storage resources and the futures resource market includes a futures market for data storage capacity.

[0461] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy storage resources and the futures resource market includes a futures market for energy storage capacity.

[0462] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources and the futures resource market includes a futures market for network bandwidth.

[0463] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a transaction of a resource in a futures resource market involves either the purchase or the sale of the resource.

[0464] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve either the machine's output value or the machine's operating cost using transactions executed in the futures resource market.

[0465] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0466] This disclosure describes a method, and such method, according to one non-limiting embodiment disclosed herein, may include determining the amount of resources for a machine to address at least one of the machine's computational task requirements, networking task requirements, and energy consumption task requirements; accessing a futures resource market; and, in accordance with the determined amount of resources, executing a transaction for resources in the futures resource market.

[0467] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include computing resources and the futures resource market includes a futures market for computing resources.

[0468] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0469] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0470] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0471] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include data storage resources and the futures resource market includes a futures market for data storage capacity.

[0472] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy storage resources and the futures resource market includes a futures market for energy storage capacity.

[0473] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources and the futures resource market includes a futures market for network bandwidth.

[0474] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a transaction of a resource in a futures resource market involves either buying or selling the resource.

[0475] Further embodiments of any of the aforementioned embodiments of this disclosure may further include adaptively improving either the output value of the machine or the operating cost of the machine using transactions executed in the futures resource market.

[0476] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a group of machines, each having at least one of the following: computation task requirements, network task requirements, and energy consumption task requirements, and the system may further include a controller, which includes: a resource requirements circuit configured to determine the amount of resources for each corresponding machine to address at least one of the computation task requirements, network task requirements, and energy consumption task requirements; a futures resource market circuit configured to access a futures resource market; and a resource allocation circuit configured to execute aggregated resource transactions in the futures resource market according to the amount of resources determined for each machine.

[0477] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include computing resources and the futures resource market includes a futures market for computing resources.

[0478] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0479] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0480] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0481] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include data storage resources and the futures resource market includes a futures market for data storage capacity.

[0482] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy storage resources and the futures resource market includes a futures market for energy storage capacity.

[0483] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources and the futures resource market includes a futures market for network bandwidth.

[0484] Further embodiments of any of the embodiments described above in this disclosure may include situations in which aggregated transactions of resources in a futures resource market include either the purchase or the sale of resources.

[0485] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve either the aggregated output value of the machine group or the operating cost of the machine group using aggregated transactions performed in the futures resource market.

[0486] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0487] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include determining the amount of resources for each machine in a group of machines to address at least one of the following tasks: computational task requirements, networking task requirements, and energy consumption task requirements for each corresponding machine; accessing a futures resource market; and executing aggregated resource transactions in the futures resource market in accordance with the amount of resources determined for each machine.

[0488] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include computing resources and the futures resource market includes a futures market for computing resources.

[0489] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0490] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0491] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0492] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include data storage resources and the futures resource market includes a futures market for data storage capacity.

[0493] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy storage resources and the futures resource market includes a futures market for energy storage capacity.

[0494] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources and the futures resource market includes a futures market for network bandwidth.

[0495] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the execution of aggregated transactions of resources in a futures resource market includes either the purchase or the sale of resources.

[0496] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving either the aggregated output values ​​of a group of machines or the operating costs of a group of machines using aggregated transactions performed in the futures resource market.

[0497] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a group of machines, each having at least one requirement of a computing task, a networking task, and an energy-consuming task, and the system may further include a controller, which includes a resource requirements circuit configured to determine the amount of resources for each corresponding machine to satisfy at least one requirement of a computing task, a networking task, and an energy-consuming task, and a resource distribution circuit configured to adaptively improve the resource utilization rate of resources for each machine among the computing task, networking task, and energy-consuming task for each corresponding machine.

[0498] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include computing resources.

[0499] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include spectral allocation resources.

[0500] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy credit resources.

[0501] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy resources.

[0502] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include data storage resources.

[0503] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy storage resources.

[0504] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources.

[0505] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0506] This disclosure describes a method, and a method according to one non-limiting embodiment disclosed herein may include determining the amount of resources for each machine in a group of machines to meet the requirements of at least one of the following: computing tasks, networking tasks, and energy consumption tasks for each corresponding machine, and adaptively improving the resource utilization rate for each machine among the computing tasks, networking tasks, and energy consumption tasks for each corresponding machine.

[0507] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include computing resources.

[0508] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include spectral allocation resources.

[0509] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy credit resources.

[0510] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy resources.

[0511] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include data storage resources.

[0512] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy storage resources.

[0513] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources.

[0514] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a machine having at least one of computing task requirements, network task requirements, and energy consumption task requirements, and further, the system may include a controller, which includes a resource requirements circuit configured to determine the amount of resources for the machine to address at least one of the computing task requirements, network task requirements, and energy consumption task requirements; a resource market circuit configured to access a resource market; and a resource distribution circuit configured to execute resource transactions in the resource market according to the determined amount of resources.

[0515] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the resource market includes an energy spot market.

[0516] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the resource market includes a spot market for energy credits.

[0517] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the resource market includes a spot market for spectral allocation.

[0518] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve either the output value of the machine or the operating cost of the machine using transactions performed in the resource market.

[0519] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0520] This disclosure describes a method, and such method, according to one of the non-limiting embodiments disclosed herein, may include determining the amount of resources for a machine to address at least one of the following: computational task requirements, networking task requirements, and energy consumption task requirements; accessing a resource market; and, in accordance with the determined amount of resources, performing resource transactions in the resource market.

[0521] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the resource market includes an energy spot market.

[0522] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the resource market includes a spot market for energy credits.

[0523] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the resource market includes a spot market for spectral allocation.

[0524] Further embodiments of any of the embodiments described above in this disclosure may further include using transactions performed in the resource market to adaptively improve either the output value of the machine or the operating cost of the machine.

[0525] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a group of machines, each having at least one of the following: computation task requirements, network task requirements, and energy consumption task requirements, and the system may further include a controller, which includes a resource requirements circuit configured to determine the amount of resources for each corresponding machine to address at least one of the computation task requirements, network task requirements, and energy consumption task requirements; a resource market circuit configured to access a resource market; and a resource distribution circuit configured to execute aggregated resource transactions in the resource market according to the determined amount of resources for each machine.

[0526] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the resource market includes an energy spot market.

[0527] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the resource market includes a spot market for energy credits.

[0528] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the resource market includes a spot market for spectral allocation.

[0529] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve either the aggregate output value of the machine group or the operating cost of the machine group using transactions performed in the resource market.

[0530] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0531] This disclosure describes a method, and a method according to one non-limiting embodiment disclosed herein may include determining the amount of resources for each machine in a group of machines to address at least one of the following tasks: computational task requirements, networking task requirements, and energy consumption task requirements for each corresponding machine; accessing a resource market; and performing aggregated resource transactions in the resource market in accordance with the determined amount of resources for each machine.

[0532] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the resource market includes an energy spot market.

[0533] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the resource market includes a spot market for energy credits.

[0534] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the resource market includes a spot market for spectral allocation.

[0535] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving either the aggregate output value of a group of machines or the operating cost of a group of machines using transactions performed in the resource market.

[0536] This disclosure describes a transaction-enabled system, which, according to one non-limiting embodiment disclosed herein, may include a machine having at least one of computing task requirements, network task requirements, and energy consumption task requirements, and the system may further include a controller, which includes a resource requirements circuit configured to determine the amount of resources for the machine to address at least one of the computing task requirements, network task requirements, and energy consumption task requirements; a social media data circuit configured to interpret data from multiple social media data sources; a futures resource market circuit configured to access a futures resource market; a market forecasting circuit configured to forecast futures market prices for resources in the futures resource market in accordance with the multiple social media data sources; and a resource allocation circuit configured to execute transactions of resources in the futures resource market in accordance with the determined amount of resources and the forecasted futures market prices for resources.

[0537] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0538] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0539] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0540] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a transaction of a resource in a futures resource market involves either the purchase or the sale of the resource.

[0541] Further embodiments of any of the embodiments described above in this disclosure may include a scenario in which the market prediction circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0542] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve either the output value of the machine or the operating cost of the machine using transactions performed in the futures resource market.

[0543] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0544] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include: determining the amount of resources for a machine to address at least one of computational task requirements, networking task requirements, and energy consumption task requirements; interpreting data from multiple social media data sources; accessing futures resource markets; predicting futures market prices for resources in the futures resource markets based on the multiple social media data sources; and executing transactions for resources in the futures resource markets based on the determined amount of resources and the predicted futures market prices for resources.

[0545] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include spectral allocation resources and the futures resource market includes a spectral allocation futures market.

[0546] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy credit resources and the futures resource market includes a futures market for energy credits.

[0547] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include energy resources and the futures resource market includes an energy futures market.

[0548] Further embodiments of any of the embodiments described above in this disclosure may include situations in which a transaction of a resource in a futures resource market involves either buying or selling the resource.

[0549] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving either the output value of the machine or the operating cost of the machine using transactions executed in the futures resource market.

[0550] This disclosure describes a transaction-enabled system, and the system according to one non-limiting embodiment disclosed herein may include a machine having at least one of computing task requirements, network task requirements, and energy consumption task requirements, and a controller. The controller includes a resource requirements circuit configured to determine the amount of resources for the machine to address at least one of the computing task requirements, network task requirements, and energy consumption task requirements; a resource market circuit configured to access a resource market; a market test circuit configured to execute a first transaction of resources in the resource market according to the determined amount of resources; and an arbitrage execution circuit configured to execute a second transaction of resources in the resource market according to the determined amount of resources and further according to the result of executing the first transaction, wherein the second transaction is a larger transaction than the first transaction.

[0551] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include computing resources.

[0552] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include spectral allocation resources.

[0553] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy credit resources.

[0554] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy resources.

[0555] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include data storage resources.

[0556] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy storage resources.

[0557] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources.

[0558] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage execution circuit is further configured to adaptively improve arbitrage parameters by adjusting the relative sizes of the first transaction and the second transaction.

[0559] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameter includes at least one parameter selected from a plurality of parameters comprising similarity values ​​in the market response of the first transaction and the second transaction, confidence values ​​of the first transaction for providing test information for the second transaction, and market effect of the first transaction.

[0560] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage execution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0561] This disclosure describes a method, and a method according to one non-limiting embodiment disclosed herein may include determining the amount of resources for a machine to address at least one of computing task requirements, network task requirements, and energy consumption task requirements; accessing a resource market; executing a first transaction of resources in the resource market in accordance with the determined amount of resources; and executing a second transaction of resources in the resource market in accordance with the determined amount of resources and further in accordance with the result of executing the first transaction, wherein the second transaction includes a larger transaction than the first transaction.

[0562] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include computing resources.

[0563] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include spectral allocation resources.

[0564] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy credit resources.

[0565] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy resources.

[0566] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include data storage resources.

[0567] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include energy storage resources.

[0568] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the resources include network bandwidth resources.

[0569] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving arbitrage parameters by adjusting the relative sizes of the first and second transactions.

[0570] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameter includes at least one parameter selected from a plurality of parameters comprising: similarity values ​​in the market response of the first transaction and the second transaction; confidence values ​​of the first transaction for providing test information for the second transaction; and market effect of the first transaction.

[0571] This disclosure describes an apparatus, and an apparatus according to one non-limiting embodiment disclosed herein may include: a resource requirements circuit configured to determine the amount of resources for a machine to address at least one of computing task requirements, network task requirements, and energy consumption task requirements; a resource market circuit configured to access a resource market; a market test circuit configured to execute a first transaction of resources in the resource market in accordance with the determined amount of resources; and an arbitrage execution circuit configured to execute a second transaction of resources in the resource market in accordance with the determined amount of resources and further in accordance with the result of executing the first transaction, wherein the second transaction includes a larger transaction than the first transaction.

[0572] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resources include at least one resource selected from a plurality of resources consisting of computing resources, spectral allocation resources, energy credit resources, energy resources, data storage resources, energy storage resources, and network bandwidth resources.

[0573] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage execution circuit is further configured to adaptively improve the arbitrage parameters by adjusting the relative sizes of the first transaction and the second transaction.

[0574] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameter includes a similarity value in the market response of the first transaction and the second transaction.

[0575] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameter further includes the market effect of the first transaction.

[0576] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameters include a confidence value of the first transaction for providing test information for a second transaction.

[0577] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage parameter further includes the market effect of the first transaction.

[0578] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the arbitrage execution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0579] This disclosure describes a transaction-enabled system, and the system according to one non-limiting embodiment disclosed herein may include a machine having resource capacity related to resources, the machine having at least one requirement of core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, and the system may further include a controller, the controller including a resource requirements circuit configured to determine the amount of resources to accommodate at least one requirement of core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, in accordance with the requirements of at least one requirement of core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, and a resource distribution circuit configured to adaptively improve the resource supply of resources among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks in accordance with the relevant resource capacity.

[0580] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes computing capacity for computing resources.

[0581] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes energy capacity for energy resources.

[0582] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes network bandwidth capacity for network resources.

[0583] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes energy storage capacity for energy storage resources.

[0584] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve resource supply in accordance with either the quality and output associated with the core task.

[0585] Further embodiments of any of the above embodiments of the present disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve resource supply in accordance with the operating costs of the machine.

[0586] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0587] This disclosure describes a method, and the method according to one disclosed non-limiting embodiment of this disclosure may include determining the amount of resources to address core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks of a machine, in accordance with at least one of the machine's core task requirements, computing task requirements, energy storage task requirements, data storage task requirements, and network task requirements, and adaptively improving the resource supply of resources among the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks in accordance with the machine's relevant resource capacity.

[0588] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes computing capacity for computing resources.

[0589] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes energy capacity for energy resources.

[0590] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes network bandwidth capacity for network resources.

[0591] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the relevant resource capacity includes energy storage capacity for energy storage resources.

[0592] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving resource supply in response to either quality or output associated with the core task of the machine.

[0593] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving resource supply in accordance with the operating costs of the machine.

[0594] This disclosure describes a transaction-enabled system, the system according to one non-limiting embodiment disclosed herein, which may include a group of machines, each having a resource capacity associated with resources, and a controller, each machine in the group of machines further having at least one requirement of core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks. The controller includes a resource requirements circuit configured to determine the aggregate amount of resources for each machine in the group of machines to correspond to at least one of the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, in accordance with the requirement of at least one of the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, and a resource distribution circuit configured to adaptively improve the aggregated resource supply of resources among the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks for each machine in the group of machines, in accordance with the aggregated associated resource capacity.

[0595] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the aggregated related resource capacity includes computing capacity for computing resources.

[0596] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes energy capacity for energy resources.

[0597] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes network bandwidth capacity for network resources.

[0598] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes energy storage capacity for energy storage resources.

[0599] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with either the quality and output associated with the core task for each machine in the machine group.

[0600] Further embodiments of any of the above embodiments of the present disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the aggregated quality and output associated with the core task for the group of machines.

[0601] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource distribution circuit is further configured to interpret resource transferability values ​​between at least two machines of a group of machines and to adaptively improve the aggregated resource supply in accordance with those resource transferability values.

[0602] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the operating costs of each machine in the machine group.

[0603] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the aggregated operating costs of the group of machines.

[0604] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0605] This disclosure describes a method, and the method according to one non-limiting embodiment disclosed herein may include determining the aggregated amount of resources to address the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks for each machine in a group of machines, in accordance with at least one of the core task requirements, computing task requirements, energy storage task requirements, data storage task requirements, and network task requirements for each machine in a group of machines, and adaptively improving the aggregated resource supply among the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks for each machine in a group of machines, in accordance with the aggregated and relevant resource capacity of the group of machines.

[0606] Further embodiments of any of the embodiments described herein may further include adaptively improving the aggregated resource supply in accordance with either the quality and output associated with the core task for each machine in the machine group.

[0607] Further embodiments of any of the embodiments described herein may further include adaptively improving the aggregated resource supply in accordance with the aggregated quality and output associated with the core tasks for each machine in the machine group.

[0608] Further embodiments of any of the embodiments described above in this disclosure may further include interpreting resource transferability values ​​between at least two machines of a group of machines and adaptively improving the aggregated resource supply in accordance with those resource transferability values.

[0609] Further embodiments of any of the embodiments described above in this disclosure may further include adaptively improving the aggregated resource supply in accordance with the operating costs of each machine in the machine group.

[0610] Further embodiments of any of the above embodiments of the present disclosure may further include adaptively improving the aggregated resource supply in accordance with the aggregated operating costs of the machine group.

[0611] This disclosure describes an apparatus, and an apparatus according to one disclosed non-limiting embodiment of this disclosure may include: a resource requirements circuit configured to determine aggregate amounts of resources for each machine in a group of machines to address core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks, in accordance with at least one of the core task requirements, computing task requirements, energy storage task requirements, data storage task requirements, and network task requirements for each machine in a group of machines; and a resource distribution circuit configured to adaptively improve the aggregated resource supply among the core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks for each machine in a group of machines, in accordance with the aggregated and relevant resource capacity of the group of machines.

[0612] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the aggregated related resource capacity includes computing capacity for computing resources.

[0613] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes energy capacity for energy resources.

[0614] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes network bandwidth capacity for network resources.

[0615] Further embodiments of any of the embodiments described above in this disclosure may include situations in which the aggregated related resource capacity includes energy storage capacity for energy storage resources.

[0616] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the quality associated with the core task for each machine in the machine group.

[0617] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the output associated with the core task for each machine in the group of machines.

[0618] Further embodiments of any of the above embodiments of the present disclosure may include a situation in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the aggregated quality associated with the core task for the group of machines.

[0619] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in response to aggregated outputs associated with core tasks for a group of machines.

[0620] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource distribution circuit is further configured to interpret resource transferability values ​​between at least two machines of a group of machines and to adaptively improve the aggregated resource supply in accordance with those resource transferability values.

[0621] Further embodiments of any of the embodiments described above in this disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the operating costs of each machine in the machine group.

[0622] Further embodiments of any of the above embodiments of the present disclosure may include a configuration in which the resource distribution circuit is further configured to adaptively improve the aggregated resource supply in accordance with the aggregated operating costs of the group of machines.

[0623] Further embodiments of any of the embodiments described above in this disclosure may include a situation in which the resource distribution circuit further includes at least one of the following: a machine learning component, an artificial intelligence component, or a neural network component.

[0624] Provided herein are methods and systems for improving machines that enable markets, including improvements in efficiency, speed, reliability, etc., for participants in such markets.

[0625] This specification provides an improved machine that enables distributed transactions at scale among a large number of participants, including human participants and automated agents.

[0626] Specific systems and operations for improving or optimizing energy use and / or acquisition for computation, networking, and / or other tasks are described herein.

[0627] In one embodiment, a platform is provided for enabling transactions, which includes a machine equipped with renewable energy facilities that optimizes the allocation of energy supply generated between computing tasks, networking tasks, and energy consumption tasks.

[0628] In one embodiment, a platform is provided for enabling transactions, which includes a machine that automatically purchases energy in the energy futures market.

[0629] In one embodiment, a platform is provided for enabling transactions, which includes a machine that automatically purchases energy credits in the futures market.

[0630] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate purchases in the energy futures market.

[0631] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate the purchase of energy credits in the futures market.

[0632] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically purchases spectrum allocations in the network spectrum futures market.

[0633] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically sells computing capacity in a futures market for computing capacity.

[0634] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically sells its computed storage capacity in a futures market for storage capacity.

[0635] In one embodiment, a platform is provided to enable transactions, which has a machine for automatically selling energy storage capacity in the futures market for that energy storage capacity.

[0636] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically sells its network bandwidth in a futures market for network capacity.

[0637] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically purchase spectrum allocations in the network spectrum futures market.

[0638] In one embodiment, a platform is provided for enabling transactions, which has a group of machines that automatically optimize energy utilization for the allocation of computing tasks (e.g., Bitcoin mining).

[0639] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data related to the collective optimization of purchases in the energy futures market.

[0640] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data on the collective optimization of purchases of energy credits in the futures market.

[0641] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data on the collective optimization of purchases in the futures market for the network spectrum.

[0642] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data on the collective optimization of sales in the futures market for computing capacity.

[0643] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically purchases energy on the spot market for energy.

[0644] In one embodiment, a platform is provided to enable transactions, which includes a machine for automatically purchasing energy credits in the spot market.

[0645] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate purchases in the spot market for energy.

[0646] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate the purchase of energy credits in the spot market.

[0647] In one embodiment, a platform is provided for enabling transactions, which includes a machine that automatically purchases spectrum allocations in the spot market for network spectrum.

[0648] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically purchase spectrum allocations in the spot market for network spectra.

[0649] In one embodiment, a platform is provided for enabling transactions, which has a group of machines that automatically optimize energy utilization for the allocation of computing tasks (e.g., Bitcoin mining).

[0650] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data related to the collective optimization of energy spot market purchases.

[0651] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data related to the collective optimization of energy credit purchases in the spot market.

[0652] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate data on the collective optimization of purchases in the spot market across the network spectrum.

[0653] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically sell the aggregated computing capacity in a futures market for computing capacity.

[0654] In one embodiment, a platform is provided to enable transactions, which includes a group of machines that automatically sell the aggregated calculated storage capacity in the storage capacity futures market.

[0655] In one embodiment, a platform is provided to enable transactions, which includes a group of machines that automatically sell the aggregated energy storage capacity in the energy storage capacity futures market.

[0656] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically sell aggregated network bandwidth in a futures market for network capacity.

[0657] In one embodiment, a platform is provided for enabling transactions that has a machine that automatically predicts futures market prices for energy prices based on information collected from social media data sources.

[0658] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices of a network spectrum based on information collected from social media data sources.

[0659] In one embodiment, a platform is provided for enabling transactions that has a machine that automatically predicts the futures market price of energy credits based on information collected from social media data sources.

[0660] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market value of computing capacity based on information collected from social media data sources.

[0661] In one embodiment, a platform is provided for enabling transactions that has a machine to automatically execute an arbitrage strategy for buying and selling computing capacity by testing the spot market for computing capacity with small transactions and then quickly executing larger transactions based on the results of those small transactions.

[0662] In one embodiment, a platform is provided for enabling transactions that has a machine to automatically execute arbitrage strategies for buying and selling energy storage capacity by testing the spot market for computing capacity with small transactions and then quickly executing larger transactions based on the results of those small transactions.

[0663] In one embodiment, a platform is provided for enabling transactions that has a machine that automatically executes arbitrage strategies for buying and selling network spectrum or network bandwidth by testing the spot market for computing capacity with small transactions and then rapidly executing larger transactions based on the results of those small transactions.

[0664] In one embodiment, a platform is provided for enabling transactions that has a machine to automatically execute an arbitrage strategy for buying and selling energy by testing the spot market for computing capacity with small transactions and then rapidly executing larger transactions based on the results of those small transactions.

[0665] In one embodiment, a platform is provided for enabling transactions that has a machine to automatically execute arbitrage strategies for buying and selling energy credits by testing the spot market for computing capacity with small transactions and then quickly executing larger transactions based on the results of those small transactions.

[0666] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically allocates energy capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0667] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically allocates computing capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0668] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically allocates network capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0669] In one embodiment, a platform is provided for enabling transactions, which has a group of machines that automatically allocate collective energy capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0670] In one embodiment, a platform is provided for enabling transactions, which has a group of machines that automatically allocate collective computing capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0671] In one embodiment, a platform is provided for enabling transactions, which has a group of machines that automatically allocate collective network capacity among core tasks, computing tasks, energy storage tasks, data storage tasks, and network tasks.

[0672] Specific systems and / or operations that utilize a knowledge blockchain to enable transactions are described herein.

[0673] In one embodiment, a platform is provided for enabling transactions, which has a smart contract wrapper using a distributed ledger, the smart contract embedding intellectual property license terms for intellectual property embedded in the distributed ledger, and when an operation is performed on the distributed ledger, access to the intellectual property is provided and the executing party is committed to the intellectual property license terms.

[0674] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger for aggregating intellectual property license terms, and a smart contract wrapper on the distributed ledger enables adding intellectual property to an aggregated stack of intellectual property through operations on the ledger.

[0675] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger for aggregating intellectual property license terms, and a smart contract wrapper on the distributed ledger allows for the addition of intellectual property through operations on the ledger to agree on the distribution of royalties among the parties in the ledger.

[0676] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger for aggregating intellectual property license terms, and a smart contract wrapper on the distributed ledger enables adding intellectual property to an aggregated stack of intellectual property through operations on the ledger.

[0677] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger for aggregating intellectual property license terms, and a smart contract wrapper on the distributed ledger enables parties to commit to contract terms through operations on the ledger.

[0678] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes an instruction set so that operations on the distributed ledger provide provable access to the instruction set.

[0679] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes executable algorithmic logic so as to provide provable access to the executable algorithmic logic on the distributed ledger.

[0680] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes the instruction set of a 3D printer so that operations on the distributed ledger provide provable access to the instruction set.

[0681] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a set of instructions for a coding process, such that operations on the distributed ledger provide provable access to the instruction set.

[0682] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes the instruction set of a semiconductor manufacturing process, such that operations on the distributed ledger provide verifiable access to the manufacturing process.

[0683] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a firmware program so that operations on the distributed ledger provide certifiable access to the firmware program.

[0684] In one embodiment, a platform is provided for enabling transactions, which has a distributed ledger that tokenizes the instruction set of an FPGA so that operations on the distributed ledger provide certifiable access to the FPGA.

[0685] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes serverless code logic so that operations on the distributed ledger provide provable access to the serverless code logic.

[0686] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes the instruction set of a crystal manufacturing system so that operations on the distributed ledger provide provable access to the instruction set.

[0687] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a set of instructions for a food preparation process, such that operations on the distributed ledger provide provable access to the instruction set.

[0688] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a set of instructions for a polymer manufacturing process, such that operations on the distributed ledger provide provable access to the instruction set.

[0689] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a set of instructions for a chemical synthesis process, such that operations on the distributed ledger provide provable access to the instruction set.

[0690] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes a set of instructions for a biological production process, such that operations on the distributed ledger provide provable access to the set of instructions.

[0691] In one embodiment, a platform is provided for enabling transactions that has a distributed ledger in which trade secrets are tokenized with expert wrappers, such that operations on the distributed ledger provide verifiable access to the trade secrets, and the wrappers provide expert verification of the trade secrets.

[0692] In one embodiment, a platform is provided for enabling transactions, which has a distributed ledger that aggregates access to trade secrets in a chain that proves which parties and how many parties have viewed the trade secrets.

[0693] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that tokenizes an instruction set such that operations on the distributed ledger provide provable access to the instruction set, and execution of the instruction set on the system results in recording a transaction in the distributed ledger.

[0694] In one embodiment, a platform is provided for enabling transactions that includes a distributed ledger for tokenizing intellectual property items, and a reporting system for reporting analysis results based on operations performed on the distributed ledger or intellectual property.

[0695] In one embodiment, a platform is provided for enabling transactions, having a distributed ledger that aggregates instruction sets, and operations on the distributed ledger add at least one instruction to an existing instruction set to provide a modified instruction set.

[0696] This document describes specific systems and / or operations for using and / or executing transactions using intelligent cryptocurrencies or cryptocurrency transaction managers.

[0697] In one embodiment, a platform is provided for enabling transactions, which has a smart wrapper for a cryptocurrency coin that directs the execution of a transaction involving a coin to a geographic location based on at least one of the tax treatments of the coin and the transaction at the geographic location.

[0698] In one embodiment, a platform is provided for enabling transactions, which has a smart wrapper for a cryptocurrency coin that directs the execution of a transaction involving a coin to a geographic location based on at least one of the tax treatments of the coin and the transaction at the geographic location.

[0699] In one embodiment, a platform is provided for enabling transactions, which has a self-executing cryptocurrency coin that commits transactions when it recognizes location-based parameters that provide favorable tax treatment.

[0700] In one embodiment, a platform is provided for enabling transactions, which has an expert system that uses machine learning to optimize the execution of cryptocurrency transactions based on tax circumstances.

[0701] In one embodiment, a platform is provided for enabling cryptocurrency transactions, which includes an expert system that aggregates regulatory information covering cryptocurrency transactions and automatically selects the jurisdiction for the operation based on that regulatory information.

[0702] In one embodiment, a platform is provided for enabling transactions, which has an expert system that uses machine learning to optimize the execution of cryptocurrency transactions based on real-time energy price information for available energy sources.

[0703] In one embodiment, a platform is provided for enabling transactions, which has an expert system that uses machine learning to optimize the execution of cryptocurrency transactions based on an understanding of the available energy sources to power the computing resources for executing the transactions.

[0704] In one embodiment, a platform is provided for enabling transactions, which has an expert system that uses machine learning to optimize the charging and recharging cycles of a rechargeable battery system and provides energy for executing cryptocurrency transactions.

[0705] Specific systems and operations for making forward market forecasts and / or enabling transactions utilizing forward market forecasts are described herein. In certain embodiments, the futures market forecasts described herein include non-traditional data and / or data for futures market forecasts that are not available in previously known systems.

[0706] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures prices in the market based on an understanding gained by analyzing data sources from the Internet of Things, and executes transactions based on that prediction of futures prices.

[0707] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures prices in the market based on an understanding obtained by analyzing data sources from social networks, and executes transactions based on that prediction of futures prices.

[0708] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the market based on an understanding obtained by analyzing data sources from the Internet of Things, and executes cryptocurrency transactions based on that prediction of futures prices.

[0709] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the market based on an understanding obtained by analyzing data sources from social networks, and executes cryptocurrency transactions based on that prediction of futures prices.

[0710] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures prices in the energy market based on an understanding gained by analyzing data sources from the Internet of Things, and executes transactions based on that futures price prediction.

[0711] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the energy market based on an understanding obtained by analyzing data sources from social networks, and executes transactions based on that futures price prediction.

[0712] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the market for computing resources based on an understanding gained by analyzing data sources from the Internet of Things, and executes transactions based on that futures price prediction.

[0713] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures prices in the spectrum or network bandwidth market based on an understanding gained by analyzing data sources from the Internet of Things, and executes transactions based on that futures price prediction.

[0714] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the market for computing resources based on an understanding obtained by analyzing data sources from social networks, and executes transactions based on that futures price prediction.

[0715] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures rates in the advertising market based on an understanding gained by analyzing data sources from the Internet of Things, and executes transactions based on that futures rate prediction.

[0716] In one embodiment, a platform is provided for enabling transactions, which has an expert system that predicts futures prices in the advertising market based on an understanding obtained by analyzing data sources from social networks, and executes transactions based on that futures price prediction.

[0717] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices for energy prices based on information collected from an automated agent's behavioral data source.

[0718] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices of a network spectrum based on information collected from an automated agent behavior data source.

[0719] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market price of energy credits based on information collected from an automated agent's behavioral data source.

[0720] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market value of computing capacity based on information collected from an automated agent behavior data source.

[0721] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices for energy prices based on information collected from a business entity's behavioral data source.

[0722] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices of a network spectrum based on information collected from a business entity's behavioral data source.

[0723] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market price of energy credits based on information collected from the behavioral data sources of an entity.

[0724] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market value of computing capacity based on information collected from a business entity's behavioral data source.

[0725] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices for energy prices based on information collected from human behavior data sources.

[0726] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts futures market prices of a network spectrum based on information collected from human behavior data sources.

[0727] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market price of energy credits based on information collected from human behavior data sources.

[0728] In one embodiment, a platform is provided for enabling transactions, which has a machine that automatically predicts the futures market value of computing capacity based on information collected from human behavior data sources.

[0729] In one embodiment, a platform is provided for enabling transactions, which includes an expert system that predicts futures prices in a spectrum or network bandwidth market based on an understanding obtained by analyzing social data sources, and executes transactions based on that futures price prediction.

[0730] In one embodiment, a platform is provided for enabling transactions, which has an intelligent agent configured to request attention resources from another external intelligent agent.

[0731] In one embodiment, a platform is provided for enabling transactions, which includes a machine for automatically purchasing a target resource in a target futures market.

[0732] In one embodiment, a platform is provided for enabling transactions, which includes a group of machines that automatically aggregate purchases in a particular futures market.

[0733] Provided herein are flexible, intelligent energy and computing facilities, as well as resource management systems for intelligent energy and computing facilities, including components, systems, services, modules, programs, processes, and other enabling elements, such as functions for data collection, storage, and processing; automatic configuration of inputs, resources, and outputs; and training on a training set of data collected from data sources to train an artificial intelligence / machine learning system to optimize the facility's outcomes, facility parameters, and parameters associated with such facilities.

[0734] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment results, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to predict the likelihood of the equipment's production results.

[0735] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment results, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to predict the production results of the equipment.

[0736] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment outcomes, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to optimize the provisioning and allocation of energy and computing resources and to generate favorable equipment resource utilization profiles among a set of available profiles.

[0737] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment outcomes, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to optimize the provisioning and allocation of energy and computing resources and to generate favorable equipment resource output selections from a set of available outputs.

[0738] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment outcomes, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to optimize the rebilling and provisioning of available energy and computing resources and to generate favorable equipment input resource profiles among a set of available profiles.

[0739] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment outcomes, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to optimize the configuration of available energy and computing resources and to generate a favorable equipment resource configuration profile among a set of available profiles.

[0740] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment results, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to optimize the selection and configuration of an artificial intelligence system and to generate a favorable equipment output profile among a set of available artificial intelligence systems and configurations.

[0741] In embodiments, provided herein is an information technology system for providing data to an intelligent energy and computing equipment resource management system, which has a system for learning on a training set of equipment results, equipment parameters, and data collected from data sources in order to train an artificial intelligence / machine learning system to generate instructions that current or future customers should contact about the outputs that may be provided by the equipment.

[0742] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility within a set of available configurations based on a set of conditions detected in relation to at least one of input resources, facility resources, output parameters, and external conditions related to the output of the facility.

[0743] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility from a set of available configurations based on a set of conditions detected in relation to a set of input resources.

[0744] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility within a set of available configurations based on a set of conditions detected in relation to a set of facility resources.

[0745] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility from a set of available configurations based on a set of conditions detected in relation to output parameters.

[0746] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility within a set of available configurations based on a set of conditions detected in relation to utilization parameters for the output of the facility.

[0747] In embodiments, provided herein is a system having an intelligent and flexible energy and computing facility, wherein an artificial intelligence / machine learning system configures the facility in a set of available configurations based on a set of parameters received from a digital twin for the facility.

[0748] An exemplary transaction-enabled system includes a controller having: a focus market access circuit configured to interpret a number of focus-related resources available in the focus market; an intelligent agent circuit configured to determine the acquisition value of a focus-related resource based on a cost parameter of at least one of the number of focus-related resources; and a focus acquisition circuit configured to claim the focus-related resource according to its acquisition value.

[0749] Specific further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system includes cases where the attention acquisition circuit is further configured to perform a request for attention-related resources by performing at least one operation selected from a plurality of operations consisting of purchasing attention-related resources from the attention market, selling attention-related resources to the attention market, making an offer to sell attention-related resources to a second intelligent agent, and making an offer to purchase attention-related resources to the second intelligent agent. The exemplary system includes cases where a plurality of attention-related resources include at least one resource selected from a list consisting of ad placements, search lists, keyword lists, banner ads, video ads, embedded video ads, panel activity participation, survey activity participation, trial activity participation, and pilot activity placement or participation. An exemplary system includes one or more of the following: the market of interest includes a spot market for at least one of a number of interest-related resources; the cost parameter of at least one of the number of interest-related resources includes a projected future cost for at least one of the number of interest-related resources; an intelligent agent circuit is further configured to identify the acquisition value of the interest-related resource in response to a comparison of the cost in the spot market with the cost parameter; the market of interest includes a futures market for at least one of the number of interest-related resources; the cost parameter of at least one of the number of interest-related resources includes a projected future cost; the cost parameter of at least one of the number of interest-related resources includes a projected future cost for at least one of the number of interest-related resources; and an intelligent agent circuit is further configured to identify the acquisition value of the interest-related resource in response to a comparison of the cost in the futures market with the cost parameter. An exemplary system includes an intelligent agent circuit further configured to identify the acquisition value of an interest-related resource in response to at least one of the number of interest-related resources having a cost parameter that falls outside the expected cost range for at least one of the number of interest-related resources.An exemplary system includes an intelligent agent circuit further configured to identify the acquisition value of a focus-related resource in accordance with a function of at least one cost parameter and / or effectiveness parameter of at least one of a number of focus-related resources. In certain further embodiments, the exemplary controller further includes an external data circuit configured to interpret a social media data source, and the intelligent agent circuit is further configured to identify at least one future predicted cost of at least one of a number of focus-related resources in accordance with the social media data source, and to utilize that future predicted cost as a cost parameter and / or effectiveness parameter of at least one of the number of focus-related resources.

[0750] An exemplary system includes a group of machines, each machine including a task system having a core task and at least one of a computation task or a network task. The system includes a controller, which has: a focus market access circuit configured to interpret a number of focus-related resources available in the focus market; an intelligent agent circuit configured to determine the acquisition value of a focus-related resource based on at least one cost parameter of the number of focus-related resources, and further based on the core task for the corresponding machine in the group of machines; a focus purchase aggregation circuit configured to determine the total purchase price of the focus-related resources in accordance with the acquisition values ​​of the number of focus-related resources from each intelligent agent circuit corresponding to each machine in the group of machines; and a focus acquisition circuit configured to purchase the focus-related resources in accordance with the total purchase price of the focus-related resources.

[0751] Specific further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system includes a case where the focus purchase aggregation circuit is located at a location selected from a plurality of locations, which are at least partially distributed on a plurality of controllers corresponding to machines in a group of machines; on a selected controller corresponding to one of the machines in a group of machines; and on a system controller that is communicatively connected to a plurality of controllers corresponding to machines in a group of machines. The exemplary system includes a case where the focus purchase acquisition circuit is located at a location selected from a plurality of locations, which are at least partially distributed on a plurality of controllers corresponding to machines in a group of machines; on a selected controller corresponding to one of the machines in a group of machines; and on a system controller that is communicatively connected to a plurality of controllers corresponding to machines in a group of machines.

[0752] An exemplary procedure includes: interpreting a number of attention-related resources available in the attention market; identifying the acquisition value of the attention-related resources based on a cost parameter of at least one of the number of attention-related resources; and billing for the attention-related resources according to their acquisition value.

[0753] Specific further aspects of the exemplary procedure are described below, any one or more of which may be present in a particular embodiment. The exemplary procedure further includes performing an operation to claim a focus resource by performing at least one operation selected from a plurality of operations consisting of purchasing the focus resource from a focus market; selling the focus resource to a focus market; offering the sale of the focus resource to a second intelligent agent; and offering the purchase of the focus resource to the second intelligent agent. The exemplary procedure includes a case where the cost parameter of at least one of a number of focus resources includes the projected future cost of at least one of the number of focus resources, and the method further includes identifying the acquisition value of the focus resource in accordance with a comparison of the cost in the spot market with the cost parameter. The exemplary procedure further includes an operation to interpret a social media data source, and an operation to identify the projected future cost of at least one of a number of focus resources in accordance with that social media data source, and to utilize that projected future cost as a cost parameter and / or effectiveness parameter of at least one of the number of focus resources. The exemplary procedure further includes cases where the operation to identify the acquisition value of the relevant resource is further based on at least one of the future projected cost or effectiveness parameters.

[0754] An exemplary procedure includes: interpreting a number of attention-related resources available in the attention market; determining the acquisition value of the attention-related resources for each machine in the machine group based on at least one cost parameter among the number of attention-related resources, and further based on the core task for each corresponding machine in the machine group; determining the total purchase price of the attention-related resources in accordance with the acquisition values ​​of the number of attention-related resources corresponding to each machine in the machine group; and purchasing the attention-related resources in accordance with the total purchase price of the attention-related resources.

[0755] Specific further aspects of the exemplary procedure are described below, any one or more of which may be present in a particular embodiment. The exemplary procedure includes a case where the cost parameter of at least one of a number of interest-related resources includes the projected future cost of at least one of the number of interest-related resources, and the procedure further includes an operation to identify the acquisition value of each interest-related resource in accordance with a comparison of the spot market cost of the interest-related resource with the cost parameter. The exemplary procedure further includes an operation to interpret a social media data source, and in accordance with that social media data source, to identify the projected future cost of at least one of the number of interest-related resources, and to use that projected future cost as a cost parameter and / or effectiveness parameter of at least one of the number of interest-related resources. The exemplary procedure further includes an operation to identify the acquisition value of an interest-related resource based on the projected future cost and / or effectiveness parameter. [Brief explanation of the drawing]

[0756] [Figure 1] This is a schematic diagram of the components of a platform for enabling intelligent transactions according to embodiments of the present disclosure.

[0757] [Figure 2A] This is a schematic diagram of additional components of a platform for enabling intelligent transactions according to embodiments of the present disclosure. [Figure 2B] This is a schematic diagram similar to Figure 2A.

[0758] [Figure 3] This is a schematic diagram of additional components of a platform for enabling intelligent transactions according to embodiments of the present disclosure.

[0759] [Figure 4]This is a schematic diagram of an embodiment of a neural network system that may be connected to, integrated into, and accessible from a platform for enabling intelligent transactions, including an expert system, self-organizing, machine learning, and artificial intelligence, and a neural network system trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assistance for autonomous control, and other purposes according to embodiments of the present disclosure. [Figure 5] This is a schematic diagram similar to Figure 4. [Figure 6] This is a schematic diagram similar to Figure 4. [Figure 7] This is a schematic diagram similar to Figure 4. [Figure 8] This is a schematic diagram similar to Figure 4. [Figure 9] This is a schematic diagram similar to Figure 4. [Figure 10] This is a schematic diagram similar to Figure 4. [Figure 11] This is a schematic diagram similar to Figure 4. [Figure 12] This is a schematic diagram similar to Figure 4. [Figure 13] This is a schematic diagram similar to Figure 4. [Figure 14] This is a schematic diagram similar to Figure 4. [Figure 15] This is a schematic diagram similar to Figure 4. [Figure 16] This is a schematic diagram similar to Figure 4. [Figure 17] This is a schematic diagram similar to Figure 4. [Figure 18] This is a schematic diagram similar to Figure 4. [Figure 19] This is a schematic diagram similar to Figure 4. [Figure 20] This is a schematic diagram similar to Figure 4. [Figure 21] This is a schematic diagram similar to Figure 4. [Figure 22] This is a schematic diagram similar to Figure 4. [Figure 23] This is a schematic diagram similar to Figure 4. [Figure 24]This is a schematic diagram similar to Figure 4. [Figure 25] This is a schematic diagram similar to Figure 4. [Figure 26] This is a schematic diagram similar to Figure 4. [Figure 27] This is a schematic diagram similar to Figure 4. [Figure 28] This is a schematic diagram similar to Figure 4. [Figure 29] This is a schematic diagram similar to Figure 4. [Figure 30] This is a schematic diagram similar to Figure 4. [Figure 31] This is a schematic diagram similar to Figure 4.

[0760] [Figure 32] This is a schematic diagram of the components of an environment, including an intelligent energy and computing facility, a resource management platform for the host intelligent energy and computing facility, a set of data sources, a set of expert systems, an interface to a market platform and a set of external resources, and a set of user or client systems and devices, according to embodiments of the present disclosure.

[0761] [Figure 33] This is a schematic diagram of an energy and computing resource platform according to an embodiment of the present disclosure.

[0762] [Figure 34] This is an exemplary description of a data record schema according to an embodiment of the present disclosure. [Figure 35] This is a similar depiction to Figure 34.

[0763] [Figure 36] This is a schematic diagram of a cognitive processing system according to an embodiment of the present disclosure.

[0764] [Figure 37] This is a schematic flowchart illustrating the procedure for selecting leads according to embodiments of the present disclosure.

[0765] [Figure 38] This is a schematic flowchart of the procedure for generating a lead list according to the embodiments of this disclosure.

[0766] [Figure 39] This is a schematic flowchart of the procedure for generating customized equipment characteristics according to embodiments of the present disclosure.

[0767] [Figure 40] This is a schematic diagram of the system, including the smart contract wrapper.

[0768] [Figure 41] This is a schematic flowchart illustrating how to execute a smart contract wrapper.

[0769] [Figure 42] This is a schematic flowchart illustrating how to update the aggregated IP stack.

[0770] [Figure 43] This is a schematic flowchart illustrating how to add assets and entities.

[0771] [Figure 44] This is a schematic flowchart illustrating how to update the aggregated IP stack.

[0772] [Figure 45] This is a schematic diagram of a system that provides provable access to an instruction set.

[0773] [Figure 46] This is a schematic flowchart illustrating a method for providing provable access to the instruction set.

[0774] [Figure 47] This is a schematic diagram of a system that provides provable access to algorithmic logic.

[0775] [Figure 48] This is a schematic flowchart illustrating a method for providing provable access to executable algorithmic logic.

[0776] [Figure 49] This is a schematic diagram of a system that provides verifiable access to firmware.

[0777] [Figure 50] This is a schematic flowchart illustrating a method for providing verifiable access to firmware.

[0778] [Figure 51] This is a schematic diagram of a system that provides verifiable access to serverless code logic.

[0779] [Figure 52] This is a schematic flowchart illustrating how to provide verifiable access to serverless code logic.

[0780] [Figure 53] This is a schematic diagram of a system that provides verifiable access to aggregated datasets.

[0781] [Figure 54] This is a schematic flowchart illustrating a method for providing verifiable access to aggregated datasets.

[0782] [Figure 55] This is a schematic diagram of a system that analyzes and reports on an aggregated stack of IP.

[0783] [Figure 56] This is a schematic flowchart illustrating how to analyze and report on an aggregated IP stack.

[0784] [Figure 57] This is a schematic diagram of a system that improves the resource utilization rate of a task system.

[0785] [Figure 58] This is a schematic flowchart illustrating a method for improving the resource utilization rate of a task system.

[0786] [Figure 59] This is a schematic flowchart illustrating a method for improving resource utilization using alternative resources.

[0787] [Figure 60] This is a schematic flowchart illustrating a method for improving resource utilization using behavioral data.

[0788] [Figure 61] This is a schematic flowchart illustrating a method for improving the resource utilization rate of a task system.

[0789] [Figure 62] This is a schematic diagram of a system that improves the results of cryptocurrency transaction requests.

[0790] [Figure 63] This is a schematic flowchart illustrating a method for improving the results of cryptocurrency transaction requests.

[0791] [Figure 64] This is a schematic flowchart illustrating a method for improving the outcome of cryptocurrency transaction requests.

[0792] [Figure 65] This is a schematic diagram of a system that improves the results of cryptocurrency transaction requests.

[0793] [Figure 66] This is a schematic flowchart illustrating a method for improving the outcome of cryptocurrency transaction requests.

[0794] [Figure 67] This is a schematic diagram of a system that improves the execution of cryptocurrency transactions.

[0795] [Figure 68] This is a schematic flowchart illustrating methods for improving the execution of cryptocurrency transactions.

[0796] [Figure 69] This is a schematic diagram of a system designed to improve transaction operations in key markets.

[0797] [Figure 70] This is a schematic flowchart outlining methods for improving transaction operations in key markets.

[0798] [Figure 71] This is a schematic diagram of a system that aggregates the acquisition of target resources for a group.

[0799] [Figure 72] This is a schematic flowchart illustrating the method for aggregating the acquisition of target resources for a group.

[0800] [Figure 73] This is a schematic diagram of a system that improves the prediction of production equipment performance.

[0801] [Figure 74] This is a schematic flowchart illustrating a method for improving the forecasting of production equipment performance.

[0802] [Figure 75] This is a schematic diagram of a system for improving equipment resource parameters.

[0803] [Figure 76] This is a schematic flowchart illustrating a method for improving equipment resource parameters.

[0804] [Figure 77] This is a schematic diagram of a system that improves equipment output values.

[0805] [Figure 78] This is a schematic flowchart illustrating a method for improving equipment output values.

[0806] [Figure 79] This is a schematic flowchart illustrating a method for improving equipment production forecasting.

[0807] [Figure 80] This is a schematic diagram of a system that improves the utilization rate of equipment resources.

[0808] [Figure 81] This is a schematic flowchart illustrating methods for improving the utilization rate of equipment resources.

[0809] [Figure 82] This is a schematic diagram of a system that improves the performance of equipment resources by adjusting the equipment configuration.

[0810] [Figure 83] This is a schematic diagram of a system that uses digital twins to improve the performance of equipment resources.

[0811] [Figure 84] This is a schematic flowchart illustrating a method for improving the performance of equipment resources using digital twins.

[0812] [Figure 85] This is a schematic diagram of a system that improves the supply of renewable energy for facilities.

[0813] [Figure 86] This is a schematic flowchart illustrating methods for improving the supply of renewable energy for facilities.

[0814] [Figure 87] This is a schematic diagram of a system that improves resource acquisition for equipment.

[0815] [Figure 88] This is a schematic flowchart illustrating methods for improving resource acquisition for equipment.

[0816] [Figure 89] This is a schematic diagram of a system that improves resource acquisition for a group of machines.

[0817] [Figure 90] This is a schematic flowchart illustrating methods for improving resource acquisition for a group of machines.

[0818] [Figure 91] This is a schematic diagram of a system for improving resource utilization for a group of machines.

[0819] [Figure 92] This is a schematic flowchart illustrating a method for improving resource utilization for a group of machines.

[0820] [Figure 93] This is a schematic diagram of a system that improves the resource utilization rate of machinery.

[0821] [Figure 94] This is a schematic flowchart illustrating methods for improving machine resource utilization.

[0822] [Figure 95] This is a schematic diagram of a system for improving resource utilization for a group of machines.

[0823] [Figure 96] This is a schematic flowchart illustrating a method for improving resource utilization for a group of machines.

[0824] [Figure 97] This is a schematic diagram of a system that improves resource utilization for machines using social media data and futures resource markets.

[0825] [Figure 98] This is a schematic flowchart illustrating a method for improving resource utilization for machines using social media data and futures resource markets.

[0826] [Figure 99] This is a schematic diagram of a system that improves resource utilization using arbitrage operations.

[0827] [Figure 100] This is a schematic flowchart illustrating a method for improving resource utilization using arbitrage operations.

[0828] [Figure 101] This is a schematic diagram of a device that improves resource utilization using arbitrage operations.

[0829] [Figure 102] This is a schematic diagram of a system that improves resource allocation for machines.

[0830] [Figure 103] This is a schematic flowchart illustrating how to improve resource allocation for machines.

[0831] [Figure 104] This is a schematic diagram of a system that improves the centralized supply of resources for a group of machines.

[0832] [Figure 105] This is a schematic flowchart illustrating a method for improving the centralized resource supply for a group of machines.

[0833] [Figure 106] This is a schematic diagram of a device that improves the centralized supply of resources for a group of machines.

[0834] [Figure 107] This is a schematic diagram of a system that improves the supply of resources for machinery using futures resource markets.

[0835] [Figure 108] This is a schematic flowchart illustrating a method for improving resource supply for machinery using futures resource markets.

[0836] [Figure 109] This is a schematic flowchart illustrating a method for improving resource supply using alternative resources. [Modes for carrying out the invention]

[0837] Referring to Figure 1, a set of systems, methods, components, modules, machines, articles, blocks, circuits, services, programs, applications, hardware, software, and other elements are provided, which are interchangeably referred to herein as System 100 or Platform 100. Platform 100 enables a wide range of machines, systems, and other components that facilitate transactions involving the exchange of value for various goods, services, and resources in various markets, including current markets or spot markets 170, futures markets 130, etc. (e.g., using currencies, cryptocurrencies, tokens, rewards, etc., and using a wide range of physical and other resources). As used herein, “Currency” should be understood to include fiat currency issued or regulated by a government, cryptocurrency, tokens of value, tickets, points, reward points, coupons, credits (e.g., credits for regulations, emissions, and / or industry-recognized exchangeable units), abstract versions thereof (such as any value indices between parties that are understood to be usable in future transactions, etc.), deliverables (e.g., contractual delivery of goods or services including service uptime, time values ​​used for averaging, or time values ​​of delivery that may be exchanged between other parties or used to offset other exchanged values), and other elements that represent or may be exchanged for value. Resources that may be exchanged for value in the market should be understood to include goods, services, natural resources, energy resources, computing resources, energy storage resources, data storage resources, network bandwidth resources, processing resources, etc., and shall include resources on which value is exchanged and resources that enable transactions to occur (such as necessary computing and processing resources, storage resources, network resources, and energy resources that enable transactions).Any currency and / or resource may be abstracted to a uniform and / or normalized measure of value, which may additionally or alternatively include temporal aspects (e.g., calendar days, seasonal adjustments, and / or appropriate circumstances relating to the value of the currency and / or resource at the time of the transaction) and / or exchange rate aspects (e.g., accounting for the value of the currency or resource compared to another similar unit, such as an exchange rate between countries, or a discount on goods or services compared to the most desirable or requested configuration of the goods or services).

[0838] Platform 100 may include a set of futures trading machines 110, each of which may be configured as an expert system or automated intelligent agent to interact with one or more of the set of spot markets 170 (e.g., see Figure 2A) and futures markets 130. Enabling the set of futures trading machines 110 are: an intelligent resource purchasing system 164 having a set of intelligent agents for purchasing resources in the spot and futures markets; an intelligent resource allocation and adjustment engine 168 for intelligent selling of allocated or adjusted resources, such as computing resources, energy resources, and other resources involved in or enabling transactions; an intelligent selling engine 172 for intelligently adjusting the sale of allocated resources in the spot and futures markets; and an automated spot market testing and arbitrage execution engine 194 for performing spot tests of the spot and futures markets, such as microtransactions, and automatically executing transactions on resources that take advantage of favorable conditions if the conditions indicate favorable arbitrage conditions. Each engine may use a model-based or rule-based expert system, such as one based on rules or heuristics, and a deep learning system in which the rules or heuristics may be learned over trials involving a large number of input sets. The engine may use any of the expert systems and artificial intelligence capabilities described throughout this disclosure. Interactions within platform 100, including interactions of all platform components, interactions between them, and interactions with various markets, may be tracked and collected, such as a data aggregation system 144 for aggregating data on purchases and sales in various markets by the set of machines described in this disclosure. Aggregated data may include tracking data and result data, which may be fed into artificial intelligence systems and machine learning systems, for example, to train or supervise the same.

[0839] The operations for aggregating information referenced throughout this disclosure should be broadly understood. Examples of operations for aggregating information (e.g., data, purchasing information, regulatory information, or any other parameters) include, but are not limited to, summaries, averaging of data values, selected binning of data, derived information about data (e.g., rate of change, area under the curve, change in indicated state based on data, threshold exceedance or compliance, etc.), changes in data (e.g., arrival of new information or a new type of information, information that occurred during a defined or selected period, etc.), and / or classification descriptions of data or other information related to data. It will be understood that the representation of aggregated information may be as desired, including at least graphical information, reports, display generation and / or stored raw data, tables, and / or data streams for further use by artificial intelligence and / or machine learning systems. In certain embodiments, aggregated data may be used by expert systems, artificial intelligence, and / or machine learning systems to perform the various operations described throughout this disclosure. Furthermore, or alternatively, expert systems, artificial intelligence, and / or machine learning systems may interact with aggregated data, including determining which parameters should be aggregated and / or aggregation criteria to be used. For example, a machine learning system for a system utilizing the futures energy purchase market may be configured to aggregate purchases for the system. In exemplary embodiments, the machine learning system may be configured to determine signal-effective parameters for incrementally improving and / or optimizing purchase decisions, and additionally or alternatively, it may modify aggregation parameters—for example, determining binning criteria for various components of the system (e.g., components that respond similarly in terms of energy demands), timeframes for aggregation (e.g., weekly, monthly, seasonal, etc.), and / or modifying the type of average, a baseline rate for the rate of change of values ​​within the system, or such. The examples provided are illustrative and are not limited to any systems or operations described throughout this disclosure.

[0840] Various engines may operate on various data sources, including aggregated data from marketplace transactions, tracking data on the operation of each engine, and a set of external data sources 182, which include social media data sources 180 (such as social networking sites like Facebook® and Twitter®), Internet of Things (IoT) data sources (including those from sensors, cameras, data collectors, appliances, personal devices, and / or instrumented machines and systems), such as IoT sources that provide information about machines and systems that enable transactions, and machines and systems involved in the production and consumption of resources. External data sources 182 may also include behavioral data sources such as automated agent behavior data sources 188 (such as tracking and reporting the behavior of automated agents used for conversation and interaction management, agents used for machine and system control functions, agents used for purchasing and selling, agents used for data collection, agents used for advertising), and human behavior data sources (such as data sources that track online behavior, data sources that track mobility behavior). IoT, social, and behavioral data sources 190 (such as energy consumption behavior, energy production behavior, network usage behavior, computation and processing behavior, resource consumption behavior, resource production behavior, purchasing behavior, attention behavior, social behavior, etc.) and entity behavior data sources 190 (behavior of entities such as business organizations, e.g., purchasing behavior, consumption behavior, production behavior, market activity, merger and acquisition behavior, transaction behavior, location behavior, etc.). IoT, social, and behavioral data from and about sensors, machines, humans, entities, and automated agents may be used collectively to personify expert systems, machine learning systems, and other intelligent systems and engines described throughout this disclosure, such as being provided as input to deep learning systems and provided as feedback or results for the purpose of training, supervising, and iteratively improving systems for prediction, forecasting, classification, automation, and control.The data may be organized as a stream of events. The data may be stored in a distributed ledger or other distributed system. The data may be stored in a knowledge graph where nodes represent entities and links represent relationships. The external data source 182 may be queried via various database query functions. The external data source 182 may be accessed via APIs, brokers, connectors, protocols such as REST and SOAP, and other data ingestion and extraction techniques. The data may be enriched with metadata and transformed and loaded into a form suitable for consumption by the engine through cleansing, normalization, deduplication, etc.

[0841] Platform 100 may include a set of intelligent predictive engines 192 for predicting events, activities, variables, and parameters such as spot markets 170, futures markets 130, resources traded in such markets, resources enabling such markets, actions (such as any of which are tracked by external data sources 182), and transactions. The intelligent predictive engines 192 may operate on data from a data aggregation system 144 and data from external data sources 182 regarding elements of Platform 100. The platform may include a set of intelligent transaction engines 136 for automatically executing transactions in the spot markets 170 and futures markets 130. This may include executing intelligent cryptocurrency transactions with an intelligent cryptocurrency execution engine 183, as will be described in more detail below. Platform 100 may also utilize assets of an improved distributed ledger 113 and improved smart contracts 103, which include embedding and operating proprietary information, instruction sets, etc., that enable the occurrence of complex transactions between individuals with reduced (or no) reliance on intermediaries. In certain embodiments, platform 100 may include a distributed processing architecture 146—for example, distributing processing or computing tasks across multiple processing units, clusters, servers, and / or third-party service or cloud devices. These and other components are described in more detail throughout this disclosure. In certain embodiments, one or more aspects of the platform referenced in Figures 1 to 3 may be performed by any system, device, controller, or circuit as described throughout this disclosure. In certain embodiments, one or more aspects of the platform referenced in Figures 1 to 3 may include any procedures, methods, or operations described throughout this disclosure. The exemplary platform depicted in Figures 1 to 3 is exemplary, and any aspect may be omitted or modified while providing one or more benefits described throughout this disclosure.

[0842] Referring to the block diagrams in Figures 2A-2B, further details and additional components of platform 100 and their interactions are depicted. The set of futures trading machines 110 may also include a regeneration capacity allocation engine 102 (for example, an engine for allocating energy generation or regeneration capacity, where energy is allocated for one or more of the following purposes, such as selling in the futures market 130, selling in the spot market 170, use upon transaction completion (e.g., mining for cryptocurrency), or other purposes, such as in a hybrid vehicle or system including energy generation or regeneration capacity, in a renewable energy system with energy storage, or in other energy storage systems). For example, the regeneration capacity allocation engine 102 may explore available options for using stored energy, such as selling it in current and futures energy markets (e.g., energy futures market 122, energy market 148, energy storage futures market 174, and / or energy storage market 178) that accept energy from producers, holding energy in storage for future use, or using energy for work (which could include processing activities for a platform such as data collection or processing, or processing activities for executing transactions, including mining activities for cryptocurrency). In certain embodiments, the regeneration capacity allocation engine 102 may consider, for example, energy leaks (e.g., losses over time when stored), useful future work activities that are expected to occur, competing factors that may affect stored energy (e.g., the release of stored water that is expected to occur at a future point in time for purposes other than energy), and / or predictable future energy regeneration that may affect the value proposition of stored energy (e.g., if energy is being held, the amount of energy stored will be exceeded, and / or the value of available useful work activities will change over the relevant time horizon).In certain embodiments, the regeneration capacity allocation engine 102 includes, for example, a rate value of stored energy that takes into account the incremental cost or benefit of rapidly utilizing the stored energy (e.g., low energy utilization at the moment is cost-effective, but high energy utilization at the moment is not). In certain embodiments, the regeneration capacity allocation engine 102 takes into account externalities that are outside the immediate economic considerations of the system. For example, the impact of energy utilization on reservoirs or downstream riverbeds, the impact on grid capacity and / or grid dynamics of providing or not providing energy from energy storage, and / or the impact on the system of providing or not providing energy (e.g., drastically increasing server utilization with cheap energy immediately before a long holiday that may incur overtime pay for service and maintenance personnel, which may affect the economic benefits of receiving, storing, or utilizing energy). The examples provided are for illustrative purposes only and are not limited to the systems or operations described throughout this disclosure.

[0843] The set of futures trading machines 110 may include an energy trading machine 104 for buying or selling energy, such as an energy spot market 148 or an energy futures market 122. The energy trading machine 104 may use an expert system, neural network or other intelligence to determine the timing of purchases based on current and projected state information regarding energy pricing and availability, and on current and projected state information regarding energy needs, such as computational tasks, cryptocurrency mining, data collection actions, and work performed by automated agents and systems, and work required of humans or entities based on their actions. For example, the energy purchasing machine may, by machine learning, recognize that, based on an increase in orders or market demand, operators are likely to need blocks of energy to perform increased levels of manufacturing, and purchase energy at a favorable price in the futures market based on a combination of energy market data and entity behavior data. To continue the example, market demand may be understood by machine learning, such as by processing human behavior data sources 184, such as social media posts indicating increased demand, e-commerce transaction data, etc. The energy trading machine 104 may sell energy on the energy spot market 148 or the energy futures market 122. The sales may also be carried out by an expert system operating on various data sources described herein, which includes training and human supervision regarding the results.

[0844] The set of futures trading machines 110 may include a renewable energy credit (REC) trading machine 108, which may purchase renewable energy credits, pollution credits, and other environmental or regulatory credits on the spot market 150 or futures market 124 for such credits. Purchases may be comprised of and managed by an expert system operating on data aggregated by any of the external data sources 182 or by a set of data aggregation systems 144 for the platform. Renewable energy credits and other credits may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, where the credits are purchased at a favorable time based on an understanding of supply and demand determined by processing inputs from data sources. The expert system may be trained on a dataset of results from purchases under historical input conditions. The expert system may be trained on a dataset of human purchase decisions and / or may be supervised by one or more human operators. The Renewable Energy Credit (REC) trading machine 108 may sell renewable energy credits, pollution credits, and other environmental or regulatory credits on the spot market 150 or futures market 124 for such credits. The sales may also be conducted by an expert system operating on the various data sources described herein, which includes training and human supervision of the results.

[0845] The set of futures trading machines 110 may include a focus trading machine 112, which may purchase one or more focus-related resources in a focus spot market 152 or a focus futures market 128, such as advertising space, search listings, keyword listings, banner ads, participation in a panel or survey activity, participation in a trial or pilot, or the like. Focus resources may include focus on automated agents such as bots, crawlers, and conversation managers used for searching, shopping, and purchasing. The purchase of focus resources may be configured and managed by an expert system operating on data aggregated by any of the external data sources 182 or by a set of data aggregation systems 144 for the platform. Focus resources may be purchased by an automated system using an expert system, including machine learning or other artificial intelligence, where resources are purchased at favorable times, such as based on an understanding of supply and demand, determined by processing inputs from various data sources. For example, the focus trading machine 112 may purchase advertising space in a futures market for advertising based on learning from extensive inputs on market conditions, behavioral data, and data on the activities of agents and systems within the platform 100. The expert system may be trained on a dataset of purchase results under past input conditions. The expert system may be trained on a dataset of human purchase decisions and / or supervised by one or more human operators. The featured trading machine 112 may also sell one or more featured-related resources in the featured spot market 152 or featured futures market 128, such as advertising space, search listings, keyword listings, banner ads, participation in panel or research activities, participation in trials or pilots, which may include access to one or more automated agents on platform 100, or providin...

Claims

1. A transaction-enabled system including equipment and controllers, The aforementioned equipment is used to perform a core task, and the core task includes customer-related output. The aforementioned controller, A facility description circuit configured to interpret multiple past facility parameter values ​​and corresponding multiple past facility output values, A facility prediction circuit configured to operate an adaptive learning system, the adaptive learning system being configured to train a facility production predictor in response to a plurality of past facility parameter values ​​and a plurality of corresponding past facility performance values, Training the aforementioned equipment production forecaster is Using a training set that includes the aforementioned multiple past equipment parameter values ​​and the corresponding multiple past equipment performance values ​​and feedback data, This includes iteratively self-improving the customer contact indicator based on the feedback data from the training set, The aforementioned feedback data is The results of a previous customer contact indicating at least one of the following determinations: whether the customer-related output satisfies the customer's volume requirements, whether the customer-related output satisfies the customer's quality requirements, or whether the customer-related output satisfies the customer's timing requirements, The report shows at least one of the following results: equipment parameters, yield, profitability, resource optimization, business objective optimization, objective satisfaction, user satisfaction, or operator satisfaction. The equipment description circuit is further configured to interpret multiple current equipment parameter values, The trained equipment production forecaster is configured to determine the customer contact indicator in accordance with the multiple current equipment parameter values. Furthermore, the system is characterized in that the controller includes a customer notification circuit configured to provide notifications to the customer in response to the customer contact indicator.

2. The system according to claim 1, characterized in that the aforementioned customers include either current customers or future customers.

3. The system according to claim 1, wherein determining the customer contact indicator includes performing at least one operation selected from a plurality of operations: determining whether the customer-related output satisfies the volume requirements from the customer; determining whether the customer-related output satisfies the quality requirements from the customer; and determining whether the customer-related output satisfies the timing requirements from the customer.

4. The system according to claim 1, wherein the equipment description circuit is further configured to interpret historical external data from at least one external data source, and the adaptive learning system is further configured to train the equipment production forecaster in accordance with the historical external data.

5. The system according to claim 4, wherein the at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices of energy sources, and futures market prices of energy sources.

6. The system according to claim 4, wherein the equipment description circuit is further configured to interpret current external data from the at least one external data source, and the trained equipment production forecaster is further configured to determine the customer contact indicator in accordance with the current external data.

7. Interpreting multiple historical equipment parameter values ​​and corresponding multiple historical equipment performance values, To operate an adaptive learning system and thereby train an equipment production forecaster associated with the equipment in response to the plurality of past equipment parameter values ​​and the corresponding plurality of past equipment performance values, Interpreting multiple current equipment parameter values, To operate the trained equipment production forecaster to determine a customer contact indicator according to the aforementioned multiple current equipment parameter values, and This includes providing a notification to the customer in response to the customer contact indicator, Training the aforementioned equipment production forecaster is Using a training set that includes the aforementioned multiple past equipment parameter values ​​and the corresponding multiple past equipment performance values ​​and feedback data, This includes iteratively self-improving the customer contact indicator based on the feedback data from the training set, The aforementioned feedback data is The results of a previous customer contact indicating at least one of the following determinations: whether the equipment meets the customer's volume requirements, whether the equipment meets the customer's quality requirements, or whether the equipment meets the customer's timing requirements, A method characterized by showing at least one result from among equipment parameters, yield, profitability, resource optimization, business objective optimization, objective satisfaction, user satisfaction, or operator satisfaction.

8. The method according to claim 7, characterized in that the aforementioned customers include either current customers or future customers.

9. The method according to claim 7, characterized in that determining the customer contact indicator includes determining whether the customer-related output satisfies the volume request from the customer.

10. The method according to claim 7, characterized in that determining the customer contact indicator includes determining whether the customer-related output meets the customer's quality requirements.

11. The method according to claim 7, characterized in that determining the customer contact indicator includes determining whether the customer-related output satisfies the timing request from the customer.

12. The method according to claim 7, further comprising interpreting historical external data from at least one external data source, and operating the adaptive learning system to further train the equipment production forecaster in accordance with the historical external data.

13. The method according to claim 12, wherein the at least one external data source includes at least one data source selected from a plurality of data sources consisting of social media data sources, behavioral data sources, spot market prices of energy sources, and futures market prices of energy sources.

14. The method according to claim 12, further comprising interpreting current external data from at least one external data source, and operating the trained equipment production forecaster to further determine the customer contact indicator in accordance with the current external data.

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