Facility level transaction-enabling systems and methods for provisioning and resource allocation

BACKGROUND

Machines and automated agents are increasingly involved in market activities, including for data collection, forecasting, planning, transaction execution, and other activities. This includes increasingly high-performance systems, such as used in high-speed trading. A need exists for methods and systems that improve the machines that enable markets, including for increased efficiency, speed, reliability, and the like for participants in such markets.

Many markets are increasingly distributed, rather than centralized, with distributed ledgers like Blockchain, peer-to-peer interaction models, and micro-transactions replacing or complementing traditional models that involve centralized authorities or intermediaries. A need exists for improved machines that enable distributed transactions to occur at scale among large numbers of participants, including human participants and automated agents.

Operations on blockchains, such as ones using cryptocurrency, increasingly require energy-intensive computing operations, such as calculating very large hash functions on growing chains of blocks. Systems using proof-of-work, proof-of-stake, and the like have led to “mining” operations by which computer processing power is applied at a large scale in order to perform calculations that support collective trust in transactions that are recorded in blockchains.

Many applications of artificial intelligence also require energy-intensive computing operations, such as where very large neural networks, with very large numbers of interconnections, perform operations on large numbers of inputs to produce one or more outputs, such as a prediction, classification, optimization, control output, or the like.

The growth of the Internet of Things and cloud computing platforms have also led to the proliferation of devices, applications, and connections among them, such that data centers, housing servers and other IT components, consume a significant fraction of the energy consumption of the United States and other developed countries.

As a result of these and other trends, energy consumption has become a major factor in utilization of computing resources, such that energy resources and computing resources (or simply “energy and compute”) have begun to converge from various standpoints, such as requisitioning, purchasing, provisioning, configuration, and management of inputs, activities, outputs and the like. Projects have been undertaken, for example, to place large scale computing resource facilities, such as Bitcoin™ or other cryptocurrency mining operations, in close proximity to large-scale hydropower sources, such as Niagara Falls.

A major challenge for facility owners and operators is the uncertainty involved in optimizing a facility, such as resulting from volatility in the cost and availability of inputs (in particular where less stable renewable resources are involved), variability in the cost and availability of computing and networking resources (such as where network performance varies), and volatility and uncertainty in various end markets to which energy and compute resources can be applied (such as volatility in cryptocurrencies, volatility in energy markets, volatility in pricing in various other markets, and uncertainty in the utility of artificial intelligence in a wide range of applications), among other factors.

A need exists for a flexible, intelligent energy and compute facility that adjust in response to uncertainty and volatility, as well as for an intelligent energy and compute resource management system, such as one that includes capabilities for data collection, storage and processing, automated configuration of inputs, resources and outputs, and learning on a training set of facility outcomes, facility parameters, and data collected from data sources to train an artificial intelligence/machine learning system to optimize various relevant parameters for such a facility.

SUMMARY

Machine learning potentially enables machines that enable or interact with automated markets to develop understanding, such as based on IoT data, social network data, and other non-traditional data sources, and execute transactions based on predictions, such as by participating in forward markets for energy, compute, advertising and the like. Blockchain and cryptocurrencies may support a variety of automated transactions, and the intersection of blockchain and AI potentially enables radically different transaction infrastructure. As energy is increasingly used for computation, machines that efficiently allocate available energy sources among storage, compute, and base tasks become possible. These and other concepts are addressed by the methods and systems disclosed herein.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a facility including a core task; a controller, including a facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values, a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility resource allocation circuit in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values, wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values, and wherein the trained facility resource allocation circuit is further structured to adjust, in response to the plurality of present state facility parameter values, a plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of facility resource values include a provisioning and an allocation of facility energy resources, and a provisioning and an allocation of facility compute resources.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource utilization profile from among a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource output selection from among a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource input profile from among a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values by one of producing or selecting a favorable facility resource configuration profile from among a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret historical external data from at least one external data source, and wherein the adaptive learning system is further configured to train the facility resource allocation circuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the at least one external data source includes at least one data source selected from a list of data sources consisting of a social media data source, a behavioral data source, a spot market price for an energy source, and a forward market price for an energy source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the facility description circuit is further structured to interpret present external data from the at least one external data source, and wherein the trained facility resource allocation circuit is further structured to adjust the plurality of facility resource values in response to the present external data.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values; operating an adaptive learning system, thereby training a facility resource allocation circuit in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values; interpreting a plurality of present state facility parameter values; and adjusting, in response to the plurality of present state facility parameter values, a plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource utilization profile from among a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a favorable facility resource utilization profile relative to a set of available facility resource utilization profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource utilization profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource output selection from among a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource output selection relative to a set of available facility resource output values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource output values in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource input profile from among a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource input profile relative to a set of available facility resource input profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource input profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by selecting a favorable facility resource configuration profile from among a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include adjusting the plurality of facility resource values by producing a facility resource configuration profile relative to a set of available facility resource configuration profiles.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating the set of available facility resource configuration profiles in response to the plurality of facility resource values.

A further embodiment of any of the foregoing embodiments of the present 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 facility resource allocation circuit in response to the historical external data.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting present external data from the at least one external data source, and further adjusting the plurality of facility resource values in response to the present external data.

The present disclosure describes a transaction-enabling system, the system according to one disclosed non-limiting embodiment of the present disclosure can include a facility including a core task; a controller, including facility description circuit structured to interpret a plurality of historical facility parameter values and a corresponding plurality of historical facility outcome values, a facility prediction circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility resource allocation circuit in response to the plurality of historical facility parameter values and the corresponding plurality of historical facility outcome values, wherein the facility description circuit is further structured to interpret a plurality of present state facility parameter values. The trained facility resource allocation circuit is further structured to adjust, in response to the plurality of present state facility parameter values, a plurality of facility resource values and the adaptive learning system is further configured to determine a relationship between input values and outcomes and add or remove input values to improve prediction of facility outcomes.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system includes a machine learning facility, an expert system or an artificial intelligence.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the adaptive learning system is further structured to add relationships between the input values and the outcomes.

The present disclosure describes a transaction-enabling system including a smart contract wrapper, the contract wrapper according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a plurality of embedded contract terms and a plurality of data values, interpret an access request value for the plurality of data values, and, in response to the access request value, provide access to at least a portion of the plurality of data values, and commit an entity providing the access request value to at least one of the plurality of embedded contract terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values includes intellectual property (IP) data corresponding to a plurality of IP assets, and wherein the plurality of embedded contract terms includes a plurality of intellectual property (IP) licensing terms for the corresponding plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to commit the entity providing the access request value to corresponding IP licensing terms for accessed ones of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to interpret an IP description value and an IP addition request, and to add additional IP data to the plurality of data values in response to the IP description value and the IP addition request, wherein the additional IP data includes IP data corresponding to an additional IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values further includes a plurality of owning entities corresponding to the plurality of IP assets, and wherein the smart contract wrapper is further configured to apportion royalties from the plurality of IP assets to the plurality of owning entities in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to: interpret an IP description value, an IP addition request, and an IP addition entity, add additional IP data to the plurality of data values in response to the IP description value and the IP addition request, commit the IP addition entity to the IP licensing terms, and further apportion royalties from the plurality of IP assets to the plurality of owning entities in response to the additional IP data and the IP addition entity.

A method for executing a smart contract wrapper for a distributed ledger may include accessing a distributed ledger including a plurality of embedded contract terms and a plurality of data values, interpreting an access request value for the plurality of data values, and, in response to the access request value, providing access to at least a portion of the plurality of data values, and committing an entity providing the access request value to at least one of the plurality of embedded contract terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including providing the entity providing the access request value with a user interface including a contract acceptance input, and wherein the providing access and committing the entity is in response to a user input on the user interface.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include including providing an access option to the user interface, and adjusting at least one of the providing access and the committed contract terms in response to a user input on the user interface responsive to the access option.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include committing the entity providing the access request value to corresponding IP licensing terms for accessed ones of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP description value and an IP addition request, and adding additional IP data to the plurality of data values in response to the IP description value and the IP addition request, wherein the additional IP data includes IP data corresponding to an additional IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of data values further includes a plurality of owning entities corresponding to the plurality of IP assets, the method further including apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP description value, an IP addition request, and an IP addition entity, adding additional IP data to the plurality of data values in response to the IP description value and the IP addition request, committing the IP addition entity to the IP licensing terms, and further apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the additional IP data and the IP addition entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include updating a valuation for at least one of the plurality of IP assets, and updating the apportioning royalties from the plurality of IP assets in response to the updated valuation for the at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that at least one of the plurality of IP assets has expired, and updating the apportioning royalties from the plurality of IP assets in response to the determining that the at least one of the plurality of IP assets has expired.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining that an owning entity corresponding to at least one of the plurality of IP assets has changed, and updating the apportioning royalties from the plurality of IP assets in response to the change of the owning entity.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a user interface to a new owning entity of the at least one of the plurality of IP assets where an ownership has changed, and committing the new owning entity to the IP licensing terms in response to a user input on the user interface.

The present disclosure describes a transaction-enabling system including a smart contract wrapper, the smart contract wrapper according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a plurality of intellectual property (IP) licensing terms corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, to add an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to interpret an IP licensing value corresponding to the IP description value, and to add the IP licensing value to the plurality of IP licensing terms in response to the IP description value and the IP addition request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the smart contract wrapper is further configured to associate at least one of the plurality of IP licensing terms to an added IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a data store having a copy of at least one of the IP assets stored thereon, and wherein the aggregate stack of IP further includes a reference to the data store for the at least one of the IP assets.

A method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a plurality of intellectual property (IP) licensing terms corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, interpreting an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, adding an IP asset to the aggregate stack of IP.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP licensing value corresponding to the IP description value, and adding the IP licensing value to the plurality of IP licensing terms in response to the IP description value and the IP addition request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include associating at least one of the plurality of IP licensing terms to the added IP asset.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include storing at least one of the IP assets on a data store, and wherein the aggregate stack of IP includes a reference to the stored at least one of the IP assets on the data store.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include apportioning royalties from the plurality of IP assets to a plurality of owning entities corresponding to the aggregate stack of IP in response to the corresponding IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an IP addition entity corresponding to the IP addition request and the IP description value, and committing the IP addition entity to the IP licensing terms.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include apportioning royalties from the plurality of IP assets to the plurality of owning entities in response to the IP addition entity.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to: access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and, in response to the instruction set access request, provide a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a 3D printer operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes and instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a field programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a food preparation instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a polymer production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a chemical synthesis instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes a biological production instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a crystal fabrication system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the instruction set, and to record a transaction on the distributed ledger in response to the execution operation.

A method may include accessing a distributed ledger including an instruction set, tokenizing the instruction set, interpreting an instruction set access request, and, in response to the instruction set access request, providing a provable access to the instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a coating process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the coating process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include recording a transaction on the distributed ledger in response to the providing commands to the production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a 3D printing process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the 3D printing process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a semiconductor fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the semiconductor fabrication process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes field programmable gate array (FPGA) instruction set.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the FPGA instruction set, and recording a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a food preparation process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the food preparation process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a polymer production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the polymer production process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a chemical synthesis process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the chemical synthesis process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a biological production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the biological production process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set includes an instruction set for a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing commands to a production tool of the crystal fabrication process in response to the instruction set access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the instruction set, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including executable algorithmic logic, tokenize the executable algorithmic logic, interpret an access request for the executable algorithmic logic, and, in response to the access request, provide a provable access to the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide the executable algorithmic logic as a black box, and wherein the instruction set further includes an interface description for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the executable algorithmic logic, and to record a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the executable algorithmic logic further includes an application programming interface (API) for the executable algorithmic logic.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including executable algorithmic logic, tokenizing the executable algorithmic logic, interpreting an access request for the executable algorithmic logic, and, in response to the access request, providing a provable access to the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an interface description for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an application programming interface (API) for the executable algorithmic logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the executable algorithmic logic, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including a firmware data value, tokenize the firmware data value, interpret an access request for the firmware data value, and, in response to the access request, provide a provable access to a firmware corresponding to the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide a notification to an accessor of the firmware data value in response to an update of the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret one of a download operation or an install operation of a firmware asset corresponding to the firmware data value, and to record a transaction on the distributed ledger in response to the one of the download operation or the install operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the firmware data value includes firmware for a component of a production process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the component of the production process includes a production tool.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the production tool includes a production tool for a process selected from the processes consisting of: a coating process, a 3D printing process, a semiconductor fabrication process, a food preparation process, a polymer production process, a chemical synthesis process, a biological production process, and a crystal fabrication process.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the firmware data value includes firmware for one of a compute resource and a networking resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a firmware data value, tokenizing the firmware data value, interpreting an access request for the firmware data value, and, in response to the access request, providing a provable access to the firmware corresponding to the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing a notification to an accessor of the firmware data value in response to an update of the firmware data value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting a download operation of a firmware asset corresponding to the firmware data value, and recording a transaction on the distributed ledger in response to the download operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an install operation of a firmware asset corresponding to the firmware data value, and recording a transaction on the distributed ledger in response to the install operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including serverless code logic, tokenize the serverless code logic, interpret an access request for the serverless code logic, and, in response to the access request, provide a provable access to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to provide the serverless code logic as a black box, and wherein the serverless code logic further includes an interface description for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of the serverless code logic, and to record a transaction on the distributed ledger in response to the execution operation.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the instruction set further includes an application programming interface (API) for the serverless code logic.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including serverless code logic, tokenizing the serverless code logic, interpreting an access request for the serverless code logic, and in response to the access request, providing a provable access to the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an interface description for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include providing an application programming interface (API) for the serverless code logic.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of the serverless code logic, and recording a transaction on the distributed ledger in response to the execution operation.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may be configured to access a distributed ledger including an aggregated data set, interpret an access request for the aggregated data set, and, in response to the access request, provide a provable access to the aggregated data set, wherein the provable access includes at least one of which parties have accessed the aggregated data set and how many parties have accessed the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger includes a block chain, and wherein the aggregated data set includes one of a trade secret and proprietary information.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include an expert wrapper for the distributed ledger, wherein the expert wrapper is configured to tokenize the aggregated data set and to validate the one of the trade secret and the proprietary information.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger includes a set of instructions, and wherein the controller is further configured to interpret an instruction update value, and to update the set of instructions in response to the access request and the instruction update value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include a smart wrapper for the distributed ledger, wherein the smart wrapper is configured to allocate a plurality of sub-sets of instructions to the distributed ledger as the aggregated data set, and manage access to the plurality of sub-sets of instructions in response to the access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an access of one of the plurality of sub-sets of instructions, and to record a transaction on the distributed ledger in response to the access.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to interpret an execution operation of one of the plurality of sub-sets of instructions, and to record a transaction on the distributed ledger in response to the access.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including an aggregated data set, interpreting an access request for the aggregated data set, and, in response to the access request, providing a provable access to the aggregated data set, wherein the provable access includes at least one of which parties have accessed the aggregated data set and how many parties have accessed the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating an expert wrapper for the distributed ledger, wherein the expert wrapper is configured to tokenize the aggregated data set and to validate at least one of trade secret or proprietary information of the aggregated data set.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger further includes a set of instructions, the method further including interpreting an instruction update value, and updating the set of instructions in response to the access request and the instruction update value.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include allocating a plurality of sub-sets of instructions to the distributed ledger as the aggregated data set, and managing access to the plurality of sub-sets of instructions in response to the access request.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an access of one of the plurality of sub-sets of instructions, and recording a transaction on the distributed ledger in response to the access.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include interpreting an execution operation of one of the plurality of sub-sets of instructions, and recording a transaction on the distributed ledger in response to the access.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure may access a distributed ledger including a plurality of intellectual property (IP) data corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, tokenize the IP data, interpret a distributed ledger operation corresponding to at least one of the plurality of IP assets, determine an analytic result value in response to the distributed ledger operation and the tokenized IP data, and provide a report of the analytic result value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the distributed ledger operation includes at least one operation selected from the operations consisting of accessing IP data corresponding to one of the plurality of IP assets, executing a process utilizing IP data corresponding to one of the plurality of IP assets, adding IP data corresponding to an additional IP asset to the aggregate stack of IP, and removing IP data corresponding to one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the analytic result value includes at least one result value selected from the result values consisting of a number of access events corresponding to at least one of the plurality of IP assets, statistical information corresponding to access events for a plurality of IP assets, a distribution of the plurality of IP assets according to access event rates, one of access times or processing times corresponding to at least one of the plurality of IP assets, and unique entity access events corresponding to at least one of the plurality of IP assets.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure may include accessing a distributed ledger including a plurality of IP data corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, tokenizing the IP data, interpreting a distributed ledger operation corresponding to at least one of the plurality of IP assets, determining an analytic result value in response to the distributed ledger operation and the tokenized IP data, and providing a report of the analytic result value.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining a number of access events corresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining one of an access time or a processing time corresponding to at least one of the plurality of IP assets.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein determining the analytic result value includes determining a number of unique entity access events corresponding to at least one of the plurality of IP assets.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can be configured to interpret a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpret a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operate an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and execute a transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes an internet-of-things (IoT) data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a network bandwidth resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the controller is further configured to operate the expert system to determine a substitution cost of the second resource, and to execute the transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the expert system is further configured to determine at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes at least one resource selected from the resources consisting of: a compute resource, a network bandwidth resource, a spectrum resource, a data storage resource, an energy resource, and an energy credit resource.

The present disclosure describes a method, the method according to one disclosed non-limiting embodiment of the present disclosure can include interpreting a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpreting a plurality of external data sources, wherein the plurality of external data sources includes at least one data source outside of the task system; operating an expert system to predict a forward market price for a resource in response to the resource utilization requirement and the plurality of external data sources; and executing a transaction on a resource market in response to the predicted forward market price.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the plurality of external data sources includes a social media data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market price includes a forward market price for a spectrum resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the resource utilization requirement includes a first resource, and wherein the resource of the forward market price includes at least one of: the first resource, and a second resource that can be substituted for the first resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include operating the expert system to determine a substitution cost of the second resource, and executing the transaction on the resource market further in response to the substitution cost of the second resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may further include determining at least a portion of the substitution cost of the second resource as an operational change cost for the task system.

The present disclosure describes a transaction-enabling system including a controller, the controller according to one disclosed non-limiting embodiment of the present disclosure can be configured to interpret a resource utilization requirement for a task system having at least one of a compute task, a network task, or a core task; interpret a behavioral data source; operate a machine to forecast a forward market value for a resource in response to the resource utilization requirement and the behavioral data source; and perform one of adjusting an operation of the task system or executing a transaction in response to the forecast of the forward market value for the resource.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the forward market value for the resource includes a forward market for energy prices.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes an automated agent behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a human behavioral data source.

A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the behavioral data source includes a business entity behavioral data source.