Systems and methods for forward market purchase of machine resources using artificial intelligence are disclosed. An example transaction-enabling system may include a fleet of machines, each one of the fleet of machines having a resource requirement comprising at least one of a plurality of machine-related resources. The system may further include a controller including an artificial intelligence (AI) circuit to aggregate data for the plurality of machine-related resources from at least one data source comprising an external data source or an internal data source; an expert system circuit to configure a purchase of at least one of the plurality of machine-related resources; and a machine resource acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one resource of the plurality of machine-related resources.

Transaction-enabled systems and methods for IP aggregation and transaction execution are disclosed. An example system may include a smart wrapper structured to aggregate intellectual property (IP) and a smart contract wrapper, wherein the smart contract wrapper is configured to access a distributed ledger comprising a plurality of IP references corresponding to IP assets of the aggregate IP, interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, add the IP corresponding to the IP description value to the aggregate IP. The system may further include a compute resource structured to perform computing tasks for at least one operation corresponding to the added IP, wherein the at least one operation corresponds to transaction execution tasks associated with the added IP.

Transaction-enabled systems and methods for smart contracts are disclosed. An example system may include a smart contract wrapper structured to aggregate intellectual property (IP) licensing terms corresponding to a plurality of IP assets, access a distributed ledger comprising the aggregated IP licensing terms and a plurality of IP references corresponding to the plurality of IP assets, interpret an IP description value and an IP addition request, and add an IP asset to the plurality of IP assets in response to the IP description value, and add IP licensing terms corresponding to the added IP asset to the aggregated IP licensing terms in response to the IP description value and the IP addition request.

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

A method, apparatus, system, and computer program product provide the ability to bid for an advertising impression. Via input from an advertising purchaser, a publisher specification is defined. The publisher specification provides impression information regarding impressions desired by the advertising purchaser. An auction is conducted by receiving one or more bids (that comply with the specification) from one or more publishers. A determination is made regarding which of the one or more publishers has provided a successful bid. The advertising purchaser is enabled to provide one or more advertisements to the one or more successful bidding publishers.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Systems and methods related to resource acquisition on a resource market are disclosed. A system may include a machine having a resource requirement for a task. A system controller may include a resource requirement circuit to determine an amount of a resource for the machine to service the task requirement, a resource market circuit to access a resource market, and a market testing circuit to execute a first transaction of the resource on the resource market. The controller may further include an arbitrage execution circuit to execute a second transaction of the resource on the resource market in response to an outcome of the first transaction, wherein the second transaction comprises a larger transaction than the first transaction.

Systems and methods for allocation of renewable energy capacity for a fleet of machines are disclosed. An example transaction-enabling system may include a fleet of machines each having an energy consumption task requirement, wherein at least a subset of the fleet of machines each comprises a renewable energy capacity. The system may further include a controller including a resource requirement circuit to determine an amount of energy for each of the machines to service the energy consumption task requirement for each corresponding machine, and a resource distribution circuit to allocate a delivery of energy from an aggregated renewable energy capacity of the subset of the fleet of machines in response to the determined amount of energy for each of the machines to service the energy consumption task requirement.

Systems and methods for the acquisition of attention-related resources are disclosed. In embodiments, an example transaction-enabled platform may include an attention market access circuit to interpret a plurality of available attention-related resources on an attention market and a data aggregation circuit to aggregate data from a data source, wherein the data source comprises data related to at least one of the plurality of available attention-related resources. The platform may further include an expert system circuit to configure a purchase of the one of the available attention-related resources, and to identify a timing of the configured purchase, at least partially based on the aggregated data; and an attention acquisition circuit to automatically solicit, in response to the identified timing, the configured purchase of the available attention-related resource in a forward market.