Systems and methods for fleet forward energy and energy credits purchase are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task; a forward resource market circuit to access a forward market for energy; and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

Systems and methods for machine forward energy and energy storage transactions are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy storage capacity requirement, a forward resource market circuit to access a forward market for energy, and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

Systems and methods for machine forward energy transactions optimization are disclosed. A transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, a forward resource market circuit to access a forward market for energy, and a controller. The controller may include an artificial intelligence (AI) circuit to configure a transaction on the forward market for energy in response to the aggregated resource requirement and a machine resource acquisition circuit to automatically solicit the configured transaction on the forward market for energy. The AI circuit may also iteratively improve the configured transaction to improve a task outcome of the fleet of machines.

Systems and methods for machine forward energy and energy storage transactions are disclosed. An example transaction-enabling system may include a resource requirement circuit to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy storage capacity requirement, a forward resource market circuit to access a forward market for energy, and a machine resource acquisition circuit to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

Systems and methods for forward market price prediction and sale of energy storage capacity with arbitrage are disclosed. An example transaction-enabling system may include a fleet of machines having an aggregate energy storage capacity, and a controller. The controller may include an external data circuit to monitor and collect data from an external data source; an expert system circuit to predict a forward market price for energy storage capacity; a market testing circuit to execute a first transaction of the aggregate energy storage capacity in a forward market for energy storage capacity in response to the predicted forward market price; and an arbitrage execution circuit to execute a second transaction of the aggregate energy storage capacity in the forward market in response to at least one of an arbitrage parameter or the predicted forward price.

Transaction-enabled systems and methods with licensing smart wrappers and IP aggregation are disclosed. An example transaction-enabling system may include a smart wrapper structured to aggregate intellectual property (IP) assets into an aggregate stack of IP; and a smart contract wrapper configured to: access a distributed ledger comprising a plurality of intellectual property (IP) licensing terms corresponding to the aggregated 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 an IP asset to the aggregate stack of IP and commit a party to at least one of the plurality of IP licensing terms.

Systems and methods for aggregating transactions and optimization data related to energy and energy credits include a transaction-enabling system including a resource requirement circuit structured to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy credit requirement; a forward resource market circuit structured to access a forward market for energy; and a machine resource acquisition circuit structured to execute a transaction on the forward market for energy in response to the aggregated resource requirement.

In an embodiment, the techniques herein include receiving a request for growing predictions for multiple regions within a growing operation. From there, user-specific tolerance rates are received for each region and scientifically-generated recommended prescription rates are determined for each region of the multiple regions. Weather risk estimates are determined for each region for each rate, and those weather risk estimates are returned in response to the request for growing predictions.

Systems and methods for forward market price prediction and sale of energy storage capacity are disclosed. An example transaction-enabling system may include a fleet of machines having an aggregate energy storage capacity; and a controller, comprising: an external data circuit structured to monitor an external data source and collect data from the external data source; an expert system circuit structured to predict a forward market price for energy storage capacity based on the collected data and the aggregate energy storage capacity; and a smart contract circuit structured to automatically sell at least a subset of the aggregate energy storage capacity on a forward market for energy storage capacity in response to the predicted forward market price.

A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.