Systems, methods, and computer-readable media for predicting a repair project are disclosed. The repair project may be a roof repair. Various data inputs may be received and analyzed to determine the likelihood of obtaining a repair job. The repair job may be associated with a lead score. The lead score may indicate a likelihood of obtaining the repair job. Repair jobs with lead scores over a threshold may be pursued to obtain the repair job. Materials may be automatically ordered to preempt obtaining the repair job. Workers for carrying out the repair job may be automatically determined and scheduled.

Implementations of a computer implemented method and system to transform geolocation exchange unit specifications and transportation and freight and parking and tolling and curb management and transportation management association unit securities or derivative unit securities or unitization structure capacity units with two waypoints or a destination waypoint or a series sequence of waypoints into multi-modal objects which are tradable as commodities such as wheat, oil, corn, stocks, foreign exchange, fixed income or other forward or securitized markets and broadcast those unitized prices, indices and associated news over a plurality of electronic devices. The present disclosed invention relates to combining the concepts of objected oriented programming and navigation systems and social networking, price-time priorities queues, replacement costs, termination valuations, financial markets, commodity structuring transformation or unitization structures as a fungible asset classes or tradable markets.

A method may include generating a feature table, hierarchical segments, and a graph network based on raw interaction data of a set of users. The method may further include generating a set of rankings for features in the feature table. The method may further include targeting hierarchical segments of the set of users through marketing campaigns and calculate a set of elasticity scores for the set of users in response to the marketing campaigns in the hierarchical segments. The method may further include generating item recommendations for the set of users based on the graph network. The method may further include executing a machine learning model to generate an uplift score for each user from the set of users based on at least one of the raw interaction data, the set of rankings, hierarchical segments, the set of elasticity scores, or the item recommendations.

A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Methods and systems are disclosed for determining resource suitability based at least in part on physical geographic mapping data. An artificial intelligence/learning engine may be trained to determine such resource suitability using training data. The trained artificial intelligence/learning engine may then me used to generate suitability indicators. The suitability indicators may be rendered in association with a map comprising the resource. The resource may be configurable to be shared amongst a plurality of physical resource users in a time displaced manner.

A system and method are disclosed herein. The system includes one or more processors and a memory having programming instructions stored thereon, which, when executed by the one or more processors, performs operations. The operations include retrieving historical account activity. The operations further include constructing a training data set that includes the historical inflow data, the historical outflow data, and known forecast information from the historical account activity. The operations further include generating a combined prediction model configured to forecast future inflow activity and future outflow activity. The operations further include receiving current inflow activity, current outflow activity, and current balance information for a user. The operations further include generating a predicted account balance by forecasting, by the prediction model, a future inflow and a future outflow and constructing the predicted account balance based on the future inflow, the future outflow, and the current balance information.

Systems, methods, and apparatus are described herein for determining a location from anonymous data. For example, a computing device may receive anonymous data associated with a browser session initialized by a user via a browser on a user computing device. The computing device may determine that the user has not been assigned a unique identifier. The computing device may determine whether the user opted-in to location tracking. If the user opted-out of location tracking, the computing device may determine a latitude coordinate and a longitude coordinate of the user computing device during the browser session. The computing device may identify a physical address for the user based on the latitude coordinate and the longitude coordinate, for example, using a map application programming interface (API). The computing device may assign the unique identifier to the user. The computing device may associate the unique identifier to the physical address.

Analyzing energy savings for a building includes receiving historical energy usage and weather data for a building, a set of operations parameters describing building operations and a set of building system parameters describing building systems. A baseline configuration is submitted to a first energy consumption simulation to determine a baseline energy usage profile A calibrated configuration is determined from the baseline configuration and the historical energy usage. An energy usage aberration is identified in the current year. The calibrated energy usage profile is adjusted to account for it. The difference between actual energy usage for the current year and the adjusted calibrated energy usage profile is an accurate measure of savings.

Transaction systems and methods are disclosed. A system may include a controller having a transaction detection circuit to interpret a transaction request value, wherein the transaction request value includes a transaction description for one of a proposed or an imminent transaction, and a cryptocurrency type value and a transaction amount value. A transaction locator circuit then determines a transaction location parameter in response to the transaction request value, wherein the transaction location is a geographic value or a jurisdiction value. A transaction execution circuit then provides a transaction implementation command in response to the transaction location parameter.

Systems and methods for forward market renewable energy credit prediction from business entity behavior data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market. The example system may include wherein the forecast is based at least in part on a business entity behavior collected from at least one business entity behavioral data source, and wherein the energy credit comprises a renewable energy credit associated a renewable energy system. The example system may further include a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price.