Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Example aspects include techniques for accurate and expeditious cloud service health communication to customers. These techniques may include determining that a service health incident has customer impact, the service health incident corresponding to an outage of one or more services of a cloud computing platform, identifying a plurality of customers impacted by the service health incident, and predicting, based on the service health incident and one or more other service health incidents, aggregated incident information identifying a plurality of service health incidents associated with the outage of the one or more services. In addition, the techniques may include identifying the one or more services associated with the service health incident, and transmitting, based at least in part on the aggregated incident information and the one or more services, a health notification to the plurality of customers.

An automated advertising scheduling and distribution process reacts to the effectiveness of sales data. A hosted platform creates location-specific playlists based on key consumer variables that impact buying behavior, and dynamically performs data analytics. Utilizing a programmatic system and machine learning algorithmic methodology, the platform gathers data from the retailer's data warehouse and automatically pulls location-by-location sales data while simultaneously collecting playback data. If sales are not being affected on the particular item that is being promoted, then the platform may be configured to replace that message with a promotional message for another product with a higher likelihood of engagement and conversion. This virtual feedback loop ensures that the platform is optimizing the most effective series of promotional messages for any given location. The content management administrator accordingly delivers relevant advertising/messages to various display screens integrated into fuel pumps, through the store, and to retailer loyalty program applications.

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

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.

Example methods and apparatus for associating media devices with a demographic composition of a geographic area are disclosed. Disclosed example apparatus are to obtain a plurality of Internet Protocol addresses assigned to a media device associated with a panel member. Disclosed example apparatus are also to determine a most used Internet Protocol address from the plurality of Internet Protocol addresses, determine a geographic location corresponding to the most used Internet Protocol address, associate a geographic area with the media device in response to a determination that the geographic location corresponds to a location of an internet service provider, determine a demographic profile associated with the geographic area, and associate the demographic profile with the media device.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

A method, apparatus, and computer program, each configured for determining a travel propensity, the travel propensity being a distance a customer is willing to travel to redeem a renderable data object, are provided An exemplary method comprises classifying each of a plurality of geographic regions as one geographic region type of a plurality of geographic region types, mapping each of a plurality of redemption locations to a geographic region type, calculating, for a first geographic region type, a first distance that accounts for a predefined number of redemptions, determining a number of merchants having redemption locations within the first distance, calculating a second distance, from a center of a geographic region of the second geographic region type, necessary to include the number of merchants having redemption locations within the first distance, and assigning the second distance as the second geographic region type travel propensity.

The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share; generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.