A computer-implemented method, computer program product and computing system for receiving a complex task; processing the complex task to define a plurality of discrete tasks each having a discrete goal; executing the plurality of discrete tasks on a plurality of machine-accessible public computing platforms; determining if any of the plurality of discrete tasks failed to achieve its discrete goal; and if a specific discrete task failed to achieve its discrete goal, defining a substitute discrete task having a substitute discrete goal.

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.

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.

The present disclosure provides a method for generating an index score. The method may comprise (a) obtaining data associated with a plurality of interactions between (i) a plurality of sales representatives and (ii) a customer; (b) for each sales representative: (i) processing the data to determine a plurality of components of the index score; (ii) applying a plurality of weights and standardizations to the plurality of components to generate a plurality of metrics; (iii) aggregating the plurality of metrics to generate the index score, wherein the index score is indicative of an effectiveness of the sales representative in interacting with the customer; (c) selecting, based on the index scores of the plurality of sales representatives, a sales representative of the plurality of sales representatives; and (d) displaying, on a graphical user interface of an electronic device of the sales representative, a recommended interaction between the sales representative and the customer.

Disclosed is a method and system for providing a behavior data sales service. A behavior data sales service providing method, implemented by a computer system, may include collecting behavior data of each of activity entities; associating the behavior data with unique information of each of the activity entities; generating sales data of each of the activity entities by assigning a valid period for the unique information of each of the activity entities to the unique information of each of the activity entities; monitoring the valid period for the unique information of each of the activity entities in response to selling sales data of each of the activity entities to each of purchase entities; and repeatedly updating the unique information of each of the activity entities according to the valid period for the unique information of each of the activity entities.

According to the present disclosure, there is disclosed a method of providing search information using an electronic apparatus, which includes: checking information on a product group corresponding to a keyword acquired from a user; checking color information on one or more products included in the product group; and providing a search result page corresponding to the keyword, the search result page including at least some of one or more color chips corresponding to the information on the product group and the color information on the one or more products.

Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and generate, based on the reconciled EV charging information and the EV telematics data, models for at least one of continuous EV load prediction, electrical vehicle supply equipment (EVSE detection), and/or optimization for at least one of aggregated load, load per feeder, or maximum revenue for time-of-use tiers.

Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

Aspects of the disclosure relate to value exchange and recommendation. A computing platform may send a selection request to a computing device for selecting one or more options for a payment transaction. The one or more options may include a value contribution in exchange for credit. The computing platform may receive, by a user of the computing device, a selection of the value contribution option. The computing platform may initiate performance of the value contribution. The computing platform may receive a notification indicating completion of the value contribution. The computing platform may identify a monetary value of the value contribution. The computing platform may cause a monetary payment to be issued to an account associated with the user of the computing device based on the identified monetary value. The computing platform may generate and send one or more recommendations associated with the payment transaction.