This document describes the collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications. Specifically, the disclosed devices and systems may be employed to produce one or more marketing and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating user engagement, thereby accurately determining the cost benefits of the campaign. The analytic results provided may then be used to guide the generation of original web content, such as for the purposes of enhancing customer or follower experience, driving business, and for driving advertising campaigns. Alternatively, web content that is in the public domain, and determined to perform well, can be reproduced, referenced, or otherwise referred to, in the context of promoting or presenting the user's web content.

An example transaction-enabled system may include a smart contract wrapper to access a distributed ledger comprising intellectual property (IP) licensing terms corresponding to IP assets, wherein the IP licensing terms include an apportionment of royalties among owning entities in the distributed ledger. The smart contract wrapper may interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, to add the apportionment of royalties corresponding to the IP description value. At least one of the plurality of IP assets comprises an instruction set and an operation on the distributed ledger provides provable access to the instruction set. A royalty apportionment wrapper apportions royalties from at least one royalty generating element to owning entities in response to the IP licensing terms.

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.

In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

A system for identifying life events for individuals based on relationships found in data. The system includes a database containing data records and fields and identifying individuals involved in each record. A supplemental source of data including location and social media data is also included. The database and the supplemental source of data are provided to a computer which executes a machine learning algorithm configured to identify life events for the individuals based on clusters in the data, where the machine learning algorithm provides output data identifying the life events, and rating values for each of the life events for each of the individuals. A communication system algorithm sends actionable communications to particular ones of the individuals based on the output data. Unsupervised learning may be used for initial system training, and supervised learning for ongoing training.

A method for determining a value indicator in a broker-mediated geospatial information service (GIS) environment includes receiving, from a second system via a communication network at a first system of the broker-mediated GIS environment, from a plurality of respondents, feedback data relating to: a broker-mediated GIS based upon geospatial data relating to at least one of natural features, constructed features and boundaries on the Earth, the geospatial data including location and characteristic data, and a geospatial information process for geospatially processing a dataset provided to each respondent of the plurality of respondents. Each respondent of the plurality of respondents is a prior requestor of a process-dataset solution for performing the broker-mediated GIS. Based on the feedback data, a value indicator is determined relating to a process-dataset solution based upon the geospatial data and the geospatial information process. The value indicator is transmitted, via the communication network from the broker-mediated GIS, in association with the process-dataset solution transmitting. The process-dataset solution relating to selection of a restaurant.

Methods and apparatus to generate consumer data are disclosed. An example method of selecting a sample of transaction data corresponding to a membership program includes defining a first type of member of the membership program; defining a second type of member of the membership program; calculating, via a processor, a target for the sample; selecting, via the processor, a first portion of the transaction data for the first type of member in accordance with the target; generating, via the processor, an updated target by recalculating the target with the first portion of the transaction data removed from consideration; and selecting, via the processor, a second portion of the transaction data for the second type of member in accordance with the updated target.

A system can arrange a plurality of transport services in a geographic region by matching received transport requests from requesters with standard vehicles to facilitate transport of the requesters to destinations indicated in the transport requests. The transport requests can comprise both requests for standard vehicles and high-capacity vehicle (HCV) requests from HCV requesters. Based on a set of transport services for HCV requesters, from the plurality of transport services, the system can determine over a specified duration of time, a set of demand data for HCV transport services in the geographic region. The system can then execute a route design model using the set of demand data to generate an HCV route network for the geographic region.

A device receives vehicle data associated with vehicles located at a vehicle dealership lot, and receives, from a user device, profile data identifying a user of the user device and data identifying a particular vehicle of the vehicles. The device compares the data identifying the particular vehicle and the vehicle data to determine particular vehicle data associated with the particular vehicle, and processes the particular vehicle data and the profile data of the user, with a first model, to determine purchase options for the particular vehicle and the user. The device provides, to the user device, the particular vehicle data and the purchase options for the particular vehicle to cause the user device to display the particular vehicle data and the purchase options for the particular vehicle.

The present disclosure describes transaction-enabling systems and methods for enabling machine resource transactions. A system can include a machine having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement; and a controller. The controller can include a resource requirement circuit to determine an amount of a resource for the machine to service task requirement, a resource market circuit to access a resource market, and a resource distribution circuit to execute a transaction of the resource on the resource market in response to the determined amount of the resource.