Systems and transaction-enabled methods for providing provable access to a distributed ledger with serverless code logic are disclosed. A transaction-enabling system may include a controller configured to access a distributed ledger comprising serverless code logic, tokenize the serverless code logic, interpret an access request for the serverless code logic and, in response to the access request, provide a provable access to the serverless code logic.

Devices, systems, and methods are provided for goal-based audience selection. A method for generating an audience using machine learning may include receiving a request to generate an audience for an advertisement campaign, the request including an objective associated with presentation of the advertisement campaign. The method may include determining first user actions based on the objective, and identifying first users of a system who performed the first user actions using the system. The method may include determining second user actions performed by the first users prior to performing the first user actions, and identifying second users of the system who performed the second user actions and failed to perform the first user actions. The method may include generating a target audience to which to present the advertisement campaign, and causing presentation of the advertisement campaign to the target audience.

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

A media management system that handles a plurality of agreement information and a plurality of promotional campaigns for a specified upcoming time-frame, receives input parameters for each of the plurality of agreement information that corresponds to an upfront inventory utilization type and commercial operator break (COB) inventory utilization type of a plurality of inventory utilization types. Reserve inventory units for each of the plurality of promotional campaigns are determined based on historical data of an amount of inventory units utilized previously under the promotion inventory utilization type for the specified upcoming time-frame. An initial allocation of inventory units for the promotion inventory utilization type is done based on the determined reserve inventory units. Inventory units from a defined amount of inventory units are dynamically allocated among each inventory utilization types of the plurality of inventory utilization types.

Aspects of the subject disclosure may include, for example, receiving, from a first client device, a recommendation pertaining to an advertisement, storing a sender identification in association with an identification of the advertisement, wherein the sender identification includes an identification of the first client device, an identification of a first user of the first client device, or a combination thereof, determining that a score associated with the recommendation exceeds a threshold at least in terms of a likelihood of relevance of the advertisement to a second user of a second client device, based on the determining, transmitting the advertisement to the second client device based on the identification of the advertisement, and based on the determining, transmitting the sender identification to the second client device. Other embodiments are disclosed.

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.

Off-policy evaluation of a new “target” policy is performed using historical data gathered based on a previous “logging” policy to estimate the performance of the target policy. An estimator may be used, wherein either a quality-based estimator or a quality-agnostic estimator is used to weight the difference between an observed reward in the historical data and an estimated reward generated by the target policy. A quality-agnostic estimator may be used to evaluate an importance weight according to a threshold. In such examples, when the importance weight exceeds the threshold, the quality-agnostic estimator clips the importance weight at the threshold, thereby providing an fixed upper bound irrespective of the quality of the reward predictor. In other examples, a quality-based estimator is used, in which an upper bound incorporates the quality of the reward predictor in order to modify an importance weight used by the estimator.

A system for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed causes the at least one processor to receive interaction data corresponding to interactions between known objects and unknown objects, develop an interaction map, develop a profile for each unknown object, process each unknown object using a machine learning algorithm to generate a reduced set of unknown objects most likely to reciprocate and identify properties of each unknown object, and trigger a communication to each of the reduced set of unknown objects.

A computer-implemented method for enhancing and utilizing user data derived from digital interactions includes receiving user submission data comprising records generated by input into a client side application interface by a first user on a first computing device and transmitted from the first computing device to the aggregation point via at least one of a data transmission service and a data transmission provider. The method includes generating a first dataset associated with the first user, generating a data model based on data in the first dataset and one or more correlations based on attributes in the first dataset, and generating a modeled dataset based on the data model.