Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. Example instructions cause a machine to at least, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; obtain a count of demographic impressions logged by a database proprietor; obtain a count of registered users of the database proprietor exposed to the media; and execute a process to determine a unique audience size by multiplying a count of the plurality of impressions by a square of the count of the registered users to generate a product; dividing the product by the count of the demographic impressions to generate a quotient; and determining the unique audience size based on a square root of the quotient.

A voucher ticket system and method of use employing a bill validator installed into any suitable automated machine, including an Automated Teller Machine (ATM), a gaming machine, etc. The bill validator is integrated with a bill reader, a voucher ticket reader, a reader to obtain an electronic voucher ticket from a portable computing device, a printer, and any other supporting peripheral devices. The method processes a deposit, a withdrawal, a transfer and a management of a financial balance of each account. The electronic money handling terminal and the electronic money transaction server communicate through an electronic money handling terminal interface. Upon recognition of an electronic money account of a gaming player, the system receives a voucher ticket master ID issued by a voucher ticket management system at the cash-out, and proceeds with a cash out event to the electronic money account.

Automatic performance triggered campaign adjustment. A hierarchical feature tree is generated. Each child node's feature is more specific than its respective parent node's feature. The discovery system creates a behavioral model comprising features of the feature tree which is used in the operation of an advertising campaign. A degraded model feature is detected at the discovery system by comparing a performance metric of a model feature from two different time windows. The discovery system matches a node of the feature tree with the degraded feature and selects a prospective model feature from an ancestor node of the matching feature's node. An estimated performance metric for the prospective model feature is determined and the results are used to decide if the prospective model feature should be incorporated into an updated model or not. The model can be updated with a new model feature selected from one or more prospective model features.

Aspects of the subject disclosure may include, for example, determining an ad play list for an Over-The-Top video stream requested by an end user device based on a price and/or a category of a creative derived from auctions with multiple Supply-Side Platform servers. The ad play list can be determined for the ad pod according to business rules, which can include a yield policy based on increasing revenue for a publisher of the video stream, and/or which can include a competitive separation policy enforced based on the category for bid responses. Other embodiments are disclosed.

A system for generating forecast data and optimizing a real-time bid on an impression comprising a user information database and a processor in communication with the user information database. The processor receives input data that is indicative of a probability of a user to purchase or rent real estate from the user information database. The processor generates a purchase window that is indicative of a predetermined time period when the user is likely to purchase or rent the real estate for the user based on the received input data. The processor then automatically adjusts a bid for an impression in real-time based on the generated purchase window.

The present disclosure generally relates to a ticketing platform configured to use machine-learning models to predict an optimal bid value for bidding on objects of content data. More particularly, the present disclosure relates to systems and methods for training machine-learning techniques to generate outputs predictive of the optimal bids for placing objects of content data on, for example, search result pages by using the machine-learning model to detect patterns in various signals, to implement a more efficient resource usage across campaigns.

A method and associated system of producing an advertising campaign of a product for an online marketplace seller including, under control of one or more processors configured with executable instructions, generating a single keyword advertising campaign of the product; collecting data on the single keyword; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output based at least in part on the data on the single keyword; generating a behavioral curve or table relating to an advertisement bid value and a cost per click value based at least in part on the machine learning component output; generating sales goal(s) of the product for the online marketplace seller on the online marketplace; generating an optimized advertisement bid value based at least in part on the sales goal(s) and the behavioral curve or table; and generating an optimized advertising campaign therefrom.

A method, apparatus, and computer program product are disclosed to improve generation of electronic communications. The method may include providing a plurality of content slots each configured to receive content. The method may also include maintaining a database comprising a plurality of content generators, and determining, using a processor, one of the plurality of content generators for providing content to each of the plurality of content slots. The determining the one of the plurality of promotion content generators or non-promotion content generators may include providing a first content generator of the plurality of content generators with a set of selection parameters, and receiving a first bid for one of the plurality of content slots from the first content generator. The first content generator may determine the bid based at least in part on the set of selection parameters. The method may also include assigning the first content generator to the one of the plurality of content slots based on the bid.

Systems, methods, and computer-readable media are disclosed for decoupled selection of content for semi-connected electronic devices. In one embodiment, an example method may include determining a set of candidate content for presentation at a semi-connected device, the set of candidate content comprising first content and second content of at least one content campaign, determining that an impression of the first content was served while the semi-connected device was disconnected from a network, determining that the first content and the second content were eligible for presentation at a first timestamp of a first impression interaction for the impression of the first content, determining a first bid amount for the first content and a second bid amount for the second content at a second timestamp of delivery of the first content to the semi-connected device, and determining a winning bid amount for the first impression interaction using the first bid amount and the second bid amount.

The present disclosure provides a computer-implemented method and system for real time advertisement auction on telecommunication notification channels. The computer-implemented method and system corresponds to an advertisement serving system. The advertisement serving system integrates a plurality of advertisers and one or more telecommunication notification channels. In addition, the advertisement serving system receives a subscriber data, a subscriber interaction data associated with a plurality of subscribers. Further, the advertisement serving system receives an advertiser data associated with the plurality of advertisers. Furthermore, the advertisement serving system analyzes the subscriber data, the subscriber interaction data and the advertiser data. Moreover, the advertisement serving system determines a subscriber context data. Also, the advertisement serving system generates a plurality of categories. Also, the advertisement serving system sends request to the plurality of advertisers for bidding. In addition, the advertisement serving system determines the highest bid and serves one or more advertisements.