Systems and methods of training and deploying a machine learning neural network in digital advertising. The method comprises receiving a plurality of input datasets at respective ones of a plurality of input layers of the neural network, the neural network being instantiated in one or more processors. The neural network comprises an output layer interconnected to the plurality of input layers via a set of intermediate layers, each of the input datasets being associated with a respective digital ad input attribute, ones of the intermediate layers being configured in accordance with an initial matrix of weights; and training the neural network in accordance with the plurality of input layers based upon recursively adjusting the initial matrix of weights by backpropogation in generating, at the output layer, at least one digital ad output attribute in accordance with diminishment of an error matrix computed at the output layer of the neural network.

A computer-implemented method, computer-readable medium, and system may be used to sell products by a merchant via a publisher website hosted by a publisher. Content may be received from the publisher website, wherein the content is associated with a product. A web page including the content may be displayed for a user, and first user input may be received from the user indicating interest in the product. Responsive to the first user input, product information about the product may be received from a merchant distinct from the publisher and, without navigating away from the publisher's website, the product information may be displayed. Further, without navigating away from the publisher's website, second user input indicating a desire to purchase the product may be displayed, and, responsive to the second user input and without navigating away from the publisher's website, an order to purchase the product may be received by the merchant.

This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

A system and method for generating an electronic catalog booklet for online computer users is disclosed. The system and method in an example embodiment includes providing access to a database of item listings; enabling selection of a plurality of item listings from at least some of the item listings provided in the database; enabling arrangement of the plurality of selected item listings on a plurality of pages in a computer-generated electronic catalog booklet; recording an identifier of the computer-generated electronic catalog booklet; and recording information associating each of the plurality of selected item listings with the identifier of the computer-generated electronic catalog booklet.

A wireless charger system for inductively charging a rechargeable battery of an implantable pulse generator (IPG) implanted in a human body is provided. A charging coil in the charger is wirelessly coupled to a receiving coil of the IPG to charge the rechargeable battery. An end-of-charge (EOC) circuit continuously monitors the reflected impedance from a reflected impedance sensor and determines the end of charge when a predetermined pattern of the reflected impedance corresponding to an EOC signal from the IPG is received. Advantageously, receiving the EOC signal through the charging coil eliminates the need to provide a separate communication circuit in the IPG that communicates with the charger.

Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; identifying each possible pair of devices from which requests were received; calculating for each possible pair of devices a probability that the pair of devices are owned or operated by a common user; and prompting a user to either confirm a characteristic of a prior browsing session or to log-in to an account associated with the common user based on a comparison of the calculated probability to one or more thresholds.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting content items for presentation along with publisher resources. In one aspect, a method includes receiving a request for a content item for presentation at a user device with a publisher resource; receiving, from a content item provider a first selection value for each of a plurality of content items provided by the content item provider; determining, for each of the content items and using accessed distribution parameters, a second selection value distinct from the first selection value; and determining, for each of the content items and independent of a bid corresponding to the content item, a combined selection value based on the first selection value for the content item and the second selection value for the content item.

A self-consistent inception architecture includes a process that integrates online data and offline data to determine an estimated lift (“prior lift”) in the number of unique visitors (UVs) to a website caused by a television (TV) spot airing on an offline medium. The prior lift is used to adjust a UV profile of the website. A baseline thus produced is fitted through an inception process in which a locally weighted scatterplot smoothing algorithm is applied iteratively until a final baseline converges. The baseline from the inception process is used to determine a calculated lift. If the prior lift and the calculated lift are not consistent (e.g., within a threshold), the process is run iteratively until the prior lift and the calculated lift are consistent. The calculated lifts can be used to determine and visualize performance metric(s) relating to media creatives such as TV spots airing in the physical world.

A method includes training a plurality of different types of machine learning models using a training dataset to produce a set of trained machine learning models and determining a lift of each trained machine learning model in the set of trained machine learning models using a validation dataset. The method also includes selecting a trained machine learning model from the set of trained machine learning models that has a highest lift of the set of trained machine learning models and predicting a likelihood that a person will perform an action by applying the selected trained machine learning model to data about the person.

Systems and methods are described for the generation of domain names that may be associated with a particular user device and may be encrypted to obfuscate the domain names of content requested by the user device.