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.

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 directed to managing computer resource allocation by monitoring signals indicating demand for services utilizing computer resources are described. A method includes maintaining, for each first event of first events, historical registration data and respective parameter values of the first event and identifying, for a second event having an open registration status, respective parameter values of the second event, and registration data for the second event. The method includes computing a similarity score between the second event and each first event of the plurality of first events, based on the respective parameter values of the first event and the second event and the registration data of the second event and the historical registration data of the first event, generating, for the second event, a projected number of entities based on determined information and determining a ranking of the second event.

A user identifies products offered by the user to an online system. The online system identifies a product offered by the user in an image by applying a identification model to the image. If the online system identifies a product in the image with at least a maximum confidence value, the online system automatically tags the post with metadata about the product or suggests a tag to the user. If an object in the image could be one of multiple products, the online system identifies the multiple products to the user, which may be ordered based on confidences of matching the object, allowing the user to select which product is in the image. If the unlisted identifies a product in the image with less than a minimum confidence value, the online system identifies the user's offered products and suggests that the user select a product.

Aspects of the subject disclosure may include, for example, a method that includes identifying, by a processing system including a processor, a first content source and a second content source as a ground truth set, training a machine learning model for use in determining a target audience score under a user-defined metric for a target audience of a content source not included in the ground truth set, and generating the target audience score using the trained model. Other embodiments are disclosed.

A computer-implemented method for optimizing electronic content delivery for non-measurable users includes receiving a feature vector for each electronic content impression opportunity, receiving a feature vector for each delivered item of electronic content for measurable users, receiving an in-target indication for each delivered item of electronic content for measurable users, estimating a probability that an electronic content impression opportunity with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications, receiving an in-target threshold value, generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, determining whether the estimated probability is greater than the in-target rate control signal, and generating conditions for delivering a new item of electronic content for an electronic content impression opportunity.

A system that analyzes a user’s communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

A system for building sets of user devices communicatively connected to a communications network, having a common or related topical interest in items available via communications over the network by the user devices with an item server. The user devices, respectively, have respective network identifiers and each exhibits related topical activity of particular articles in a subnetwork of the communications network, for example, in a social communications network. A server communicatively connected to the subnetwork detects communicative interactions over the subnetwork of the user devices with the particular articles. The system includes a collector communicatively connected to the communications network for obtaining a group of articles from the server, an identifier communicatively connected to the collector, the identifier determines the particular articles as a subset of the group of articles, a query device communicatively connected to the identifier and the communications network, the query device communicates over the communications network with the subnetwork obtaining the respective network identifiers of the user devices, respectively, interactive with the particular articles through communications over the communications network, a generator communicatively connected to the query device, the generator derives a tag corresponding to the particular articles, and a reporter is communicatively connected to the generator for delivering the respective network identifiers of the user devices, respectively, interactive with the particular articles and the tag. A topical collection of articles serves as a proxy to communications devices having particular interests for use in targeted advertising.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.