Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reducing redirects. In one aspect, a method includes receiving request data indicating that a user device has requested a content item. The request data specifies other data processing apparatus to which user interactions with the content item are to be reported. The content item includes a reference to a resource that is requested in response to user interaction with the content item. Response data is provided. The response data includes data that cause presentation of the content item. Interaction data is received specifying user interaction with the content item occurred. Redirect data is provided that cause the user device to be redirected to the resource. Reporting data is provided to the other data processing apparatus, specifying user interaction with the content item occurred. The reporting data is provided asynchronously relative to the redirect data.

Methods, systems, and storage media for determining advertisements to display are disclosed. Exemplary implementations may: receive at least one recent user engagement signal; based, at least in part, on the at least one recent user engagement signal and a social media explore context, determine at least one first advertisement to display for a user associated with the at least one recent user engagement signal; and cause display of the at least one first advertisement within the social media explore context.

Provided is a sales management system configured to: acquire: sales data on a target product sold at a shop to be targeted, the sales data indicating a plurality of time-series values relating to sales of the target product; and a period of a bargain sale of the target product at the shop to be targeted; and output an image including a graph which is based on the plurality of time-series values indicated by the acquired sales data, and in which a display mode for an area corresponding to the period of the bargain sale is different from a display mode for another area.

Methods and apparatus to associate transactions with media impressions are disclosed. An example method includes transmitting a request for commercial transaction information to a database proprietor, the request including an identifier corresponding to a media impression associated with media presented via a computing device; receiving the commercial transaction information in response to the request, the commercial transaction information comprising data associated with a commercial transaction conducted using an account accessed by the computing device; and associating the media impression with the commercial transaction.

Interactive product auditing with a mobile device is described. Example methods disclosed herein include performing, with an auditing device, image recognition based on a first set of candidate patterns accessed by the auditing device to identify a first product in a first region of interest of a segmented image. The disclosed example methods also include prompting, with the auditing device, a user to enter input associated with a first grid of the first region of interest displayed on a display, the first grid including the first product. The disclosed example methods further include determining, with the auditing device, a second set of candidate patterns to use to identify a second product in a second region of interest of the segmented image, the second set of candidate patterns determined based on the user input and a group of products identified in a neighborhood of the first region of interest.

A non-transitory computer-readable recording medium stores therein an extraction program that causes a computer to execute a process including: generating a plurality of combinations of conditions relating to a plurality of item values included in data; calculating an index value that indicates a degree of cooccurrence between a specified response variable and each of the plurality of combinations, by using a machine learning model that estimates a response variable from the plurality of item values, the machine learning model having been trained by using the data; and extracting a specific combination from among the plurality of combinations based on any one of the condition and the index value.

A leasing AI platform receives a message pertaining to leasing a real estate unit, generates a token matrix representing at least a portion of the message, and provide the token matrix as an input to a trained machine learning model. An output of the trained machine learning model comprises an indication of a first category associated with the at least the portion of the message. The leasing AI platform identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and automatically executes the one or more actions without human involvement in response to receiving the message.

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.

Methods, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a controller to cause a people meter to emit a prompt for input of audience identification information at a first time and determine a first audience count based on the input, an audio detector to determine a second audience count based on signatures generated from audio data captured in the media environment, and a comparator to cause the people meter to not emit the prompt for at least a first time period after the first time when the first audience count is equal to the second audience count.