Methods, apparatus, systems and articles of manufacture to expand panelist enrollment are disclosed. An example first media device includes processor circuitry to at least one of instantiate or execute the machine readable instructions to scan at least one local area network to which the first media device is connected, store an identification of a second media device connected to the at least one local area network, in response to scan of the at least one local area network indicating that the second media device is commonly connected to the at least one local area network with the first media device, add the identification of the second media device to a fingerprint, and transmit the fingerprint to a central facility.

Devices are disclosed for home based electric vehicle (EV) charging. According to one embodiment, an EV charger includes a processor, a memory electrically coupled with the processor, EV charging circuitry electrically coupled with the processor, a network interface electrically coupled with the processor, and a broadcast transmitter electrically coupled with the processor.

Methods, devices, and systems are disclosed for home based electric vehicle (EV) charging. According to one embodiment, a method is implemented on an EV charger for determining relative position of a mobile device to an EV charger in accordance with embodiments of the present disclosure. The method includes receiving known location data associated with a global position of the EV charger, wherein the known location data has an accuracy better than 10 centimeters, receiving first global navigation satellite system (GNSS) timestamped data associated with the EV charger from a first constellation of GNSS satellites, determining first GNSS location data based on the first GNSS timestamped data, and determining first GNSS error data based on the first GNSS location data and the known location data.

An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

A system creates alerts of issues of importance to an organization. While an organization does not want to miss the opportunity to run an advocacy campaign on an issue of importance, it also does not want to run an unsuccessful campaign that might burden or bore those to whom the campaign is directed. The system maintains a history of previous campaigns as well as success outcomes of those campaigns. A computational model operates on selected previous campaigns and a candidate issue to determine a score indicative of whether a campaign should be run. The score can include a combination of relevancy to criteria for an issue of importance, similarity to issues from previous campaigns, and outcome success data for the selected previous campaigns. If the combined score meets a threshold, the system can present an option to initiate a new advocacy campaign on the candidate issue.

The present invention discloses a computing system for targeting advertising audiences, comprising a user data processing module, a user behavior characteristic processing module and a data packaging module. The user data processing module receives a plurality of user data to obtain to-be-analyzed overall data. The user behavior characteristic processing module is connected to the user data processing module, and the user behavior characteristic processing module derives a store active visitor set from to-be-analyzed overall data and a target user group set from a store active visitor set. The data packaging module is connected to the user behavior characteristic processing module, and the data packaging module performs a packaging process on the target user group set to obtain an advertising audience packet and transmits the advertising audience packet to an advertising platform.

A sales promotion support device according to the present disclosure includes: one or more memories storing instructions; and one or more processors configured to execute the instructions to: acquire at least one of behavior information that is information regarding a behavior of a user and a time at which a sales promotion plan is to be provided; estimate an acceptability of the user to a sales promotion plan based on the at least one of the behavior information and the time; and determine a sales promotion plan to be provided to the user according to the acceptability.

A targeted advertising scheduling and displaying process in the context of product terminal displays is made possible by the present invention. An embodiment described herein provides for a first server retrieving digital content from CMS. The first server formats the digital content to be output by at least one product terminal of a plurality of product terminals in accordance with one or more characteristics of the at least one product terminal. Each product terminal of the plurality of product terminals is disposed in a fixed location remote from the first server. The server identifies the at least one product terminal from the plurality of product terminals to output the formatted digital content and causes output of the formatted digital content by the at least one product terminal by transmitting the formatted digital content to a second server associated with the at least one product terminal.

This disclosure describes embodiments of methods, systems, and non-transitory-computer readable media that personalize visual content for display on digital signage near a projected location of a person by mapping visual content to physical items selected by the person. In some examples, the disclosed system identifies physical items selected by a person based on signals from the physical items, such as signals emitted by RFID tags affixed to (or other devices associated with) the physical items. The disclosed system analyzes the collection of physical items—as identified by the signals—to tailor digital signage content specific to the person. The disclosed system further tracks the location of the person as the person moves through a physical space and interacts with the physical items. Based on the tracked positions, the disclosed system determines a digital sign in proximity to a predicted location of the person to display the personalized visual content.

A system and method for real-time geophysical social grouping comprising customer profiles and venue profiles, wherein the profiles comprise expressed and inferred attributes, and a social grouping and recommendation server which utilizes machine learning algorithms on the profiles to generate recommendations for social group pairing, venues, and activities. Attribute matching provides optimized grouping between customers who share certain commonalities while also providing venues a system for locating and attracting ideal customers. Machine learning algorithms may be used to analyze profile attributes and identify patterns of commonality that would not otherwise be recognized. This system allows patrons to meet, dine, and socialize with one or more matched individuals at a venue that satisfies all participants preferences and attributes.