Systems, apparatus, and methods for providing diversified promotions are discussed herein. Some embodiments may include a system including circuitry configured to provide diversified promotions within an impression or other communication including multiple promotions. For example, promotions may be associated with diversity characteristics to determine the diversity of promotional offerings within an impression and/or within a series of impressions over a period of time sent to a targeted consumer. When an impression is determined to lack diversity based on the diversity characteristics of the promotions, the system may be configured to take corrective action such as by replacing at least one promotion with a replacement promotion that provides greater diversity to the impression.

A method for controlling a refrigerator performed by an artificial intelligence computing device may include photographing a food material stored inside of the refrigerator; comparing the photographed image with a preconfigured previous image, and transmitting storage information of the food material to a cloud according to a comparison result; learning the transmitted storage information of the food; determining a stock state of the food material based on the learned storage information of the food material; and determining whether to transmit relation information related to the food material depending on the determined stock state of the food material. One or more of the artificial intelligence computing device according to the present disclosure may be linked with an Artificial Intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an Augmented Reality (AR) device, a virtual reality (VR) device, a device related to 5G service, and the like.

A device may receive first customer data, and may receive first contact data. The device may generate second customer data that includes the first customer data, and may generate second contact data that includes the first contact data and additional contact data. The device may generate a quantity of simulated future communications based on differences between the first customer data and the second customer data and between the first contact data and the second contact data, and may process the quantity of simulated future communications, with a machine learning model, to determine a probability distribution for an opt out event. The device may determine a relationship between the quantity of simulated future communications and probabilities of the opt out event, and may identify a particular probability of the opt out event based on the relationship. The device may perform actions based on the particular probability.

Portable devices include environmental sensors that generate metrics about the environment (e.g., accelerometers detecting impulses and vibration, and GPS receivers detecting position and velocity). Such devices often use environmental metrics to extract user input directed at the device by the user, and status information about the device and the environment. Presented herein are techniques for using environmental metrics to infer physical activities performed by the user while attached to the device. For example, jogging may be inferred from regular, strong impulses and typical jogging speed; walking may be inferred from regular, weak impulses and typical walking speed; and riding in a vehicle may be inferred from low-level vibrations and high speed (optionally identifying the type of vehicle ridden by the user). Based on these inferences, the device may automatically present applications and/or or adjust user interfaces suitable for the user's physical activity, rather than responsive to user input.

Computer-readable media, computer systems, and computing devices of a method for facilitating providing offers utilizing population densities are provided. In embodiments, a population density for a geographical space is determined based on locations of a plurality of user devices. The population density associated with the geographical space is used to determine to provide an electronic offer to a user. The electronic offer may be associated with an item in the geographical space to entice the user to move to the geographical space. In accordance with determining to provide an electronic offer to the user, the electronic offer is provided for viewing by the user via a user device.

Vehicles, components, and methods present information based on context, for example presenting a first list or menu of items (e.g., food) or first set of signage or color scheme when in a first location or during a first period, and presenting a second list or menu of items (e.g., food) or second set of signage or color scheme when in a second location or during a second period. For instance, a first menu of items (e.g., relatively more expensive entrees, beverages) may be displayed via one or more displays or screens at a first location during a first period, and a second menu of items (e.g., relatively less expensive entrees, beverages) may be displayed at a second location during a second period. Context (e.g., present location, destination, date, day, period of time, event, size of crowd, movement of crowd, weather) may be manually provided, or autonomously discerned, and presentation automatically.

Systems and methods are provided for automatically generating and maintaining user profile cookie sets. The user profile cookie sets may be used by a web crawler when gathering data such as advertisement data associated with one or more websites. The cookie sets may be generated by choosing a user profile with a set of user traits, selecting a set of websites related to the user traits, and browsing the selected set of websites using a web crawler while allowing the website to place cookies in storage of the web crawler. The cookie sets may be maintained by selecting a website to browse, selecting a user profile associated with the selected website, loading a previously generated cookie set for the selected user profile into the storage of a web crawler, and loading the webpage while allowing the website to place, update, or replace cookies in the storage of the web crawler.

Disclosed embodiments relate to performing an advertisement campaign filtering process while protecting the privacy of both an advertiser and a user of a personal computing device. Techniques include maintaining a plurality of sets of advertising competition rules, the plurality of sets of advertising competition rules being associated with a plurality of discrete advertising campaigns; for a set of advertising competition rules from the plurality of sets of advertising competition rules: identifying advertisement targeting criteria associated with the set of advertising competition rules, differentiating, from within the advertisement targeting criteria, between advertisement-sensitive targeting criteria and advertiser-insensitive criteria, and transforming the advertisement-sensitive sensitive targeting criteria; and transmitting, to the personal computing device, at least a portion of the transformed advertisement-sensitive targeting criteria.

Systems and methods for controlling contacts with a client's users make use of segment-based contact limits. A contact limit sets a maximum number of contacts that a client can have with a user within a predetermined time window. A segment-based contact limit only applies the contact limit to a subset of all the client's users. The type of contact being limited could include messages that are sent to the user or advertising or sales campaigns that are conducted for the user. A segment is a subset of all of the client's users, and a segment may be defined based on one or more filters.

A computer-implementation method for dataless contextual targeting includes the following steps. First, automatically crawling noisy labeled corpora from one or more sites using a category mapping from first categories to second categories. Second, applying one or more statistical methods to automatically mine representative keywords for each of the first categories from the noisy labeled corpora. Applying dataless classification learning to induce a text classifier with the automatically mined representative keywords and unlabeled web pages as input.