An advertising system and its method of use are disclosed. The advertising system includes at least one sensor coupled to a fixed location, where the at least one sensor is configured to generate an impression based on at least one characteristic of a proximate automobile, at least one processor configured to receive the impression, at least one server, having an advertising database and a user database, and at least one delivery mechanism. Preferably, the advertising database contains information relating to at least one advertisement and the user database is configured to check if the at least one characteristic matches an existing entry for a user, and either associate that impression with a user or create a new entry for the impression.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.

A data processing system and method for delivering a personalized recommendation to an individual, using computerized, dynamic assessment and classification of communication interaction records, is described. The system and method comprises a machine learning application to (1) classify a persona based on communication processing of communication interaction records captured over time, and (2) generate an adaptive personalized recommendation for use within a coaching, advisory or other personalized service. The system and method allows for improved assessments and identification of factors affecting achievement of a goal or outcome, thereby allowing for adaptive, personalized recommendations, improving delivery of personalized support services, and improved success rates for achieving a defined outcome. The system and method may also be used to create a collection of persona analytics information that may be used to visualize and evaluate trends associated with an individual or within a given population.

Disclosed herein are a method and system that utilize a programmed content predictor to dynamically select electronic publishing content. In particular, the content predictor applies a selection model to select content for one or more selected webpages presented during an electronic transaction. The selection model utilizes a set of one or more machine learning models to select content based on calculated quality scores. The nature of the quality scores determined by the quality score model depend on the particular application. The predictor generates and populates a permutation quality table based on a set of selected content items and page variant, wherein the page variant defines locations of content positions within a webpage. The predictor then consumes the selection model to select a best permutation of content item-content position combinations to be returned for display on a webpage.

Announcing new users of an electronic communications system includes receiving an indication of a new user of an electronic communications system. The new user has an identifier for the electronic communications system. Existing users of the electronic communications system that are known to the new user are identified. The identified existing users are sent a message notifying the identified existing users of the identifier of the new user. The identified existing users may be enabled to add the identifier of the new user to participant lists used by the identified existing users. The new user may be enabled to add identifiers of the identified existing users to a participant list of the electronic communications system used by the new user.

Systems and methods are described herein for inserting contextual advertisements into a virtual environment. While presenting a virtual environment, a media device receives speech input. The media device selects an advertisement from an advertisement database based on the speech. The media device then determines the current environment of the virtual environment and generates a virtual object that is related to the current environment. The media device overlays the selected advertisement over the generated virtual object and displays the generated virtual object in the virtual environment.

In general, embodiments of the present invention provide systems, methods and computer readable media for recommending contextually relevant promotions to consumers in order to facilitate their discovery of promotions that they are likely to purchase from a promotion and marketing service.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate resource and space efficient analysis of large scale datasets. Methods include obtaining activity data for objects in a dataset. For each data item the dataset, a hashed parameter having a binary representation is generated using an identifier for the object. A register is identified from in a set of registers based on the hashed parameter. A determination is made that the hashed, parameter for the object contributes to an aggregation amount that specifies a number of occurrences of the object in the dataset Based on this determination, an aggregation amount stored in the register is updated. Based on aggregation amounts stored in the set of registers, a reporting output is generated that provides an aggregate distribution of the objects in the dataset based on the activity data for the objects.

Techniques for programmatically interfacing with mobile chat sessions are discussed herein. Some embodiments may include one or more servers configured to: access the mobile chat session hosted by the mobile chat server; receive input chat data from the mobile chat server; determine promotions scores of candidate promotions based at least in part on the input chat data; and provide the output chat data to the mobile chat server including an electronic marketing communication of one or more promotions selected based on the promotion scores. The one or more servers may be further configured to programmatically interact with chat accounts within the mobile chat session, such as to request additional search terms for improved promotion relevance targeting, among other things.

A data processing system collects messages to create user profiles for users of user devices. The messages are collected from various systems and include data regarding user events on user devices in order to create user profiles. The user profiles are stored. In response to a request for information corresponding to a user device, a user profile is identified using data from the request and information from the profile is provided to the requester. A method and system is utilized to select and provide targeted advertisements.