An advertisement delivery apparatus according to the present application includes a receiving unit, a classifying unit, and a determining unit. The receiving unit receives a delivery request for a plurality of advertisements to be delivered to a terminal device. The classifying unit classifies the advertisements for which the delivery request is received by the receiving unit into a plurality of groups, based on a predetermined condition. The determining unit determines the advertisements to be delivered, for each of the groups classified by the classifying unit.

Techniques described herein provide for a live advertising processing engine service to act as an intermediary between client applications and advertising networks. The live advertising processing engine service can provide an interface with which the client can interact, via an application programming interface (API) or directly through the use of a standardized language, thereby allowing client applications to receive advertisement data without the need to be customized to communicate with specific advertising networks. The live advertising processing engine service can further track where, in playback the live content, a client currently is, to indicate to the client—or other application—when advertisement breaks are and/or provide supplemental advertisement information to enable the client to provide a rich user experience.

Disclosed herein are systems, methods, and apparatuses that can provide, for example, dynamic page feed recognition for providing zero-configuration analysis of web pages and tailored advertisement provision to website visitors. The systems, methods, and apparatuses can be used to analyze inventory feed. Based on analysis of user interaction data on the web pages, one or more matching methodologies can be used to determine matching data between the visited web pages and the inventory feed.

A method, apparatus, and computer readable medium for facilitating communication between a merchant and a client are provided. An example method may include creating a beautician identification record for a beautician, receiving an information related to an interaction between the beautician and a client, determining at least one product or service for the client from the information, and creating, using a processor, a communication to the client. The communication may include an indication of the at least one product or service. The method may also include sending the prescription to the client. Example systems, apparatuses, and computer readable media are also provided.

The present disclosure provides a system and method for serving one or more advertisements during streaming of dynamic, adaptive and non-sequentially assembled video. The method includes reception of a set of preference data and a set of user authentication data. The method includes fetching of the one or more tagged videos. The method includes fragmentation of each tagged video into the one or more tagged fragments. The method includes segregation of one or more mapped fragments into one or more logical sets of mapped fragments. The method includes mining of semantic context information from each mapped fragment. The method includes clustering of the one or more logical sets of mapped fragments into one or more logical clusters. The method includes allocation and insertion of one or more advertisements in one or more advertisement slots. The method includes assembling of the one or more logical clusters of mapped fragments.

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: monitoring first activities of a user over a first time period; based on the first activities of the user over the first time period, identifying, using a Markov model, a first probability of the user being in a first state; determining when the first probability is above a first probability predefined threshold; in response to determining when the first probability is above the first probability predefined threshold, automatically customizing first content on a graphical user interface for the first state to create a first graphical user interface on an electronic device of the user while the user is determined to be in the first state; monitoring second activities of the user over a second time period occurring after the first time period and after the user has been determined to be in the first state and before the user has been determined to be in a second state; based on the second activities of the user over the second time period, identifying, using a mixed model different from the Markov model, a second probability that the user has transitioned from the first state into the second state, wherein the second state is related to the first state; determining when the second probability is above a second probability predefined threshold; and in response to determining when the second probability is above the second probability predefined threshold, automatically customizing a second content on the graphical user interface for the second state to create a second graphical user interface on the electronic device user while the user is determined to be in the second state. Other embodiments are disclosed herein.

A method and system of determining a viewership of a media content includes displaying a media content on an outward display device. For each vehicle in a predetermined range of the outward display device, a data packet comprising data from one or more sensors of the vehicle is received. It is determined whether the vehicle is unique based on the data packet. Upon determining from the data packet that the vehicle is unique, a dwell time of the vehicle is determined with respect to the displayed media content on the outward display device.

This document describes the collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications. Specifically, the disclosed devices and systems may be employed to produce one or more marketing and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating user engagement, thereby accurately determining the cost benefits of the campaign. The analytic results provided may then be used to guide the generation of original web content, such as for the purposes of enhancing customer or follower experience, driving business, and for driving advertising campaigns. Alternatively, web content that is in the public domain, and determined to perform well, can be reproduced, referenced, or otherwise referred to, in the context of promoting or presenting the user's web content.

An online system provides a content data model to content providers for optimizing content creation. The content data model is a hierarchical model with multiple levels for content creation, e.g., campaign level, content item set level and content item level. At each level of the content data model, a content provider can specify certain information concerning the content creation at that level, such as optimization goal for each level. The information specified at each level of the content data model is applied to all elements under that level by the online system during the content creation process. With the content data model, a content provider can efficiently design creative campaigns by specifying objectives, optimization goals, target audiences and budgets, etc., at different design levels. The online system dynamically optimizes content item creation based on information about creatives to be included in a content item for a target user.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.