Systems and methods are provided for a brand search ecosystem for brands and branded products, locations, features, and services to enhance search results with real-time content and input on business rules, algorithms, and information from brandholders and provided over various modalities, including phones, TVs, kiosks, and speech recognition. The systems and methods include a brand name database including a plurality of brands and brandholders, bidders, and other content providers associated with respective brands. The search engine receives search requests from users, accesses the brand name database, and provides search results to the users based on a variety of factors, including the identity, search history, membership in a brand's rewards programs, aggregated feedback and ratings of others users, and direct feedback and ratings of the user conducting the search. The systems and methods also enable and process incentives to brands, bidders, content providers, and end users to encourage usage and real-time feedback, ratings, and other information on brands, branded products and services, and specific branded locations to further enhance search results.

Systems, methods, and computer-readable media that may be used to modify a voice action system to include voice actions provided by advertisers or users are provided. One method includes receiving electronic voice action bids from advertisers to modify the voice action system to include a specific voice action (e.g., a triggering phrase and an action). One or more bids may be selected. The method includes, for each of the selected bids, modifying data associated with the voice action system to include the voice action associated with the bid, such that the action associated with the respective voice action is performed when voice input from a user is received that the voice action system determines to correspond to the triggering phrase associated with the respective voice action.

A system and methods for generating dynamic market pricing that is fair for both publishers and advertisers and the use of the pricing in real-time auctions. When a user views content over the internet, an online publisher provides content to the user with executable instructions, which notify an advertisement source that there is an impression for filling by an advertiser. The impression is submitted to a real-time bidding market for competing advertisers to bid to fill the impression with an advertisement. The system and methods generate dynamic pricing for the individual competing advertisers and use the dynamic pricing to compare advertiser bids to determine a highest or winning bid and a clearing price associated with the highest or winning bid.

The present disclosure is directed to systems and methods of providing content. A server can generate a request for a push content item for an account identifier linked with a computing device. The server can establish a push auction for the account identifier with multiple candidate push content items. The server can determine an auction score for each candidate push content item and select a push content item therefrom based on the auction score. The server can determine a parameter for the account identifier and control delivery of the selected push content item based on a delivery control policy. The server can compare a value of the parameter with a threshold value to authorize the push content item. The server can provide the selected and authorized push content item for presentation in a push content slot via the computing device linked to the account identifier.

Systems and methods are disclosed for employing supervised machine learning methods with activities and attributes of viewers with truth as input, to produce models that are utilized in determining probabilities that an unknown viewer belongs to one or more demographic segment categories. Using these models for processing viewer behavior, over a period of time a database of known categorized viewers is established, each categorized viewer having a probability of belonging to one or more segment categories. These probabilities are then used in bidding for online advertisements in response to impression opportunities offered in online media auctions. The probabilities are also used in predicting on-target impressions and GRPs (Gross Rating Points) in advance of online advertising media campaigns, and pricing those campaigns to advertiser/clients. Strategies are also disclosed for fulfilling a campaign when an available inventory of known categorized viewers is not adequate to fulfill a campaign in a required runtime.

A real-time messaging platform and method are disclosed which can be used to promote content in the messaging platform. In one embodiment, the promotion system is disclosed which performs initial candidate selection so as to narrow down the set of candidate promotions before applying more expensive processing. The candidate selection takes advantage of the connection graph information associated with accounts in the messaging platform to identify targeted accounts. In another embodiment, the promotion system uses a prediction model to predict a user's engagement with the promotion and utilizes the prediction to assist in ranking the candidate promotions. Promotions can be assigned metrics based, for example, on a weighted combination of user engagement rates, decayed with time to reflect an intuition that recent interactions by one or more users with the promotion will have a greater impact than older interactions with the promotion.

Whether video advertisements are being delivered to a human user or a machine is determined passively, i.e., without having the user to perform any explicit actions. Based on the time and frequency of ad requests generated from a user device, a daily schedule is estimated for the user. The estimated daily schedule is compared with a daily schedule pattern to determine whether the user is a human user or not.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing data and analysis for advertising on networked devices. One of the methods includes creating a vector of identifiers representing an ad opportunity. The method includes linking data attributes that describe the ad opportunity to the identifiers. The method includes expressing the data attributes following predefined scheme of hierarchy. The method includes linking a taxonomy describing data attributes. The method includes obtaining outcome measurements of ad events associated with the ad opportunity. The method also includes associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition are disclosed. In one aspect, a method includes receiving a candidate adword from an advertiser. The method further includes generating a score for the candidate adword based on a likelihood of a speech recognizer generating, based on an utterance of the candidate adword, a transcription that includes a word that is associated with an expected pronunciation of the candidate adword. The method further includes classifying, based at least on the score, the candidate adword as an appropriate adword for use in a bidding process for advertisements that are selected based on a transcription of a speech query or as not an appropriate adword for use in the bidding process for advertisements that are selected based on the transcription of the speech query.

Guiding purchasing via smartphone by, determining, via smartphone input of a user, the smartphone user's intent to purchase a given product. At least one sequence of tasks to purchase each of a plurality of products is determined. The determined intent to purchase the given product is associated with a determined sequence of tasks to purchase one of the products in the plurality of products. The smartphone user's current state in the associated sequence of tasks is determined. The smartphone user is notified, via the smartphone, of the next uncompleted task from the associated sequence of tasks based on the smartphone user's current state in the associated sequence of tasks.