Techniques for using online engagement footprints for video engagement prediction are provided. In one technique, events are received from multiple client devices, each event indicating a type of engagement of a video item from among multiple types of engagement. One or more machine learning techniques are used to train a prediction model that is based on the events and multiple features that includes the multiple types of engagement. In response to receiving a content request, multiple entity feature values are identified for a particular entity that is associated with the content request. Two or more of the entity feature values correspond to two or more of the types of engagement. A prediction is generated based on the entity feature values and the prediction model. The prediction is used to determine whether to select, from candidate content items, a particular content item that includes particular video.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of:(a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

An environment interactive system includes: a first camera capturing first images of an area external to the vehicle; a second camera capturing second images of an area interior to the vehicle; a telematics control module determining a geographical location of the vehicle; a viewing device displaying items adjacent to, on or in front of detected objects and in a FOV of an occupant. The items correspond to the detected objects; and an infotainment module. The infotainment module: detects the objects based on the first images and the location of the vehicle; based on the second images, tracks at least one of a location of an eye of the occupant or a location of a body part of the occupant other than the eye; and based on the location of the eye and the location of the body part, displays the items via the viewing device in the FOV.

A synthesized, advertisement-based, video stream is generated first by receiving a video stream essentially consisting of a representative image of a person, a video stream comprising a place, and a video stream comprising an advertisement, and then combining the video stream essentially consisting of the representative image of the person with the video stream comprising the place and with the video stream comprising the advertisement into the synthesized, advertisement-based, video stream for transmission to an end-user device via which to display the synthesized, advertisement-based, video stream.

Systems, methods, and computer-readable media are disclosed for filtering data with probabilistic filters for content selection. In one embodiment, an example method may include determining a user interaction history with a first product identifier for a user account, determining a first parent product identifier of the first product identifier, and generating a database with the first parent product identifier and a user account identifier for the user account. Example methods may include determining a set of candidate content with first content and second content for the user account, determining a second product identifier associated with the first content, and determining a second parent product identifier of the second product identifier. Example methods may include determining that the second parent product identifier is not present in the database using a probabilistic filter, and determining that the first content is eligible for presentation.

Devices, systems, and methods are provided for goal-based audience selection. A method for generating an audience using machine learning may include receiving a request to generate an audience for an advertisement campaign, the request including an objective associated with presentation of the advertisement campaign. The method may include determining first user actions based on the objective, and identifying first users of a system who performed the first user actions using the system. The method may include determining second user actions performed by the first users prior to performing the first user actions, and identifying second users of the system who performed the second user actions and failed to perform the first user actions. The method may include generating a target audience to which to present the advertisement campaign, and causing presentation of the advertisement campaign to the target audience.

A method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label; performing the classifier using customer feature data during a third period of time to obtain customers having positive predicted classifier labels; and performing the regressor using the customer feature data during the third period of time for the customers having positive predicted classifier labels, to obtain CLVs of the customers.

A system and method is provided for delivering customized information related to a specific product of interest to consumer. In practice, the consumer uses a suitably enabled portable, mobile and/or wireless device (e.g., such as a mobile camera phone) to scan or otherwise read a marker associated with a product, retail item or other article of interest. From the marker, a unique ID is obtained (i.e., the marker ID). The marker ID is then used to cross-reference a URL or other like address in a database that relates marker IDs to corresponding URLs. The target URL is returned to the consumer's device and an http session is established with a content management server at the target URL. In one suitable embodiment, the content management server obtains a SKU and/or template web page ID that are associated with the marker ID in a database. Suitably, the SKU relates to the specific product with which the marker was associated. Having in this way determined the actual specific product of interest to the consumer, customized information related thereto can be delivered to the consumer's device, e.g., via a web page optimized for the identified device. Optionally, the web page content and/or template is obtained from a database that associates the same with the template web page ID received by the content management server.