A method for implementing an enterprise's outbound campaign in which an offer is communicated to target customers in a manner personalized for each. The method includes: providing a customer profile database; providing a personalization platform; updating the customer profile of a first target customer according to data received from a personal bot running on a device of the first target customer; deriving interaction predictors for the first target customer relating to behavioral tendency for a interaction type; receiving an enterprise campaign dataset from the enterprise related to the outbound campaign, the enterprise campaign dataset including: information describing the offer, context; and list of the target customers; augmenting, at the personalization platform, the enterprise campaign dataset with the interaction predictors related to the first target customer to produce an enriched campaign dataset; and transmitting the enriched campaign dataset to the enterprise.

Provided are methods and systems for optimization and personalization of existing communication channels. An example method commences with aggregating user data received from a plurality of data sources and creating a user profile for a user associated with the user data. The method includes creating a business logic for user interactions of the user via the existing communication channels. The business logic includes trigger conditions and actions corresponding to the trigger conditions. The method continues with mapping, using a recommendation algorithm, content to the user according to the business logic. The content is templatized to create personalized communication messages for the existing communication channels. The method includes receiving feedback data in response to the user interactions with the personalized communication messages. Based on the feedback data, the user profile is updated, and the recommendation algorithm and the next suggestion communication action are updated based on the updated user profile.

A unified platform obtains, stores, and shares search results based on user profiles or groups with a common interest. User profiles or group profiles may be used to identify categories containing objects related to a common interest, and the objects may be selected for placement in a graphical user interface from which they may be purchased by or for the user. The objects may preferably be virtual objects stored in a virtual locker or other virtual display unit. Users are able to conduct web-based searches for products or services from different websites and store dimensional representations of virtual products or services in a unified platform.

The present disclosure describes a smart apparatus, comprising: determining if an application is authorized to use a user profile, when the application installed on the smart apparatus requests the user profile; and transmitting the user profile to the server corresponding to the application when the application is determined to have a user right.

A media sharing and communication system, including a recording mechanism that records a desired portion of media upon activation by a first individual user, a first user transmitter/receiver that transmits the portion of media and a message generated by the first individual user regarding the portion of media to a second individual user and is capable of transmitting a message to a second individual user, a confirmation mechanism that confirms that the second individual user is authorized to view the portion of media, a notification mechanism that notifies the first individual user if the second individual user is not authorized to receive the portion of media, a second user transmitter/receiver that receives the portion of media and voice message upon authorization of the second individual user, a search mechanism, and a video recording mechanism, an online betting module, and an online food ordering module.

Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

Systems and methods for targeted advertising to specific consumers are disclosed. A system may include a memory storing instructions; and at least one processor configured to execute the instructions to: receive, over a network, consumer data from a client device; identify a plurality of client-provided consumers from the consumer data; obtain a plurality of unique consumer identifiers corresponding to the plurality of client-provided consumers; and identify at least one first overlapping unique consumer identifier by matching at least one of the plurality of client-provided consumers with at least one publisher-provided consumer provided by a first publisher device of a plurality of publisher devices, the first publisher device having a highest priority among the plurality of publisher devices.

Systems and methods for distributing a three dimensional (3D) online advertisement are described. The systems and methods may provide for receiving an uploaded 3D image file, the 3D image file comprising a panoramic photo or panoramic video, converting the 3D image file to a first format configured to be accepted by a first digital advertising platform, generating a display advertisement including the 3D image file, displaying the display advertisement on a device through the first digital advertising platform.

Disclosed techniques include in-vehicle drowsiness analysis using blink-rate. Video of an individual is obtained within a vehicle using an image capture device. The video is analyzed using one or more processors to detect a blink event based on a classifier for a blink that was determined. Using temporal analysis, the blink event is determined by identifying that eyes of the individual are closed for a frame in the video. Using the blink event and one or more other blink events, blink-rate information is determined using the one or more processors. Based on the blink-rate information, a drowsiness metric is calculated using the one or more processors. The vehicle is manipulated based on the drowsiness metric. A blink duration of the individual for the blink event is evaluated. The blink-rate information is compensated. The compensating is based on demographic information for the individual.

An agent provides anonymity to users of business transactions. The agent acts as an interface between the users and businesses accessible. The businesses are identified as trusted, semi-trusted or non-trusted by the agent. The agent enforces terms and conditions of smart contracts agreed upon by the businesses and intended to protect the privacy of the users. A user may purchase goods and/or services from a business without revealing their identity to the business. Advertisements from this or other businesses may or may not reach the user depending on the user's preferences. The agent may collect loyalty points from the businesses on behalf of the users while maintaining the confidentiality of the users. The businesses may collect only data about the agent and a platform that implements the agent without collecting data about the users.