Zoomph, Inc.

Other

Metrostar Systems, Inc.

John William Seaman

Thomas Mathew

Lee Evan Kohn

Ali Reza Manouchehri

Jorge Luis Vasquez

David Woo

Reza Ali Manouchehri

A system includes a processor and a memory device communicatively coupled to the processor. The system also includes a database communicatively coupled to the processor. The database is configured to store a first plurality of prediction sets. Each prediction set is associated with a respective individual within a first population. Each prediction set comprises a plurality of prediction results, and each prediction result corresponds to a selected one of a plurality of features. The processor is configured to receive a request for a distribution value associated with a selected feature and one or more parameters. The distribution value indicate how often the selected feature appears in a second population of individuals, the second population being defined by the one or more parameters.

Database query and data mining in intelligent distributed communication networks

One example method of operation may include identifying a number of features associated with information of one or more entities, accessing a probability distribution store comprising defined numerical ranges as potential possibilities for being paired with the features of the one or more entities, determining first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features, determining second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features, determining a merged probability distribution based on the first probability distributions and the second probability distributions, determining and storing one or more prediction sets based on the merged probability distribution, selecting one or more content items to display on a device interface based on the one or more prediction sets, and displaying the one or more content items the device interface.

Systems, apparatus, and methods for generating prediction sets based on a known set of features

An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Na&#xef;ve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group.

An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Na&#xef;ve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group. For example, an advertisement may be selected and displayed based on the predicted feature.

A distributed telecommunication network including a VPN, a cellular network, a LAN, an Ethernet network, a server, a database, all communicatively coupled to the backbone of the network, a Wi-Fi network, a firewall, and a mobile device, communicatively coupled to the Wi-Fi network and the cellular network, wherein the database is configured to store a profile of a user of the distributed telecommunication network, the profile including a biography of the user, user relationships, a plurality of posts, an activity level of the user, and wherein the server is configured to determine whether the profile belongs to a category using a weighted average of a biography indication related to the biography of the profile and a post indication related to posts and relationship indication related to user relationships.

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Reston, VA, United States of America

John William Seaman