Abstract: One example method of operation may include receiving, at a server, one or more communications from one or more drones, identifying, via the server, the one or more drones are active in a predefined geographical area, authorizing, via the server, the one or more drones to receive one or more assignments, and performing, via the server, the one or more assignments to the one or more drones to perform one or more operations, and the operations assigned to the one or more drones are based on one or more roles and permissions assigned to the one or more drones.

Abstract: 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

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: Embodiments described herein are directed systems and methods that include the actions of receiving sensor data from a sensor configured to monitor a resource and analyzing the sensor data to identify one or more determined activities of the resource. A determined activity is selected, based on criteria, for a collection of additional information, and a query is generated for the collection of the additional information to reduce an error potential of the determined activity. The query is transmitted and results are received. Based on the results, the determined activity is updated, then stored with a timestamp as an activity data object in an activity data store. A record is generated that comprises the determined activity.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system includes a plurality of large screen display devices located at a venue, and a storage adapted to store one or more items of social media content having respective content tags. The system also includes one or more processors adapted to select a plurality of items of social media content having content tags matching one or more predetermined criteria, cause first items selected from the plurality of items of social media content to be displayed, during a live event occurring at the venue, on each of the plurality of large screen display devices, select a second item having a particular content tag from the first plurality of items of social media content, identify an advertisement image having an advertisement campaign tag that matches the particular content tag of the second item, and cause the second item and the advertisement image to be displayed, at a selected time during the live event, simultaneously on each of the plurality of large screen display devices.

Abstract: 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.

Abstract: Methods, systems, and apparatus are disclosed which include filtering social media data for content resources that match one or more search terms, generating a feed based on the filtering, associating a content resource tag with each content resource, associating the feed with a first section of a layout of a content display, associating an advertisement campaign with a second section of the layout, providing the layout to be displayed on a display device, providing the content resources from the feed to be displayed in the first section of the layout, determining that one of the content resources currently displayed in the first section of the layout is associated with a content resource tag that matches one of the campaign tags of the advertisement campaign; and providing the advertisement image to be displayed in the second section of the layout.