Abstract: The system obtains a set of tickets representing customer requests for a customer-support ticketing system. Next, the system produces a set of request vectors by feeding words from each ticket through a model to generate a request vector for the ticket, wherein the set of request vectors is represented as a set of points in a vector space. The system then performs a clustering operation on the set of points to form clusters representing support topics, wherein the clustering operation creates a new point for a new ticket in the vector space when the new ticket is received, and incrementally updates existing clusters to accommodate the new point. Finally, the system presents a user interface to a customer-support agent, wherein the user interface uses the support topics to organize the customer requests, and enables the customer-support agent to perform a customer-support operation in response to a customer request.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber.

Abstract: We have developed a system that automatically suggests macros to help customer-support agents process customer-support tickets in an online customer-support system. During operation, the system receives a customer-support ticket, which is associated with a request from a customer in the customer-support system, wherein the request relates to a product or a service used by the customer. Next, the system converts text from the customer-support ticket into a ticket embedding in a vector space. The system then feeds the ticket embedding into a macro-suggestion model, which correlates ticket embeddings with macros, wherein each of the macros comprises a sequence of commands that performs an operation to facilitate processing of the customer-support ticket. If the macro-suggestion model produces suggested macros, the system presents the suggested macros to a customer-support agent.

Abstract: Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber.

Abstract: 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.

Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber.

Abstract: Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.

Abstract: Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber.

Abstract: A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

Abstract: Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Abstract: Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Abstract: The system obtains a set of tickets representing customer requests for a customer-support ticketing system. Next, the system produces a set of request vectors by feeding words from each ticket through a model to generate a request vector for the ticket, wherein the set of request vectors is represented as a set of points in a vector space. The system then performs a clustering operation on the set of points to form clusters representing support topics, wherein the clustering operation creates a new point for a new ticket in the vector space when the new ticket is received, and incrementally updates existing clusters to accommodate the new point. Finally, the system presents a user interface to a customer-support agent, wherein the user interface uses the support topics to organize the customer requests, and enables the customer-support agent to perform a customer-support operation in response to a customer request.

Abstract: Software downloads to Internet of things (IoT) devices are facilitated over a constrained network. In one embodiment a method comprises monitoring, by a network device comprising a processor, data determined to have been sent to a device for transmission to the device via a radio access network device of a wireless communication network, and determining, by the network device, a type of traffic associated with the data. The method further includes based on a determination that the data comprises firmware and that the type of traffic is of a traffic priority that is lower than a defined traffic priority, applying, by the network device, a low priority transport protocol to the data, wherein the applying comprises associating protocol information with the data representative of the low priority transport protocol.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.