At&t Intellectual Property I, LP

Vahid Tavassoli

Mohammad Nikain

Jiyuan Wang

Jeff Johnson

Abdelmajid Belhareth

Allison Whittaker

Keith Dorking

Jayant V Ragde

Daniel Connolly

Amee Fontanetta

The technologies described herein are generally directed to the autonomous onsite remediation of adverse conditions for network infrastructure in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include detecting a condition of a component of network equipment at a site that has a likelihood of indicating a defined adverse event that has at least a threshold likelihood of occurring. The method can further include, in response to detecting the condition, facilitating generating a graphical image of the component. Further, the method can include, based on information determined from the graphical image, remediating the condition.

Autonomous onsite remediation of adverse conditions for network infrastructure in a fifth generation (5G) network or other next generation wireless communication system

The technologies described herein are generally directed to selecting network routes based on aggregating models that can predict routing performance in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include communicating, to second routing equipment, a first model describing a delay predicted to be caused to a future communication by the future communication being transited via the first routing equipment. The method can further include receiving, from the second routing equipment, a current communication for transit via the first routing equipment to destination equipment, wherein the first routing equipment was selected by the second routing equipment based on the first model, and second models, other than the first model, describing respective predicted delays from other routing equipment other than the first routing and second routing equipment.

Selecting network routes based on aggregating models that predict node routing performance

The technologies described herein are generally directed to monitoring the configuration of an inventory of devices using captured images of a site. For example, a method described herein can include identifying a group of equipment installed at locations within a site. The method can further include, based on an image captured at the site by image capture equipment, detecting configuration activity at the site. Further, the method includes, based on analysis of the image, associating, by the tracking equipment, the configuration activity with equipment of the group of equipment.

Monitoring configuration of an inventory of devices using captured images of the devices

The technologies described herein are generally directed to deploying network equipment. For example, a method described herein can include facilitating receiving a first request to deploy first network equipment at a deployment location, with the first request including a first deployment parameter for the first network equipment. The method further includes, based on the first network equipment and the first deployment parameter, generating a deployment template. Further, the method can facilitate receiving a second request to deploy second network equipment at the deployment location, with the second request including a second deployment parameter for the second network equipment. The method can further include, based on the deployment template and the second deployment parameter, deploying the second network equipment.

Generating and utilizing templates and logical paths to deploy network equipment

Machine learning (ML) can be used to gather data on prospective customers from publicly available online sources. The ML can provide data on a likelihood of near future, customers and subscribers and a list of people with low interest in the company's products. The ML system can be recursively updated as new public data becomes available online. Thus, the ML system can be used to generate targeted advertising and root cause analysis based on a relationship between people's statuses and their respective interests in certain products and/or services.

Facilitation of artificial intelligence predictions of telecommunications customers

Methods, systems and computer readable media for self-configuration of virtual network functions based on user intent are provided. In a network, a destination with an initial offering having a set of initial offering attributes is provided. A first virtual network function is connected to the destination that is capable of advertising the initial offering. A source having a set of need attributes defined by a user is also provided. The initial offering is advertised, and the source is connected to the first virtual network function. A second virtual network function with an added value is added and a modified offering having a set of modified attributes based on the set of initial offering attributes and the added value is created. The initial offering and the modified offering is advertised to the source. The best offering from the initial offering and modified offering is selected based on the first set of need attributes, the first set of modified offering attributes and the set of initial offering attributes. The first source is then connected to the first best offering.

Systems and methods for intent-based self configuration of virtual network functions

The present disclosure provides methods, computer-readable media, and devices for making content recommendations in a manner that protects private user data. In one example, a method includes collecting content consumption history data for a specific user of a telecommunications network, inserting random noise into the content consumption history data to produce anonymized data, sending the anonymized data over a network to a centralized device, receiving correlation data from the centralized device, wherein the correlation data indicates correlations among a plurality of items of content that is available for consumption via the telecommunications network, and predicting a likelihood that the specific user would be interested in a particular item of the plurality of items of content, wherein the predicting is based on the content consumption history data and on the correlation data. The steps may be carried out by a processor of a user endpoint device.

Growing community of inventors

Alpharetta, GA, United States of America

Vahid Tavassoli