Sprint Communications Comapny L.p.

Kyndryl, Inc.

International Business Machines Corporation

Verizon Patent and Licensing Inc.

Sprint Spectrum L.p.

Sprint Communication Cômpany L.p.

Seng Chai Gan

Shikhar Kwatra

Aaron Paul Hinkle

Adam Lee Griffin

Michael Francis Fiumano

Khoi Dinh Nguyen

Timothy Hugh Pearson

Gregg Talewsky

HuyAnh Dinh Ngo

Andrew F Patka

Henry Burford

Craig M Trim

Vijay Ekambaram

Randy Allan Rendahl

Srinivas Babu Tummalapenta

Timothy E Coyle

Steven Kenneth Guthrie

Michael Treadway

Eric C English

Danmei Wu

Adeel Ahmed Siddiqui

Charles Kenneth Flack

Muhammad Ebad Ullah

Gandhi Sivakumar

Vinod A Valecha

Badri Prasad Subramanyan

Saravanan Sadacharam

Indervir Singh Banipal

Anil Bindu Lingambudi

Abhishek Malvankar

Saurabh Tahiliani

Subham Biswas

Lloyd Alan Treinish

Sankar Subramanian

Komminist Weldemariam

Cheryl Marie Nichols

Charles Aaron Robeck

Michael Nicholas Virnoche

Iranna Dharmaraya Ankad

John E Anderson

Michael J Sullivan

Jason Jugar

John C Burlison, Jr

Jaime Lugo

Thomas Joseph Eunice

Ronald Carloni

Rajendra K Damle

Thomas L Kelley

Reiko Miller

Stanley Bryan Hardter

Vikram Tiwari

Christopher A Graffeo

Anand V Bhatia

Arun Cherla

Alex Redmon, Iii

John David Mandra

Al Ebnereza

Raghavendra Jayaram

John S McCormick

A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an device detection and network slice assignment service. The device detection and network slice assignment service may include providing a subnetwork slice connection between a fixed wireless access device and a fifth generation (5G) radio access network device to an end device that is not 5G compatible. The fixed wireless access device may detect the end device based on an identifier included in a network assignment request. The fixed wireless access device may assign a network address, which may be associated with the network slice assignment service, to the end device. The fixed wireless access device may route traffic to and from the end device via the subnetwork slice connection.

Device detection and network slice assignment service

Embodiments are directed to techniques for secure network connectivity. The techniques including a system having a credential server storing a Personal-Public (PP) Service Set. Identifier (SSID) profile configured according to registration information provided from a personal computing device. The system further including a Wireless Access Point (WAP) communicatively coupled to the credential server and configured to implement a PP SSID connection using the PP SSID profile to create a single-device, single-use, password-protected, unadvertised, and encrypted networking channel between the personal computing device and the Internet.

Establishing a personal-public service set identifier connection between a personal device and the internet

One or more computing devices, systems, and/or methods for adaptive API call sequence detection are provided. A series of API calls and gap times between API calls of the series of API calls are recorded. The API calls are received and processed by a production system. The API calls are assigned into API call sequences. An end of an API call sequence is detected based upon a minimum response time and the gap times between the API calls. The API call sequences are utilized to simulate execution of the production system. A configuration is generated and applied to the production system based upon a result of the simulation.

Adaptive API call sequence detection

A management system may receive base station information regarding 5G base stations identified by a fixed wireless access (FWA) device. The base station information may identify the 5G base stations and sectors of the 5G base stations. The management system may provide a request for a quantity of devices connected to each of the sectors. The management system may receive device information indicating the quantity of devices connected to each sector of the sectors of the 5G base stations. The management system may determine loads of the 5G base stations based on the device information. The management system may provide load information regarding the loads of the 5G base stations. The load information may be provided to cause a 4G base station to determine the loads of the 5G base stations prior to causing a 5G base station to establish a connection with the FWA device.

Systems and methods for determining loads of base stations for connection with fixed wireless access devices

A device may include a processor configured to receive a report from a fixed wireless access device indicating that no anchoring relationship exists between a Fourth Generation (4G) base station and a Fifth Generation (5G) base station to which the fixed wireless access device is attempting to connect. The device may be further configured to instruct a network management system to create the anchoring relationship between the 4G base station and the 5G base station; and update an anchoring relationships database with information relating to the created anchoring relationship, wherein the anchoring relationships database stores information relating to anchoring relationships between base stations in a radio access network.

Systems and methods for anchoring relationship management between base stations

A mobile gateway may provide access to one or more networks based on a variety of configurable parameters, constraints, etc. A Mobile Gateway Management System ('MGMS') may maintain different sets of rules, parameters, etc. associated with multiple different mobile gateways, such that a network may be able to determine whether to provide access to a particular mobile gateway based on location parameters, temporal parameters, account parameters, or other parameters. When access is granted to the network based on the parameters, the mobile gateway may serve as an interface between one or more User Equipment (“UEs”) and the network.

Systems and methods for dynamically providing network access via a mobile access gateway

Configuration and dynamic profiling of storage class memory (SCM) devices is provided. Information is retrieved that includes historical SCM device configurations, historical SCM device utilization, functional and non-functional properties of a plurality of SCM devices on a host node, current real time utilization of the plurality of SCM devices by an application workload of a customer running on the host node, and relationships between the plurality of SCM devices, needs of the customer, and resource capabilities and real time resource utilization on the host node. A configuration of each respective SCM device is determined based on retrieved information and an artificial intelligence-predicted SCM device future utilization trajectory of the customer. Each respective SCM device is dynamically configured with a set of SCM device partitions according to a corresponding SCM device profile based on the determined configuration of each respective SCM device of the plurality of SCM devices.

Dynamic profiling of storage class memory (SCM) based on monitored SCM utilization and predicted future SCM utilization

A system described herein may provide a technique for the assignment of Centralized Units ('CUs') to Distributed Units ('DUs') in a radio access network ('RAN') that includes a distributed or hierarchical arrangement of network infrastructure equipment. Different groups of DUs may be modeled based on usage or traffic patterns, and complementary groups of DUs may be identified based on measures of usage that may vary with time. For example, one model associated with one group of DUs may experience relatively heavy usage during morning hours and light usage during evening hours, and another model associated with a complementary group of DUs may experience relatively light usage during morning hours and heavy usage during evening hours.

Systems and methods for centralized unit load balancing in a radio access network

A mobile gateway may provide access to one or more networks based on a variety of configurable parameters, constraints, etc. A Mobile Gateway Management System ('MGMS') may maintain different sets of rules, parameters, etc. associated with multiple different mobile gateways, such that a network may be able to determine whether to provide access to a particular mobile gateway based on location parameters, temporal parameters, account parameters, or other parameters. When access is granted to the network based on the parameters, the mobile gateway may serve as an interface between one or more User Equipment ('UEs') and the network.

In an approach for precipitation detection, a processor trains a first machine learning model for detecting precipitation in a region using a first training set of data including a plurality of historical data from a plurality of mobile devices collected in the region and a plurality of quantitative precipitation estimation data. A processor trains a second machine learning model for detecting a location of a mobile device in the region using a second training set of data including both historical indoor and outdoor data from the plurality of mobile devices collected. A processor receives a current data from the mobile device. A processor determines whether the mobile device is located indoor or outdoor based on the current data. A processor compares the current data against a threshold set in the first machine learning model to indicate precipitation. A processor determines whether the current data exceeds the threshold.

Precipitation detection using mobile devices

An approach is provided in which the approach converts a set of learning content into a teaching content vector representation. The approach determines a learning preference mode of a user based on a set of user responses responding to a set of learning type segments, and computes an environmental context vector representation of a physical environment surrounding the user. The approach conducts a conversation with the user based on the learning preference mode, the environmental context vector representation, and the teaching content vector representation.

Adaptive learning in smart products based on context and learner preference modes

Embodiments are directed to techniques for secure network connectivity. The techniques including a system having a credential server storing a Personal-Public (PP) Service Set Identifier (SSID) profile configured according to registration information provided from a personal computing device. The system further including a Wireless Access Point (WAP) communicatively coupled to the credential server and configured to implement a PP SSID connection using the PP SSID profile to create a single-device, single-use, password-protected, unadvertised, and encrypted networking channel between the personal computing device and the Internet.

Personal-public service set identifiers connection implemented by a WAP

A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Cognitive processing resource allocation

In an approach for optimally grouping containers, a processor passively monitors a set of parameters for a set of containers within a network. A processor records the set of parameters for each container of the set of containers. A processor deploys a k-nearest neighbor neural network (KNN) to determine a first set of groupings of the set of containers based on the set of parameters. A processor simulates the network having grouped containers based on the first set of groupings of the set of containers output by the KNN. A processor simulates an action on the set of containers. A processor updates a reward function based on the action. A processor determines whether a maximum value of the reward function is reached.

Dynamic container grouping

A method, system, and computer program product for malicious virtual machine detection is provided. The method passes network traffic from a set of virtual machines to a set of network resources. The network traffic passes through a network monitor. The network monitor monitors a set of traffic characteristics for the network traffic. A change in a network traffic characteristic is identified. The change results in a modified traffic characteristic. The method identifies a first virtual machine of the set of virtual machines. The first virtual machine is associated with the modified traffic characteristics. An alert is then generated which identifies the first virtual machine and the modified traffic characteristic.

Malicious virtual machine detection

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Ashburn, VA, United States of America

Seng Chai Gan