AT&T Intellectual Property I, LP

At&t Delaware Intellectual Property Corporation, Inc.

Bellsouth Intellectual Property Corporation

Worcester Technologies LLC

At&t Mobility II LLC

Georgia Tech Research Corporation

Intellectual Ventures II LLC

Jeffrey Aaron

James Fan

John Ruckart

Edgar Vaughan Shrum, Jr

Jun-Gang Alin

Robert C Streijl

Venson Shaw

Cheng P Liu

Thomas Arnold Anschutz

James Carlton Bedingfield, Sr

Larry Woodring

Jennifer K Lam

Randolph Wohlert

Cagatay Buyukkoc

Mark Stockert

Sanjay Agraharam

Steven R Polston

Michelle Martens

Arun Gupta

Rittwik Jana

Jason H Collins

Manoop Talasila

Robert E Wright

Leslie Westhaver Pickering

Ojus Parikh

A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.

Data pipeline controller

A method includes creating one or more first policy shims to be applied to a ML/AI module, applying the one or more first policy shims to an input or an output of the ML/AI module and executing the ML/AI module on a data set in response to the applying step. The one or more first policy shims includes an input policy shim and an output policy shim and the applying step includes applying the input policy shim to the data set prior to the executing step and applying the output policy shim to an output of the executing step.

Public policy rule enhancement of machine learning/artificial intelligence solutions

A method includes defining a plurality of variables to modify in a control loop; collecting first data using a first variable of the plurality of variables while executing the control loop, generating a first result based on the collecting first data step, substituting a second variable of the plurality of variables for the first variable, collecting second data using the second variable while executing the control loop, generating a second result based on the collecting second data step, comparing the first result and the second result; and taking an action based on the comparing step.

Automatic control loop decision variation

A processing system including at least one processor may obtain a first request for delivery of a first data set to a first destination, map the first request to a first information model, obtain a second request for delivery of a second data set to a second destination, map the second request to a second information model, and identify that a portion of data is part of both data sets. The processing system may next determine a plan for configuring data pipeline components for delivering the first data set to the first destination and the second data set to the second destination, the plan comprising: a combination of the first information model and the second information model, and at least one modification to the combination. The processing system may then configure the data pipeline components in accordance with the plan.

Data blending for multiple data pipelines

Concepts and technologies disclosed herein are directed to automated control loop searching ('ACLS'). According to one aspect disclosed herein, an ACLS system can create a search model that provides high-level information regarding what the ACLS system should search for when a search pattern is detected within data that is output from execution of a control loop. The ACLS system can activate a control loop system that executes the control loop to yield the data as output. The ACLS system can detect the search pattern within data, and in response, the ACLS system can execute, based upon the search model, a search of the data. The ACLS system can collect search results of the search and select additional data from the search results.

Automatic control loop searching

Fast-aiding radio access network intelligent controllers (RICs) can assist other RICs that are experiencing performance issues. The system can take into account the availability of other RICs that can aid the RIC needing assistance. Therefore, multiple RICs cooperatively working together can generate synergies, and allow differentiated RICs to operate in both predefined and/or dynamically create RIC groups. Once a RIC that needs assistance is identified, other RICs can offload functionalities that are less important than the functionality of the RIC that is experiencing the performance issues. After the assisting RIC has offloaded its functionalities, it can then devote resources to the RIC that needs the assistance.

Facilitation of fast aiding radio access network intelligent controllers for 5G or other next generation network

In one embodiment, a method includes monitoring, by a control loop including a processor and a memory, a first environment. The control loop includes one or more predetermined control loop parameters. The method also includes receiving, by the control loop and in response to monitoring the first environment, first data from the first environment and receiving, by the control loop, information from an adaptation control loop. The method also includes determining, by the control loop, to automatically adjust at least one of the one or more predetermined control loop parameters based at least in part on the information received from the adaptation control loop and automatically adjusting, by the control loop, the one or more predetermined control loop parameters. The method further includes determining, by the control loop, to initiate an action based on the first data collected from the first environment and the one or more adjusted control loop parameters.

Self-adjusting control loop

Concepts and technologies disclosed herein are directed to automated control loop grading and data labeling ('ACLGDL'). An ACLGDL system analyzes results of an execution, by a control loop system, of a control loop. The ACLGDL system can grade the results. The ACLGDL system also can instruct, based at least in part upon the grade of the results of the execution, the control loop system to collect additional data. The ACLGDL system can label the additional data for use by an output system. The ACLGDL system can establish plurality of policies including a grading-analysis policy, a grading-results policy, a labeling-collection policy, a labeling policy, a publishing policy, and a notification policy. The ACLGDL system can publish the data labeled in accordance with the labeling policy based, at least in part, upon the publishing policy. The ACLGDL system can notify the output system based, at least in part, upon the notification policy.

Automatic control loop grading and data labeling

In one example, a method and apparatus for optimizing a software defined network configuration are disclosed. In one example, the method determines a first network relative performance parameter for a current configuration of a network, based on respective weighting profiles associated with services for which the network carries data. The method then determines a second network relative performance parameter for a proposed configuration of the network, based on the respective weighting profiles associated with the services for which the network carries data. The proposed configuration is implemented in the network when the second network relative performance parameter is greater than the first network relative performance parameter.

Method and apparatus for optimizing a software defined network configuration

Concepts and technologies disclosed herein are directed to escalation of feedback instances. In one aspect disclosed herein, a feedback instance escalation resolver ('FIER') system can receive a feedback instance escalation request from an original feedback instance. The feedback instance escalation request can identify a deficiency in the original feedback instance. The FIER system can examine the feedback instance escalation request and an objective of the original feedback instance to identify a new objective of an escalated feedback instance. The FIER system can create a definition for the escalated feedback instance to satisfy the objective and can map the definition for the escalated feedback instance to an existing feedback instance model. The FIER system can generate a feedback instance escalation realization request directed to a feedback instance orchestrator and controller ('FIOC') system. The feedback instance escalation realization request can instruct the FIOC system to realize the escalated feedback instance.

Escalation of feedback instances

Provided are exemplary embodiments including a method for creating and using a personal encounter history using a communication device. The method involves the communication device receiving the transmission of a pseudo identifier from a proximal communication device where the pseudo identifier is associated with the user of the proximal communication device. Once received, the method continues with the wireless communication device requesting and receiving the actual identification of the user of the proximal communication device that is correlated with the pseudo identifier. The communication device includes a transceiver capable of communicating wirelessly with a mobile telecommunications network, a memory device and a processor. To ensure privacy, the processor is capable of receiving a pseudo identifier from a proximate communication device and then requesting an actual identification correlated with the pseudo identifier of the proximate communication device.

Device, system and method for recording personal encounter history

Concepts and technologies disclosed herein are directed to a pressure-based input method for user devices. According to one aspect disclosed herein, a user device can receive an output from a pressure sensor. The output can include parameters associated with a pressure applied to the pressure sensor by a user. The user device can analyze the output to determine a command to be executed by the user device. The user device can execute the command.

Pressure-based input method for user devices

Methods, systems, and products monitor athletic performance. Location information is acquired that indicates a device is in movement. A level of difficulty associated with the location information is retrieved and associated to a distance traversed during the movement. The distance traversed and the level of difficulty are stored in a database.

Methods, systems, and products for monitoring athletic performance

Concepts and technologies disclosed herein are directed to the dynamic creation and management of ephemeral coordinated feedback instances. In accordance with one aspect disclosed herein, a system can receive a feedback instance creation request. The feedback instance creation request can be received from a policy engine in response to the policy engine attempting to satisfy a policy request. The system can examine the feedback instance creation request to determine an objective to be met by a new feedback instance model. The system can build a specification for the new feedback instance model. The specification can be built in accordance with a feedback instance building policy. The system can create the new feedback instance model in accordance with the specification. The system can store the new feedback instance model and a unique identifier associated with the new feedback instance model in a feedback instance model repository.

Growing community of inventors

Atlanta, GA, United States of America

Jeffrey Aaron