Landis+gyr Innovations, Inc.

Scientific-atlanta, Inc.

Transnexus, Inc.

Intellisync Corporation

David Decker

Keith Mario Torpy

James Randall Turner

Ruben E Salazar Cardozo

Matt Karlgaard

Jeffrey B Shudark

Matthew Donald Karlgaard

Ruben Salazar

Vishal Kumar

Stephen Anthony Thomas

James P G Dalton

Altan John Stalker

Mark R Murray

Mohit Srinivasan

Kevin Peterson

John V Martinez

Rui J P De Figueiredo

Paul Porter

Eric Allen

Randy Turner

Troy Bartlett

Jay Bredenberg

Keith Henry

Technologies for implementing edge intelligence for utility communication networks are provided. For example, a system includes a mesh network and a utility fog configured to manage the mesh network. The utility fog includes a secure utility system configured for executing a private utility application and a first edge intelligence device configured for executing a first subset of software applications. Each software application is configured to manage endpoints in the mesh network or process data collected by the mesh network. The mesh network includes the endpoints and an edge intelligence device configured for executing a second subset of the software applications that is different from the first subset of software applications.

Dynamic processing distribution for utility communication networks

A system for detecting the topology and phase information of an electrical power distribution system is provided. For example, the system includes a group of meters connected to an electrical power distribution system and can communicate with each other through local network connections such as a mesh network. Each of the meters is configured to generate and transmit voltage data to a correlator of the group of meters. The correlator calculates the correlations between each pair of meters in the group based on the voltage data received and further transmits the calculated correlations to a mapper through the mesh network. The mapper determines the topological or phase relationship between at least the group of meters based on the received correlations.

Topology and phase detection for electrical supply network

A utility meter has a multiprocessor architecture including (i) a microprocessing unit (MPU) for executing multiple software applications and (ii) another processing unit for performing core metrology functions in real time. For instance, the utility meter includes a metrology engine, an MPU, and one or more metrology applications. The metrology engine measures consumption of a resource and generates consumption data based on the consumption of the resource. The metrology engine includes a metrology processor, a dedicated memory, and a real-time operating system run by the metrology processor to enable the metrology engine to run time-critical metrology functions in real time. The MPU is coupled to the metrology processor and includes one or more processor cores. The MPU runs the one or more metrology applications over a primary operating system of the MPU, and the one or more metrology applications utilize the consumption data.

Multiprocessor utility meter featuring a metrology processor coupled to an application processor

A method for responding to a voice activated request includes receiving a speech input request from a smart speaker requesting energy management data associated with energy consumption at a premises of the smart speaker. The method also includes generating a voice service request including a first query for a first data source. The first query includes a request for the energy management data. Additionally, the method includes communicating the first query to the first data source and receiving a first response to the first query from the first data source. Further, the method includes generating an audible speech output in response to the speech input request based on the first response to the first query and transmitting the audible speech output to the smart speaker. The smart speaker audibly transmits the audible speech output.

Voice-activated energy management system

A method described herein is performed by a utility meter lacking an integrated display. The method includes measuring consumption of a resource and, further, generating consumption data based on measuring the consumption of the resource. Via a communication device, the utility meter wirelessly connects directly to an external device having a display. The utility meter verifies that the external device is authorized to access the utility meter. The utility meter transmits the consumption data to the external device to utilize the display of the external device as an interface to the utility meter. The utility meter receives an instruction via manual entry at the display of the external device, and the utility meter executes the instruction.

Utility meter supporting a remote display

A service node performs computational tasks or other operations on behalf of a meter or other network device. A meter may collect data regarding electric parameters associated with an electric distribution network. When analysis of the collected data is required, the meter may determine that it requires the resources of a service node. The meter may send a request to a directory node to identify a service node or it may send a request seeking a service node. The service node performs the analysis and sends the results of the analysis back to the meter. The service nodes supports the dynamic distributed processing of analytic services.

Dynamically distributing processing among nodes in a wireless mesh network

An energy allocation system may include a group of agents, each agent corresponding to a device requesting an amount of energy. The energy allocation system may perform a comparison, such as a hybridized comparison, between a selected agent and each other agent included in the group. Based on the outcome of each comparison for the selected agent, an aggregated outcome for the selected agent is determined. The aggregated outcome for the selected agent is compared to a threshold for the energy allocation system. Based on the comparison of the aggregated outcome to the threshold, the selected agent either receives the requested amount of energy or receives an instruction to enter a low-power state.

Energy consumption management based on game theoretical device prioritization

An IP packet may include an IP header and one or more optional IP extension headers. The packet may contain non-IP protocol data in one of the IP extension headers. An application that uses non-IP protocol data may use the non-IP protocol data from the IP extension header. This allows an application designed for a non-IP protocol stack to operate on a device with an IP protocol stack with minimal modification. The non-IP protocol data may alternatively support non-IP routing options within a network or sub-network.

Method and system for using extension headers to support protocol stack migration

An IP packet may include an IP header and one or more optional IP extension headers. The packet may contain non-IP protocol data in one of the IP extension headers. An application that uses non-IP protocol data may use the non-IP protocol data from the IP extension header. This allows an application designed for a non-IP protocol stack to operate on a device with an IP protocol stack with minimal modification. The non-IP protocol data may support routing options within a network or sub-network.

Disclosed are systems and methods for reserve allocation of event data. A request event is received. The request event is associated with memory storing request event data. Memory for response event data is allocated from a first pool. The response event data is associated with a response, and the response is associated with the request. Upon failure of the allocation of memory for the response event data, memory is obtained for out-of-memory response event data. The out-of-memory response event data is associated with the response event. The request event is completed using the response event.

System and method for handling memory allocation failures through reserve allocation of event data

Predicting the quality of a communication carried via a distributed computer network, such as the global Internet, before the initiation of that communication between a pair of endpoints. A prediction system comprises software-implemented processes that can be implemented within an Internet gateway or in an external system that provides prediction results to an Internet gateway. A first process accepts historical data and current environment data and, in response, generates a prediction of objective performance characteristics. A second process accepts the objective performance characteristics and, in response, generates an estimate of the expected quality of communications with an end user. This user quality estimate can be used to support a determination of whether to proceed with the communication or to rely upon an alternative to computer-network supported communications.

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Atlanta, GA, United States of America

David Decker