A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.

A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

A system and method for triggering on platform usage can include at a platform, receiving and storing a trigger configuration of an account; operating a platform comprising internally executing a process on behalf of an account and publishing at least one event when executing the process; at the platform, incrementing a counter in response to the at least one event and if the stored trigger configuration species a usage key associated with a category of counted events of the at least one event; monitoring counters in a context of an associated trigger; and processing the trigger upon the counter satisfying condition of an associated trigger.

A system and method for triggering on platform usage can include at a platform, receiving and storing a trigger configuration of an account; operating a platform comprising internally executing a process on behalf of an account and publishing at least one event when executing the process; at the platform, incrementing a counter in response to the at least one event and if the stored trigger configuration species a usage key associated with a category of counted events of the at least one event; monitoring counters in a context of an associated trigger; and processing the trigger upon the counter satisfying condition of an associated trigger.

Various medical devices and related systems, including robotic and/or in vivo medical devices, and various robotic surgical devices for in vivo medical procedures. Included herein, for example, is a robotic surgical system having a support beam positionable through an incision, and a robotic device having a device body, first and second rotating shoulder components coupled to the device body, and first and second robotic arms coupled to the first and second shoulder components, respectively.

Various medical devices and related systems, including robotic and/or in vivo medical devices, and various robotic surgical devices for in vivo medical procedures. Included herein, for example, is a robotic surgical system having a support beam positionable through an incision, and a robotic device having a device body, first and second rotating shoulder components coupled to the device body, and first and second robotic arms coupled to the first and second shoulder components, respectively.

As described herein, a machine learning system, method, and computer program provide marketing segmentation of residential spaces. In use, network usage data is collected from each residential network router of a plurality of residential network routers operating in a different residential space of a plurality of residential spaces. Additionally, the network usage data is processed by a machine learning algorithm to segment the plurality of residential spaces into a plurality of segments. Further, the plurality of segments are output.

As described herein, a machine learning system, method, and computer program provide marketing segmentation of residential spaces. In use, network usage data is collected from each residential network router of a plurality of residential network routers operating in a different residential space of a plurality of residential spaces. Additionally, the network usage data is processed by a machine learning algorithm to segment the plurality of residential spaces into a plurality of segments. Further, the plurality of segments are output.