A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

Examples described herein may include a surgical power device to balance power needs. The surgical power device may determine a first power expectation associated with a first surgical module, a second power expectation associated with a second surgical module, and an available amount of operating room power within an operating room; determine a power budget for the first surgical module and the second surgical module based on the available amount of operating room power, the first power expectation, and the second power expectation; and control the power distribution unit, based on the power budget, to a set a first portion and a second portion of the operating room power supplied to the power distribution unit.

A computing system may obtain, from surgical hub(s) and/or other system(s), collections of unredacted data associated with different surgical procedures. The computing system, the surgical hub(s), and other systems may be located on a local data network. The local data network may be within a boundary protected by health insurance portability and accountability act (HIPAA) data rules. The computing system may train machine learning model(s) based on the unredacted data. The computing system may generate information that optimizes the clinical outcome and cost effectiveness of future surgical procedure(s) based on the machine learning model(s). The computing system may send generated information to the surgical hub(s) and/or other system(s). The computing system may be in communication with a remote cloud computing system. The computing system may send the generated information to the remote cloud computing system.

Systems, methods, and instrumentalities are disclosed for data processing and creating a record of the processing for archival in metadata associated with the results of the processing. The processing may include transformations of the data. Transforming the data may generate transformed data. The processes performed may be archived, for example, in metadata associated with the transformed data. The metadata may be annotated with information associated with previous transforms performed on the transformed data. The metadata may be stored with the transformed data.

A technique involves modular storage of network service plan components and provisioning of same. A subset of the capabilities of a service design system can be granted to a sandbox system to enable customization of service plan offerings or other controls.

Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Various embodiments are disclosed for a services policy communication system and method. In some embodiments, a communications device stores a set of device credentials for activating the communications device for a service on a network; and sends an access request to the network, the access request including the set of device credentials.

A central controller is configured to obtain a scan of a quick response (QR) code affixed to an internet-of-things (IoT) device. The central controller decodes the QR code to extract various operating parameters associated with the IoT device. The central controller then provisions a device controller for coordinating operation of the IoT device. The central controller configures the device controller based on the operating parameters, thereby allowing the device controller to coordinate operations of the IoT device in a device-specific manner. The central controller may then install the device controller on the IoT device, or cause the device controller to coordinate IoT device operations across a network. With this approach, a technician is no longer required to manually obtain IoT device operating parameters or input those parameters to a central controller, thereby streamlining the IoT device installation process.

Various embodiments of the present disclosure are directed to automatic network architecture configuration maintenance. A network architecture for a particular organization, user, or other entity, may include various networked devices, any of which may be vulnerable to one or more cyberattacks due to outdated software, hardware, and/or firmware configurations. Embodiments include apparatuses, computer program products, and methods for retrieving an updated device configurations data object, identifying a vulnerable networked device set based at least in part on the updated device configurations data object and a detected networked device set, and generating a device cyber risk score data object set that may be output and/or otherwise provided to one or more systems, devices, or the like. Some example embodiments further include identifying update recommendation(s), generating device cyber risk priority data object(s), and/or providing various combinations of such data for rendering to one or more displays associated with a user.

Intelligent learning and management of networked architectures is disclosed. A network architecture can be mapped to identify a set of interconnected hardware and software elements that comprise the network architecture. Data sources associated with the set of interconnected hardware and software elements can be identified and employed to compile data associated with the elements. The data can be utilized to determine an action to address potential negative effects of a change to the network architecture such as an update or patch. In one instance, the action corresponds to a reconfiguration of at least one of the set of interconnected hardware and software elements. Further, machine learning can be employed to determine a particular configuration. Once determined the action can be implemented on the network architecture.