Abstract: Aspects of the disclosure relate to monitoring a computing network to determine data exfiltration. A computing platform may use time-series modeling to determine anomalous network activity with respect to outgoing data. Additional aspects of this disclosure relate to analysis of web activities associated with a user to determine compromised user accounts/devices. The computing platform may use domain categorization to determine if web activity associated with a user is anomalous.

Abstract: Systems, devices, and methods for deploying a graft to a treatment site of a patient. Exemplary systems include a device body and an insertion mechanism. The device body can have a handle and a trigger, and a controller. The insertion mechanism can be in operative association with the device body, and can have an outer sheath, a central shaft disposed at least partially within the outer sheath, an expansion mechanism in operative association with the central shaft, and a graft that can be deployed by the expansion mechanism.

Abstract: Systems, devices, and methods for deploying a graft to a treatment site of a patient. Exemplary systems operate to fix a graft to bone or soft tissue by placing multiple anchors through the graft in different locations, with individual anchors interconnected by sutures fixing the graft to the patient tissue. Exemplary anchor assemblies include an initial anchor with a suture permanently affixed, intermediate anchors through which the running suture can pass freely after placement, and a final anchor which locks the running suture under tension once placed.

Abstract: A computing platform may configure a rules-based state machine to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data. The computing platform may generate, based on the initial telemetry data, an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate, based on the additional telemetry data, an additional telemetry state image. The computing platform may compare a pattern, corresponding to the initial telemetry state image, the additional telemetry state image, and a corresponding transition, to historical patterns to identify a match. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may send, based on the likelihood of failure for the system, preemptive resolution commands causing modification of operations at the system to prevent a predicted failure.

Abstract: A computing platform may train, using historical telemetry state images, an image comparison model to identify matches between telemetry state images. The computing platform may generate a plurality of system alerts corresponding to a period of time. The computing platform may access telemetry data corresponding to the period of time. The computing platform may generate, based on the telemetry data and for a time corresponding to each of the plurality of system alerts, a telemetry state image. The computing platform may input, into the image comparison model, the telemetry state images to identify whether or not any of the plurality of telemetry state images match. Based on detecting a match, the computing platform may consolidate system alerts corresponding to the matching telemetry state images, which may produce a single system alert and may send, to a user device, the single system alert.

Abstract: A computing platform may train a hybrid deep learning model, including a CNN and RNN, to predict system failure for a system based on telemetry state images and transitions between the telemetry state images. The computing platform may receive initial telemetry data, and may generate an initial telemetry state image. The computing platform may receive additional telemetry data, and may generate an additional telemetry state image. The computing platform may classify, using the CNN and based on historical telemetry state images, the initial telemetry state image and the additional telemetry state image. The computing platform may identify, using the RNN and based on the classified telemetry state images and transitions between the classified telemetry state images, a matching pattern. The computing platform may identify, using the identified matching pattern, a likelihood of failure for the system, and may cause modification of operations at the system to prevent a predicted failure.

Abstract: A robotic surgical system and method of selectively binding articulation limits to a surgical instrument includes detecting a proximal or a distal linear movement of the surgical instrument, calculating a distance between a tool center point of the surgical instrument after the proximal or the distal linear movement and a remote center of motion, assigning and binding an articulation limit to the surgical instrument based on the calculated distance when the detected linear movement is distal linear movement, and assigning a non-binding articulation limit to the surgical instrument based on the calculated distance when the detected linear movement is proximal a linear movement.

Abstract: A root portion of a root-analog dental implant may include a core positioned in an approximate center of the root portion of the dental implant. The core may provide mechanical strength and/or support for the dental implant and may include one or more struts. The root-analog dental implant may further include a porous surface positioned on a portion of a vertically oriented exterior surface of the core. The struts may be configured and arranged to provide mechanical strength and/or support for the root-analog dental implant.

Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, partially transferred data associated with an ongoing data transfer from the source to the target system and identify a second set of patterns associated with the partially transferred data. The computing platform may evaluate integrity of the partially transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison and halt the ongoing data transfer based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

Abstract: Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, transferred data associated with a data transfer from the source system to the target system and identify a second set of patterns associated with transferred data. The computing platform may evaluate integrity of the transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison. The computing platform may send one or more notifications based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

Abstract: Systems, devices, and methods for deploying a graft to a treatment site of a patient. Exemplary systems include a device body and an insertion mechanism. The device body can have a handle, a trigger, and a controller. The insertion mechanism can be in operative association with the device body, and can have an outer sheath, a support shaft disposed at least partially within the outer sheath, an expansion mechanism coupled with the support shaft, and a graft in contact with the expansion mechanism.

Abstract: Aspects of the disclosure relate to monitoring a computing network to determine data exfiltration. A computing platform may use time-series modeling to determine anomalous network activity with respect to outgoing data. Additional aspects of this disclosure relate to analysis of web activities associated with a user to determine compromised user accounts/devices. The computing platform may use domain categorization to determine if web activity associated with a user is anomalous.

Abstract: Embodiments of the present invention provide a system for analyzing operational parameters of electronic and software components associated with entity applications to detect anomalies. The system is configured for extracting one or more historical images associated with resiliency status of electronic and software components associated with an entity application, analyzing the one or more historical images to generate a pixel wise average of the one or more historical images, generating similarity scores between the one or more historical images, determining a distribution of the similarity scores, receiving a real-time image associated with a current resiliency status of the electronic and software components associated with the entity application, generating a real-time image similarity score for the real-time image, and comparing the real-time image similarity score with the distribution to detect presence of an anomaly.

Abstract: Systems, computer program products, and methods are described herein for analyzing system health of individual electronic components using image mapping. The method includes receiving a component health image for a component based on an execution of a process. The method also includes comparing the component health image based on the execution of the process to previous component health image(s) for the component based on one or more previous executions of the process The method further includes determining a component health image similarity score based on the comparison of the component health image to the one or more previous component health images for the component. The method still further includes determining a component health action based on the component health image similarity score. The component health action includes causing a transmission of an alert in an instance in which the component health image similarity score is outside of a threshold range.

Abstract: Systems, computer program products, and methods are described herein for analyzing system health of individual electronic components using component relational graphs. The method includes receiving a process request. The process request is a request to execute a process. The method also includes determining one or more components of the system used during the process. The method further includes generating a component knowledge graph for the process. The component knowledge graph includes one or more nodes corresponding to each of one or more components used during the process. The method still further includes determining a component health rating for each of the one or more components used in the process.

Abstract: Systems, computer program products, and methods are described herein for real-time overload detection using image processing analysis. The present disclosure is configured to receive a first set of images associated with a device, wherein the one or more images are associated with a resiliency status of the device; deploy, using a machine learning (ML) subsystem, a trained ML model on the first set of images of the device; determine, using the trained ML model, a change in the resiliency status of the device based on the first set of images; and generate a notification indicating the change in the resiliency status of the device; and transmit control signals configured to cause a first user input device to display the notification.

Abstract: Systems, computer program products, and methods are described herein for performance monitoring using aggregated telemetry. The present disclosure is configured to receive, from the first performance monitoring engine, a first metadata associated with the first resiliency status; receive, from the second performance monitoring engine, a second metadata associated with the second resiliency status; determine, using a machine learning (ML) subsystem, an overall resiliency status of the device based on at least the first metadata, the second metadata, the first resiliency status, and the second resiliency status; determine one or more actions to be executed on the device, wherein the one or more actions are associated with the overall resiliency status; generate a notification indicating the overall resiliency status of the device and the one or more actions associated with the overall resiliency status; and transmit control signals configured to cause a user input device to display the notification.

Abstract: A root portion of a root-analog dental implant may include a core positioned in an approximate center of the root portion of the dental implant. The core may provide mechanical strength and/or support for the dental implant and may include one or more struts. The root-analog dental implant may further include a porous surface positioned on a portion of a vertically oriented exterior surface of the core. The struts may be configured and arranged to provide mechanical strength and/or support for the root-analog dental implant.

Abstract: Aspects of the disclosure relate to monitoring a computing network to determine data exfiltration. A computing platform may use time-series modeling to determine anomalous network activity with respect to outgoing data. Additional aspects of this disclosure relate to analysis of web activities associated with a user to determine compromised user accounts/devices. The computing platform may use domain categorization to determine if web activity associated with a user is anomalous.

Abstract: Systems, devices, and methods for deploying a graft to a treatment site of a patient. Exemplary systems include a device body and an insertion mechanism. The device body can have a handle, a trigger, and a controller. The insertion mechanism can be in operative association with the device body, and can have an outer sheath, a support shaft disposed at least partially within the outer sheath, an expansion mechanism coupled with the support shaft, and a graft in contact with the expansion mechanism.