Abstract: Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection.

Abstract: Root-analog dental implants include one or more coronal grooves that are sized, shaped, and/or configured to correspond to a size, shape, and/or position of a bone crest for an extracted tooth a root-analog dental implant is designed and manufactured to replace. The coronal grooves may be configured to engage with bone of an alveolar socket to enhance engagement between the root-analog dental implant and the alveolar socket particularly for the time period following seating the root-analog dental implant within the socket site and prior to osseointegration of the root-analog dental implant with the alveolar socket. This promotes stability of the root-analog dental implant within the socket site immediately following insertion and thereafter, which may decrease movement of the root-analog dental implant within the socket site, decrease a likelihood of foreign material such as bacteria or food entering the socket site, and/or reduce a risk of implant failure and jawbone injury.

Abstract: Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures.

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: 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: 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 and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection.

Abstract: Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics like CPU usage, memory consumption, network traffic, and error rates, and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The cleaned and engineered data is ingested into a data warehouse for storage. Gaps in time series data are filled using influencer factor analysis and forecasting. The complete dataset trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection.

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: Time series anomalies with unusual patterns are found. Data collecting from data inception points starts the invention. Real-time data logging ensures temporal integrity and correct sequence analysis. After extracting important data points, the logged data is analyzed for trends and abnormalities. The data is cleaned to reduce noise and unnecessary information for feature engineering accuracy and dependability. For orderly retrieval, primary data sources receive engineered features. Advanced algorithms detect regularities and abnormalities, including holiday and weekend-influenced trends. Each trend cluster receives a predictive model after data clustering. Serializing these models improves storage and retrieval. After classifying new data points to their trend cluster, appropriate models are loaded for comparison to expected patterns. Alerts and corrective measures are automatically triggered by substantial deviations.

Abstract: Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures.

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: 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 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 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: 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: 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: 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: Exemplary lateral suture anchor fixation systems include a lateral anchor, a lateral anchor inserter, an auxiliary or second anchor, and an auxiliary or second anchor inserter.