Abstract: Techniques for analyzing audiovisual content, such as streaming content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of audiovisual content, using a video decoder to decode compressed model weights of at least one trained model, using the at least one trained model with the decoded weights to analyze the frame and extract content based on the analysis, using the extracted content to make a determination that the audiovisual content comprises a category of content, and causing actionable information to be transmitted to a client device of a user in response to the determination that the audiovisual content comprises the category of content.

Abstract: Techniques for analyzing audiovisual content, such as streaming content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of audiovisual content, using a video decoder to decode compressed model weights of at least one trained model, using the at least one trained model with the decoded weights to analyze the frame and extract content based on the analysis, using the extracted content to make a determination that the audiovisual content comprises a category of content, and causing actionable information to be transmitted to a client device of a user in response to the determination that the audiovisual content comprises the category of content.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A device may receive technician data identifying home geographical locations of technicians and base station data identifying geographical locations of base stations, and may calculate travel distances and travel times between each of the technicians and each of the base stations based on the technician data, the base station data, and map data. The device may calculate technician distances between the technicians based on the technician data, and may determine the base stations to assign to each of the technicians, to generate groups of assigned base stations, based on a least cost model. The device may determine technician clusters based on processing the groups of assigned base stations with a clustering model, and may generate optimized technician assignments and modified groups of assigned base stations based on utilizing a balancing model. The device may perform one or more actions based on the modified groups of assigned base stations.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A community mapping platform may receive an image that depicts a community layout of a community and may process, using a computer vision model, the image to identify a unit, of the community, that is depicted in the image (e.g., based on identifying a text string and/or a polygon in the image). The community mapping platform may determine sets of community geographical coordinates for a set of reference locations of the community and may map the sets of community geographical coordinates to corresponding reference pixel locations of the image. The community mapping platform may determine, using a geographical information system, unit geographical coordinates of the unit based on the reference pixel locations and may perform an action associated with the unit geographical coordinates.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A computer device may include a processor configured to obtain key performance indicator (KPI) values for KPI parameters associated with at least one device and compute a set of historical statistical values for the obtained KPI values associated with the network device. The processor may be further configured to provide the KPI values and the computed set of historical statistical values to an anomaly detection model to identify potential anomalies; filter the identified potential anomalies based on a designated desirable behavior for a particular KPI parameter to identify at least one anomaly; and send an alert that includes information identifying the at least one anomaly to a management system or a repair system associated with the device. The computer device may further determine a root cause KPI parameter for the identified at least one anomaly and include information identifying the determined root cause KPI parameter in the alert.

Abstract: A community mapping platform may receive an image that depicts a community layout of a community and may process, using a computer vision model, the image to identify a unit, of the community, that is depicted in the image (e.g., based on identifying a text string and/or a polygon in the image). The community mapping platform may determine sets of community geographical coordinates for a set of reference locations of the community and may map the sets of community geographical coordinates to corresponding reference pixel locations of the image. The community mapping platform may determine, using a geographical information system, unit geographical coordinates of the unit based on the reference pixel locations and may perform an action associated with the unit geographical coordinates.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.