28 results about "Alert type" patented technology

A smart home monitoring device is configured to plug into an electrical outlet and includes a multi-sensor array, excluding audio and video sensors, for monitoring environmental conditions, behavioral patterns, and odor-related indicators within a monitored environment. The device includes Bluetooth Low Energy communication with angle-of-arrival capability to enable sub-meter asset tracking, and supports home automation control through programmable automation frameworks such as IFTTT or Node-RED to initiate safety responses. The system further provides multi-tiered burglar detection through correlated event analysis across multiple sensor inputs, and generates health-related risk indicators based on detected patterns. Visual indicators, including multi-color LED illumination and blinking patterns, are used to communicate alert types and severity levels. The system enables non-intrusive monitoring while supporting proactive safety, security, and health management.

A method for optimizing alerts for a user is disclosed. The method comprises monitoring a plurality of alerts of one or more alert types within a predefined time period; determining a count of the plurality of alerts of each alert type within the predefined time period; determining the count of the plurality of alerts of each alert type exceeds a predefined threshold level within the predefined time period; triggering a storm alert corresponding to the plurality of alerts of each alert type upon determining the count of the plurality of alerts of each alert type exceeds the predefined threshold level, wherein the storm alert corresponds to an alert triggered when the plurality of alerts of each alert type is occurred multiple times within the predefined time period; and displaying a notification related to the storm alert and information related to the storm alert, to a user.

An electronic control unit for a vehicle. The electronic control unit including a memory region in which is stored a lookup table indicating, depending on a type of alert and on the distance between the vehicle and a traffic event relating to the alert, the need to send a response message to the alert signal. The electronic control unit being configured to: receive an alert signal relating to a traffic event containing the type of alert and the position of the traffic event, receive the predetermined position of the vehicle, determine the distance between the vehicle and the traffic event, determine, based on the lookup table and on the determined distance, whether or not there is a need to send a response message to the alert signal, and dispatch a command signal commanding a response message to be sent.

The invention discloses a kubernetes-based container group health scoring method, and belongs to the technical field of computers. The method comprises the following steps of: creating a HealthCheckPolicy health scoring rule; the health scoring rule of the HealthCheckPolicy comprises a lifecycle type and an alert type, and the health scoring rule of the HealthCheckPolicy comprises a lifecycle type and an alert type; obtaining alarm information and pod information; according to the alarm information, the pod information and a HealthCheckPolicy health scoring rule, a health score is obtained through calculation; and restarting the container according to the health score. According to the method, the health condition of the running application can be evaluated, and the application in the sub-health state can be restarted gracefully.

In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and / or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and / or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

System and method for an alarms simulator including selecting an alarm type from a plurality of alarm types, customizing the selected alarm type based, at least in part, on environment and operations data of a user by configuring at least one parameter for the selected alarm type, and replicating the customized alarm type. The method further includes practicing responding to the replicated customized alarm type in a practice mode operated on the alarms simulator, operating the alarms simulator in a test mode to train and test knowledge of the plurality of alarm types including the customized alarm type, and displaying, via a display on the alarms simulator, a result of the test mode representing an accuracy of immediate feedback from the user during test mode.

Aspects of the subject disclosure may include, for example, a method in which a processing system receives an alert for delivery to user equipment devices (UEs) communicating on a network. The system determines a type of the alert and a coverage area for the alert; identifies UEs in the coverage area for delivery of the alert; and determines, for each of the UEs, a priority level for the alert, and selects, for each of the UEs, a delivery profile for delivering the alert; the delivery profile is based on the type of the alert and comprises the priority level, and specifies a manner of presenting the alert at the UE, based on the priority level. The system then delivers the alert to each of the plurality of target UEs respectively in accordance with the delivery profile. Other embodiments are disclosed.

The present disclosure provides a security event identification method and device, and electronic equipment, and relates to the technical field of network security. The method comprises the following steps: in response to obtaining security event information, extracting a plurality of entity information related to the security event from the security event information, wherein the plurality of entity information comprises event entity information and network entity information; determining entity association information corresponding to the security event according to the plurality of entity information, wherein the entity association information represents the association relationship between the event entity information and another entity information, and the another entity information is the event entity information or the network entity information; extracting an alarm feature of the event entity information according to the entity association information; and identifying the alarm type of the security event through the alarm feature. According to the technical scheme, the alarm type of the security event is identified based on the multidimensional information of the security event and the relatively complex association relationship between the multidimensional information, and the accuracy of identification can be improved.

The invention relates to an aircraft system log file automatic processing and alarm system. Systems, devices, and methods provide for cycling through multiple aircraft system alert types. Alarm rules associated with each aircraft system alarm type are determined, where the alarm rules have an alarm threshold associated with the category type. An aircraft log data repository is scanned for error events associated with the alert rules. Error events are grouped based at least in part on the category type. It is determined whether a set of error events satisfies an alarm threshold. In response to an alarm threshold not being met, the set of error events is ignored.

A system is provided comprising external device(s), tracking device(s) configured to generate blood glucose level data for tracked individual(s), and an alert device for management of alerts generated at the external device(s). The alert device comprises processor(s) and memory device(s). The memory device(s) comprise computer readable code that, when executed by the processor(s), causes the processor(s) to receive input data comprising blood glucose level data received from the tracking device(s), to determine an alert setting for an alert to be generated at an external device of the external device(s) based on the input data, and to generate a signal that causes the alert to be generated at the external device. The alert setting includes an identification of the external device where the alert is to be generated, an alert magnitude, and / or the alert type, with the alert type including an audible alert, a haptic alert, and / or a visual alert.

Devices, systems and processes for providing local alerts to users via non-local channels are described. A process may include receiving, by a content distributor system, an alert message. The alert message includes a first alert condition and a second alert condition. The first alert condition and the second alert condition are identified in the alert message, and an alert notice is generated, based on the identified alert conditions, and transmitted to a user device coupled to the content distributor system. The first alert condition specifies an alert type. The second alert condition specifies a time period. The alert message may include a third alert condition specifying an action for a user of the user device to take or refrain from taking during the time period and the alert notice may identify the action. The alert message may include a target condition and the alert notice identifies actions to be performed.

Systems, computer program products, and methods are described herein for automatically building dynamic queries for identifying data in unstructured datasets. The present disclosure is configured to receive a plurality of pre-defined query templates associated with an alert type(s); dynamically filter the plurality of pre-defined query templates based on the alert type(s); identify an unstructured database note(s) based on a user identifier; apply the unstructured database note(s) and the filtered plurality of pre-defined query templates to an unstructured notes search engine; generate, by the unstructured notes search engine, at least one raw score for the at least one unstructured database note and for each alert type(s); transform, by the unstructured notes search engine, the at least one raw score to a relevancy score using a quantile transformation; and determine a highest relevancy score for each of the at least one unstructured database note for the user identifier.

Approaches of the disclosure are directed towards the intelligent management of notifications. Notifications can be intelligently managed across digital devices by contextualizing and prioritizing notifications to, for example, match a current state or situation of a user. Incoming notifications may be contextualized by analyzing their content and sources, such as by using large language models. Such an approach may further take into account the user's current status, including factors such as location, activity, and personal preferences, as may be obtained from various sources or learned over time. Preferences or appropriate delivery methods can be learned by observing and / or analyzing user interactions associated with previously-presented notifications and adjusting the delivery methods for subsequent notifications, which may involve suppressing the notification, presenting immediately, changing an alert type, or altering content for presentation.

Methods, systems, and computer program products obtain electronically stored records generated by one or more computers and indicative of one or more alerts raised in response to a computer security incident. A processor extracts data informative of one or more of: (i) presence or absence of specific alert types, (ii) a number of different alert types raised in response to the incident, and (iii) a number of times a given alert type or combination of alert types was raised in a specified previous time period, executes a scoring model on the extracted data to generate a maliciousness score for the incident, computes contribution values for portions of the extracted data indicating respective contributions to the maliciousness score, selects a portion having a greatest contribution value, and selects and initiates a protective action of one of isolating a computing device, terminating a process, quarantining a file, or restricting user access.

Disclosed is a system (100) and a method (300) for monitoring historical performance of nodes (120) in a communication network. The method comprises receiving a plurality of parameters for generating visualization data of historical alarms corresponding to the nodes (120) of one or more vendors for a geographical location during a user defined time-period. Based on the plurality of parameters, historical alarm data is fetched from a first database (270-1). Based on the fetched historical alarm data, an alarm type of each of the historical alarms corresponding to the nodes (120) for each vendor of the one or more vendors, and a count of the historical alarms of each alarm is determined. Subsequently, based on the determined count of the historical alarms, the visualization data including information related to at least one of an aggregated count or a performance trend of the historical alarms for each alarm type is generated.

Implementations generally relate to hearing aids. In some implementations, a method includes receiving a sound at a hearing aid, the sound being an alarm. The method further includes determining a type of the alarm. The method further includes generating a notification based on the type of alarm.

System and method for a permanent monitoring and control automation system for a well includes a grease tank including a storage container configured to hold grease, sensors for measuring grease volume in the grease tank and pressure levels, and a pump for greasing a valve at a pre-set pressure level value using grease from the grease tank. The system further includes a greasing control system for monitoring parameters, inputting parameters into an artificial intelligence model, determining pumping rate, volume, and time schedule, predicting a greasing maintenance schedule, and pumping grease. The greasing control system includes a computer processor connected to a remote terminal unit and a supervisory control and data acquisition. The system further includes an alert system for transmitting alarms to notify a user of system status. The alarms include low grease level on the valve, operational status, alert type, system malfunction, and grease tank low level.

In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and / or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and / or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

The present invention relates to a method for generating an alarm pertaining to continuous biometric data and, more specifically, to an alarm generation method in which, if an alarm condition is satisfied, an alarm is output to a user while maintaining the alarm in an activated state during a set alarm period, and changing the alarm type selected by the user during the alarm period, thereby enabling the user to be prompted to input information or take an action in response to the alarm without ignoring the alarm, while not causing inconvenience to the user due to frequent alarms; and when the alarm condition is satisfied, an operating mode of a receiver is determined and the alarm type, which has been set in accordance with the alarm condition, is changed to an alarm type corresponding to the operating mode of the receiver, thereby making it possible to prevent an alarm from being generated against the user's wishes.

Alarms issued by a security system may be received, with each alarm including an alarm type, an alarm time stamp, and an alarm location in the facility. The received alarms are logged in an alarm log. Alarms in the alarm log that have an alarm time stamp that fall within a predetermined time window of interest are identified. Based at least in part on the identified alarms, a prediction is made that a first one of the plurality of secure regions of the facility will have at least a first threshold number of alarms during a first predicted future time period. A first indication is outputted that the first one of the plurality of secure regions of the facility is predicted to have at least the first threshold number of alarms during the first predicted future time period.

System and method for a permanent monitoring and control automation system for a well includes a grease tank including a storage container configured to hold grease, sensors for measuring grease volume in the grease tank and pressure levels, and a pump for greasing a valve at a pre-set pressure level value using grease from the grease tank. The system further includes a greasing control system for monitoring parameters, inputting parameters into an artificial intelligence model, determining pumping rate, volume, and time schedule, predicting a greasing maintenance schedule, and pumping grease. The greasing control system includes a computer processor connected to a remote terminal unit and a supervisory control and data acquisition. The system further includes an alert system for transmitting alarms to notify a user of system status. The alarms include low grease level on the valve, operational status, alert type, system malfunction, and grease tank low level.

A system that can, using reduced memory resources, receive by a transaction channel monitoring layer, an alert issued by a server application of a server associated with a transaction channel of a transaction system regarding a problem associated with the transaction channel, and subsequently retrieve, by a real-time notification layer, the alert received by the transaction channel monitoring layer. The system can use information contained in the alert, such as for example, an identification of the transaction channel associated with the alert, an alert type, and an alert severity, to automatically determine a form of a notification to be generated, the content of the notification, and at least one recipient of the notification. The system can then automatically generate the notification, and automatically transmit the notification in real-time to the at least one recipient of the notification.

A method and system are provided for video verification of security system alarms using automated video analytics. When a sensor generates an alarm, the system automatically identifies the associated camera and retrieves relevant video recordings corresponding to the alarm timestamp. A video analytics algorithm analyzes the recording to generate a confidence score for alarm verification. If the confidence score exceeds a threshold, the alarm is reported as verified to a central monitoring station; otherwise, it is reported as unverified. The system can select a specific analytics algorithm based on the alarm type and manage multiple associated cameras with defined priorities. The confidence score can take into account factors such as camera field-of-view overlap and image quality. These analytics algorithms can be trained using operator-provided ground truth data to improve accuracy.

Methods and systems for managing operation of a data processing system are disclosed. A management controller of the data processing system may obtain an alert via an out-of-band communication channel. The alert may be broadcasted by a service system using a wireless wide area network. A determination may be made regarding whether the alert is valid, and, if the alert is valid, then the management controller may identify a policy for the data processing system that specifies types of alerts and associated modalities of communication of the types of alerts. The management controller may initiate presentation of the alert to a user of the data processing system in accordance with the policy.

Systems, computer program products, and methods are described herein for automatically building dynamic queries for identifying data in unstructured datasets. The present disclosure is configured to receive a plurality of query templates associated with an alert type(s); identify an unstructured database note(s) based on a user identifier; apply the unstructured database note(s) and the plurality of query templates to an unstructured notes search engine; generate, by the unstructured notes search engine, at least one raw score for the at least one unstructured database note and for each alert type(s); transform, by the unstructured notes search engine, the at least one raw score to a relevancy score using a quantile transformation; and determine a highest relevancy score for each of the at least one unstructured database note for the user identifier.

A method for generating a risk score includes: for each user in a plurality of users, collecting a plurality of sensor readings over a time period using an input device worn by the user, the input device configured to collect the plurality of sensor readings; calculating one or more comparison metrics based on the plurality of sensor readings; determining whether the one or more comparison metrics satisfies one or more threshold requirements; generating a plurality of information alerts if the one or more comparison metrics does not satisfy the one or more threshold requirements in the time period, each information alert corresponding to an information alert type; generating a risk factor for each information alert based on the information alert type; generating one or more alert totals for the time period based on the plurality of information alerts; generating a risk score for the user based on each of the risk factors and the one or more alert totals; and ordering the plurality of users in a list according to the risk score for each user.

The invention discloses a network security alarm optimization method and system based on deep learning and multi-dimensional correlation analysis, belongs to the technical field of detection and protection in network security, and aims to solve the technical problems of how to optimize network security alarm, reduce the false alarm rate of network security alarm and prevent operation and maintenance personnel from being interfered by false alarm. According to the technical scheme, the method comprises the steps of alarm information collection and storage, wherein comprehensive and accurate alarm information and processing information are collected from all key nodes of a network and stored; multi-dimensional correlation analysis: starting from four dimensions of time, alarm types, asset IPs and judgment results, mining correlation rules among the time, the alarm types, the asset IPs and the judgment results through an Apriori algorithm, and further judging whether the new alarms are false alarms or not; constructing and training a deep learning model: dividing the alarm data into a training set and a test set according to a preset proportion in a disorganized order; and false alarm judgment and feedback are carried out.

Alarms issued by a security system may be received, where each alarm includes an alarm type, an alarm timestamp, and an alarm location in a facility. The received alarms are recorded in an alarm log. An alarm in the alarm log having an alarm timestamp that falls within a predetermined time window of interest is identified. Based at least in part on the identified alerts, it is predicted that a first security region of the plurality of security regions of the facility will have at least a first threshold number of alerts during the first predicted future time period. A first indication is output indicating that a first security region of the plurality of security regions of the facility is predicted to have at least a first threshold number of alerts during the first predicted future time period.