22 results about "Incident report" patented technology

A method to identify which safety leading indicators are not capturing hazards during safety inspections that later contribute to safety incidents and to identify blind spots in safety inspections. The method includes inputting a plurality of incident reports from the worksite and a plurality of inspection reports from the worksite into a system that uses machine learning (ML) and artificial intelligence (AI); identifying socio-technical factors influencing safety risks at the worksite using unsupervised clustering for topic modeling; evaluating semantic similarity between the plurality of incident report and the plurality of inspection reports conducted in a preceding time period to detect missing socio-technical factors not captured during safety inspections; and providing the identified socio-technical factors previously not captured during safety inspections to a stakeholder for development of modified inspection checklists tailored to capture the identified socio-technical factors previously not captured during safety inspection.

This disclosure describes, in part, systems for reporting and locating incidents within a facility using user-directed independently controlled devices. The systems and techniques provide for a user to report an incident through an interface of the independently controlled device, such as a smart shopping cart, and for the device to communicate with a facility management system to identify the location of the device within the facility. The location and the incident report may be communicated to have an associate of the facility address and remediate the incident without interrupting a customer's experience within the facility.

A method and system for collecting, matching and reporting evidence related to a collision event captured on a first collision detection device associated with a vehicular transportation system. A first incident report of the collision event communicated from the first collision detection device to the cloud-based application. The first incident report includes image data, location data, and temporal data of the collision event. A collision event database of the cloud-based application is searched to locate a second incident report of the collision event. The first incident report is compiled with the second incident report when the collision event of the first incident report matches the recorded collision event of the second incident report. A collision evidence report is generated based on the compiled first and second incident reports.

Certain example embodiments herein relate to techniques that help automatically derive test scripts based on incidents reported using a trouble ticket reporting system or the like. Information is extracted from a support incident report related to an underlying incident reported via a ticket system. The extracted information includes one or more elements of a computer-implemented product implicated by the underlying incident and a category of incident. A test case is generated based thereon. If there is no test matching the generated test case already existing in a test repository, a new test based on the test case is generated using a generative artificial intelligence (AI) model, and the generated new test is stored to the test repository. Responsive to a developer fix being committed, one or more tests relevant to the developer fix is / are automatically identified and automatically executed.

An unsupervised pattern extraction system and method for extracting incident and root cause patterns from various kinds of machine data such as system-level metric values, system call traces, and semi-structured or free form text log data and performing holistic root cause analysis for distributed systems. The system utilizing Natural Language Processing and machine learning techniques to extract incident and root cause information from received incident reports and other system data. The system consists of both real time data collection (104) and analytics functions (200). The previously reported incident data is used to discover and apply remediation techniques to utilize prior remediation efforts to automatically classify and correct incidents. The system may then annotate a remediation data file with the technique applied. The system will utilize prior known remediation techniques for identified categories to predict and prevent future issues.

The present invention relates to a safety compliance, identification, and security monitoring system that includes a monitoring device operable to capture images / videos of a predefined area and persons and events within the area. The monitoring device is further operable to determine whether persons within the area are wearing required personal protective equipment. In one embodiment, the monitoring device initiates an alert a safety equipment infraction. In one embodiment, the system is operable to further record an incident report identifying the nature, date, and location of the infraction, as well as the identity of the person committing the infraction. In one embodiment, the system is operable to monitor various external conditions, such as heat, humidity, smoke, and initiate an alert of the external condition detected. In one embodiment, the system detects quality assurance / quality control issues, including situations involving inspecting equipment, tools, and / or materials for non-conformance.

A method to identify which safety leading indicators are not capturing hazards during safety inspections that later contribute to safety incidents and to identify blind spots in safety inspections. The method includes inputting a plurality of incident reports from the worksite and a plurality of inspection reports from the worksite into a system that uses machine learning (ML) and artificial intelligence (AI); identifying socio-technical factors influencing safety risks at the worksite using unsupervised clustering for topic modeling; evaluating semantic similarity between the plurality of incident report and the plurality of inspection reports conducted in a preceding time period to detect missing socio-technical factors not captured during safety inspections; and providing the identified socio-technical factors previously not captured during safety inspections to a stakeholder for development of modified inspection checklists tailored to capture the identified socio-technical factors previously not captured during safety inspection.

A system and method for reducing data record processing in incident report generation is provided. The method includes: accessing a plurality of event records, each event record generated based on an event in a computing environment; parsing each event record based on a predetermined data field; extracting from each predetermined data field a data value; correlating a group of event records of the plurality of event records based on at least an extracted data value; generating an incident data record based on the extracted data values of the correlated group of event records; generating a prompt based on the incident data record; and generating an incident report by configuring a large language model (LLM) to execute the generated prompt.

Disclosed are a method, system, and apparatus of a body-worn camera system with integrated artificial intelligence for real-time field assistance and automated incident reporting. In one embodiment, a cloud-based data management system includes an artificial intelligence module to generate a transcript of an incident using data captured from a body-worn safety device (e.g., audio or video data) that captures an incident surrounding a first responder. The transcription is highly accurate, even in noisy environments, and discerns different speakers, making it valuable for documenting interactions and statements. The transcribed text is analyzed with a private Large Language Model (LLM), to interpret at least one of the audio and video data, and to provide insights, summaries, and / or flag potential areas of concern based on the context and content of a conversation during the incident. An incident report based on the recorded and analyzed data.

A method comprises receiving an incident report comprising a textual description of an incident; generating a regularized incident report in which out-of-vocabulary terms in the received incident report are replaced with in-vocabulary terms; determining importance measures for a plurality of incident report terms, wherein each of the incident report terms is in the regularized incident report; generating an incident matrix in which similarity values are defined for combinations of terms in the incident report and terms in a predetermined term set; generating an incident vector based on the incident matrix and the importance measures for the terms in the incident report; applying one or more machine learning (ML) models that identify, based on the incident vector, relevant software support records and / or software modules, wherein the relevant software support records and the software modules are potentially relevant to the incident; and outputting data identifying relevant software support records and / or software modules.

A method includes obtaining an incident report and assigning an incident type to the incident report. The method also includes determining that the incident type has not been assigned to any prior incident reports and generating a prompt associated with the incident type based on determining that the incident type has not been assigned to any prior incident reports. The method also includes generating a response to the incident report based on the prompt using a large language model (LLM).

An embodiment includes a method of creating an incident report for an incident in an incident workspace. The method includes obtaining incident data including one or both of screenshots and screen recordings of the incident. The one or both of screenshots and screen recordings are obtained using screen capture feature integrated in the incident workspace. The method includes analyzing the incident data based on the one or both of screenshots and screen recordings to identify one or more incidental features. The method includes generating a reconstructed incident corresponding to the incident based on the one or more incidental features. The method includes performing diagnostics of the incident based on the reconstructed incident to identify one or more potential causes and one or more potential solutions for the incident. The method includes evaluating the one or more potential solutions.

A method includes obtaining an incident report and assigning an incident type to the incident report. The method also includes determining that the incident type has not been assigned to any prior incident reports and generating a prompt associated with the incident type based on determining that the incident type has not been assigned to any prior incident reports. The method also includes generating a response to the incident report based on the prompt using a large language model (LLM).

A system and method for generating a report of events includes receiving procedure data captured during a procedure by at least one of a first sensor and a second sensor, each of the first sensor and the second sensor communicatively coupled to data processing hardware. The system and method also include detecting a first plurality of events in the procedure data, detecting a second plurality of events in the procedure data, and for each event of the first plurality of events and the second plurality of events, associating the event with a respective timestamp of when the event occurred during the procedure. The system and method further include merging the events by synchronizing the first plurality of events and the second plurality of events based on the respective timestamps, and generating a machine readable report including the merged events.

We provide the system. [Solution] A means for receiving basic information about the disability, A means of searching past incident reports and evaluating similarities, A method for automatically generating new incident reports based on previously searched reports, A means of generating predicted Q&A by referring to the customer's industry and past inquiry history, A means of preparing answers to the generated Q&A, A system that includes means for receiving actual Q&A and evaluating its accuracy by comparing it with predicted Q&A.

A method for dynamically improving monitoring miss resolution comprises obtaining an incident report describing a monitoring miss, wherein the monitoring miss is associated with an outage in the communication system that has not yet been resolved, adding, by the incident reporting application, a flag to the incident report, wherein the flag indicates that the incident report is associated with one or more monitoring misses in the communication system that have not yet been resolved, extracting identification data identifying the outage in the communication system from the incident report, wherein the identification data comprises an identification of one or more network elements (NEs) at which the outage has occurred, and obtaining, based on the identification data, incident data describing a context of the outage in the communication system to identify a cause of the monitoring miss.

A server(100) for facilitating collecting information about an incident, the server(100) comprising: a memory (130) configured to store instructions; and a processor (120) for executing the stored instructions and configured to : receive a request to initiate an incident reporting mode from a driver via a computing device(301);receive a message for the incident from the driver via the computing device(302);identify an intention from the message(303);classify the message into one or more incident categories among a plurality of predetermined incident categories, based on the identified intention(304); assign the message to one or more agents corresponding to the classified incident categories among a plurality of predetermined agents,to analyse the message to collect the information about the incident(305); and provide output information based on the collected information about the incident(306).

Systems and methods for generating certified images and incident reports are disclosed. An image capture device can be used to capture an image and integrate metadata from camera sensors as well as other ancillary device sensors into the image. The image and its metadata can then be certified upon a check that the image and its metadata are authentic and unaltered. The image and its metadata can then be included in or as a part of an incident or other report describing an incident or event such as an accident or a crime. The image and / or incident report may be maintained at a cloud-based server for viewing, authorized editing, and subsequent distribution.

A method (400) includes obtaining an incident report (102) and assigning an incident type (132) to the incident report. The method also includes determining that the incident type has not been assigned to any prior incident reports and generating a prompt (162) associated with the incident type based on determining that the incident type has not been assigned to any prior incident reports. The method also includes generating a response (182) to the incident report based on the prompt using a large language model (LLM) (180).

A method and system for collecting, matching and reporting evidence related to a collision event captured on a first collision detection device associated with a vehicular transportation system. A first incident report of the collision event communicated from the first collision detection device to the cloud-based application. The first incident report includes image data, location data, and temporal data of the collision event. A collision event database of the cloud-based application is searched to locate a second incident report of the collision event. The first incident report is compiled with the second incident report when the collision event of the first incident report matches the recorded collision event of the second incident report. A collision evidence report is generated based on the compiled first and second incident reports.