The present invention relates to computer systems and, more particularly, to computer systems associated with monitoring and/or processing noise level exposure data (e.g., associated with a workplace).
An enterprise may want to monitor and/or process noise level exposure data. For example, an employer may want to monitor noise level exposure data to help protect employees from hearing loss caused by loud and/or sustained noise levels. In some cases, an employer (or a party associated with disability insurance claims) may have an industrial hygienist visit a work site and perform a noise site survey to help understand the noise levels workers are exposed to during a typical workday. Such an approach, however, can be an expensive and error-prone process. For example, the industrial hygienist might not realize that different levels of noise are generated during different times of day, days of the week, etc. (e.g., due to different machines being operated and/or different processes being performed). As a result, improved ways to facilitate a monitoring and/or processing of noise level exposure data may be desired.
According to some embodiments, systems, methods, apparatus, computer program code and means may facilitate a monitoring and/or processing of noise level exposure data. In some embodiments, a plurality of stationary noise sensors may each include a microphone to sense noise, a power source, and a communication device to transmit data about noise sensed by the microphone. A plurality of mobile noise sensors may each include a microphone to sense noise, a power source, and a communication device to transmit data about noise sensed by the microphone. A noise information hub may receive data from the stationary noise sensors and mobile noise sensors and provide indications associated with the received data via a cloud-based application. An analytics platform may receive the indications and analyze them to determine noise level exposure information for each of a plurality of locations within a workplace. The analytics platform may also transmit information to facilitate rendering of an interactive graphical operator interface that displays a map-based presentation of the noise level exposure information and prior noise-related results for each of the locations.
Some embodiments provide: means for collecting, via a plurality of stationary noise sensors, data about noise sensed by each of the plurality of stationary noise sensors; means for collecting, via a plurality of mobile noise sensors, data about noise sensed by each of the plurality of mobile noise sensors; means for receiving, at a noise information hub, the data from the plurality of stationary noise sensors and the plurality of mobile noise sensors; means for providing, from the noise information hub, indications associated with the received data via a communication network; means for receiving, by an enterprise analytics platform, the indications associated with the received data via the communication network; means for analyzing, by the enterprise analytics platform, the received indications to determine noise level exposure information for each of a plurality of locations within the site of the enterprise; and means for transmitting, from the enterprise analytics platform, information to facilitate rendering of an interactive graphical operator interface, the interactive graphical operator interface displaying a map-based presentation of the noise level exposure information and prior noise-related results for each of the plurality of locations.
A technical effect of some embodiments of the invention is an improved, secure, and computerized method to facilitate a monitoring and/or processing of noise level exposure data. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.
The present invention provides significant technical improvements to facilitate a monitoring and/or processing of noise level exposure data, predictive modeling, and dynamic data processing. The present invention is directed to more than merely a computer implementation of a routine or conventional activity previously known in the industry as it significantly advances the technical efficiency, access and/or accuracy of communications between devices by implementing a specific new method and system as defined herein. The present invention is a specific advancement in the areas of noise level exposure monitoring and/or processing by providing benefits in data accuracy, data availability, and data integrity, and such advances are not merely a longstanding commercial practice. The present invention provides improvement beyond a mere generic computer implementation as it involves the processing and conversion of significant amounts of data in a new beneficial manner as well as the interaction of a variety of specialized client and/or third party systems, networks and subsystems. For example, in the present invention information may be processed, forecast, and/or predicted via a analytics engine and results may then be analyzed efficiently to evaluate the safety of a workplace, thus improving the overall performance of an enterprise system, including message storage requirements and/or bandwidth considerations (e.g., by reducing a number of messages that need to be transmitted via a network). Moreover, embodiments associated with predictive models might further improve worker performance, predictions of employee claims, resource allocation decisions, etc.
An enterprise, such as an employer, may want to monitor and/or process noise level exposure data. For example, an employer may want to monitor noise level exposure data to help protect employees from hearing loss that might be caused by prolonged exposure to loud noises. To help prevent such damage, an insurer associated with disability insurance claims might have an industrial hygienist visit a work site to perform a noise site survey to help understand the noise levels that workers are exposed to during a typical workday. Such an approach, however, can be an expensive and error-prone process. For example, the industrial hygienist might not realize that different levels of noise are generated during different times of day, days of the week, etc. As a result, improved ways to facilitate a monitoring and/or processing of noise level exposure data may be desired.
According to some embodiments, the noise information hub 150 exchanges data with a noise information database 160 and/or an enterprise analytics platform via a communication network 170. For example, a Graphical User Interface (“GUI”) 152 of the noise information hub 150 might transmit information to facilitate a rendering of an interactive graphical operator interface display 190 and/or the creation of electronic alert messages, automatically created employee and/or site recommendations, etc. According to some embodiments, the noise information hub 150 may instead store this information in a local database.
The noise information hub 150 and/or enterprise analytics platform 180 may receive a request for a display from a requestor device. For example, an employer might use his or her smartphone to submit the request to the noise information hub 150. Responsive to the request, the noise information hub 150 might access information from the noise information database 160 (e.g., associated with noise level exposures over a period of time). The noise information hub 150 and/or enterprise analytics platform 180 may then use the GUI 152 to render operator displays 190. According to some embodiments, an operator may access secure site 110 information through a validation process that may include a user identifier, password, biometric information, device identifiers, geographic authentication processes, etc. According to some embodiments, the enterprise analytics platform 180 may further access electronic records from a noise impact data store 162. The noise impact data store 162 might, for example, store information about prior noise-related results associated with an enterprise (and each result might be associated with a location of the enterprise).
The noise information hub 150 and/or enterprise analytics platform 180 might be, for example, associated with a Personal Computer (“PC”), laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. The noise information hub 150 and/or enterprise analytics platform 180 may, according to some embodiments, be associated with an insurance provider.
According to some embodiments, an “automated” noise information hub 150 may facilitate the provision of noise exposure level information to an operator. For example, the noise information hub 150 may automatically generate and transmit electronic alert messages (e.g., when a noise incident occurs) and/or site/employee recommendations. As used herein, the term “automated” may refer to, for example, actions that can be performed with little (or no) intervention by a human.
As used herein, devices, including those associated with the noise information hub 150 and any other device described herein may exchange information via any communication network 170 which may be one or more of a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.
The noise information hub 150 and/or enterprise analytics platform 180 may store information into and/or retrieve information from the noise information database 160. The noise information database 160 might be associated with, for example, an employer, an insurance company, an underwriter, or a claim analyst and might also store data associated with past and current insurance claims (e.g., workers' compensation insurance claims associated with hearing loss). The noise information database 160 may be locally stored or reside remote from the noise information hub 150. As will be described further below, the noise information database 160 may be used by the noise information hub 150 to generate and/or calculate noise level exposure data. Note that in some embodiments, a third party information service may communicate directly with the noise information hub 150 and/or enterprise analytics platform 180. According to some embodiments, the noise information hub 150 communicates information associated with a simulator and/or a claims system to a remote operator and/or to an automated system, such as by transmitting an electronic file to an underwriter device, an insurance agent or analyst platform, an email server, a workflow management system, a predictive model, a map application, etc.
Although a single noise information hub 150 and enterprise analytics platform 180 is shown in
Note that the system 100 of
At S210, data about noise sensed by each of a plurality of “stationary” noise sensors may be collected. Each stationary noise sensor might include, for example, a microphone to sense noise, a power source (e.g., associated with a battery, a re-chargeable battery, and/or an Alternating Current (“AC”) power adapter), and a communication device, coupled to the microphone and the power source, to transmit data about noise sensed by each of the plurality of stationary noise sensors. As used herein, a sensor may be stationary if it is not typically to move between locations (although the sensor might be occasionally moved from one location to another).
At S220, data about noise sensed by each of a plurality of “mobile” noise sensors may be collected. Each mobile noise sensor might include, for example, a microphone to sense noise, a power source (e.g., associated with a battery and/or a re-chargeable battery), and a communication device, coupled to the microphone and the power source, to transmit data about noise sensed by each of the plurality of mobile noise sensors. As used herein, a sensor may be mobile if it often moves from one location to another (although the sensor might remain at one location for a period of time). By way of example only, a mobile noise sensor might be associated with a smartphone, a tablet computer, an activity tracker, a headphone or earmuff device, an earplug, a hardhat, a work vest, work shoes, safety goggles, a lanyard or badge, a clipboard, work gloves, a self-driving device, and a drone.
At S230, a noise information hub may receive data from the plurality of stationary noise sensors and the plurality of mobile noise sensors. The noise information hub may also provide indications associated with the received data via a communication network (e.g., via a cloud-based application).
At S240, an enterprise analytics platform may receive the indications associated with the received data via the communication network. At S250, the enterprise analytics platform may analyze the received indications to determine noise level exposure information for each of a plurality of locations within a site of an enterprise. At S260, the enterprise analytics platform may correlate noise level exposure information with prior noise-related results (e.g., what levels of noise level exposure resulted in a higher likelihood of a particular result occurring?). The results might be associated with, for example, workers' compensation insurance claims, quarterly hearing tests, etc. At S270, the enterprise analytics platform may transmit information to facilitate rendering of an interactive graphical operator interface that displays a map-based presentation of the noise level exposure information and prior noise-related results for each of the plurality of locations. According to some embodiments, the interactive graphical operator interface further includes indications of noise level exposure incidents or events.
According to some embodiments, an enterprise analytics platform may also automatically generate an electronic alert message based on the noise level exposure information. Moreover, the enterprise may be associated with an employer and the electronic alert message might further be based on: an employee location, an employee age, an employee gender, an industry standard, an employee protective equipment status, a length of time, a potential cause of a noise level event, and/or an indication of a remedial action. For example, the enterprise analytics platform might recommend that a 45 year old's work be removed from a relatively noisy environment for two hours in the afternoon (based on his or her actual noise level exposure in the morning). According to some embodiments, selection of a location via the interactive graphical operator interface results in a display of detailed noise level exposure information about that location (e.g., a particular rating or decibel level).
In some embodiments, the enterprise analytics platform may store noise level exposure information representing a period of time (e.g., data representing the last thirty working days). Moreover, the noise level exposure information representing the period of time might be used to calculate a noise level exposure rating for the enterprise (e.g., an employer might be classified as “moderately noisy”). According to some embodiments, the noise level exposure rating is an input to an insurance underwriting module that outputs at least one insurance based parameter (e.g., associated with an insurance premium, a deductible value, a co-payment, an insurance policy endorsement, and/or an insurance limit value). For example, an employer classified as “not noisy” might receive a percent or fixed premium discount for disability insurance (e.g., because fewer hearing-related claims might be expected as compared to “very noisy” employers).
At S640, an enterprise analytics platform may receive the indications associated with the received data via the communication network. The enterprise analytics platform may analyze the received indications to determine noise level exposure information for each of a plurality of locations within a site of an enterprise (e.g., to facilitate rendering of an interactive graphical operator interface that displays a map-based presentation of the noise level exposure information for each of the plurality of locations). At S650, the enterprise analytics platform may automatically determine if noise level exposure exceeds a pre-determined threshold The threshold might be associated with, for example, Occupational Safety and Health Administration (“OSHA”) guidelines or industry standards. If the threshold is not exceeded at S650, the process may continue at S610 (e.g., collecting data). If the threshold is exceeded at S650, the enterprise analytics platform may automatically generate and transmit an electronic alert message at S660 based on the noise level exposure information. The electronic alert message might also be based on, for example, an employee location, an employee age, an employee gender, an employee protective equipment status (e.g., is he or she wearing earplugs), a length of time, a potential cause of a noise level event, and/or an indication of a remedial action. For example, the enterprise analytics platform might recommend that all workers at the site be removed for 30 minutes due to help reduce the risk of hearing damage. Instead of a pre-determined threshold, the process at S650 might dynamically analyze the data searching for unusual levels of noise and/or conditions outside of a normal range of conditions.
In some embodiments, an enterprise analytics platform may store noise level exposure information representing a period of time (e.g., data representing the previous year). Moreover, the noise level exposure information representing the period of time might be used to calculate a noise level exposure rating for the enterprise (e.g., an employer might be classified as “moderately noisy”).
Embodiments described herein may be associated with various types of enterprises. For example, a music venue, a night club, an airport, a demolition team, an outdoor construction site, etc. might all be interested in monitoring and/or processing noise level exposure information.
According to some embodiments, the noise information hub 950 exchanges data with a noise information database 960 and/or an enterprise analytics platform via a communication network 970. For example, a GUI 982 of the noise information hub 950 may transmit information to facilitate a rendering of an interactive graphical operator interface display 990 and/or the creation of electronic alert messages, automatically created employee and/or site recommendations, etc. The noise information hub 950 and/or enterprise analytics platform 980 may, according to some embodiments, be associated with an insurance provider.
According to some embodiments, an overall noise level exposure rating may be used as an input to an insurance underwriting module that generates at least one insurance based parameter.
At 1120, an indoor positioning system may provide location information. For example, beacons (e.g., Bluetooth enabled beacons for indoor locations) may transmit a Universally Unique Identifier (“UUID”) to IoT sensors/devices within range. At 1030, an IoT hub may collect noise data. The IoT hub might be associated with, for example, a smartphone able to receive Bluetooth or Wi-Fi signals, or a wireless router. Note that the noise data may be collected locally before being sent to one or more remote computers for processing. According to some embodiments, the IoT hub might encrypt locally stored data, transmit data via a cloud application using secure transport techniques, record battery levels for sensors and/or hub devices, and/or capture indoor location data.
At 1140, an IoT network may be used to transfer the collected noise data. For example, data may be transferred in accordance with a Message Queuing Telemetry Transport (“MQTT”) light weight messaging protocol for use on top of the TCP/IP protocol. The IoT network may register/configure IoT devices for a given customer and/or location. The IoT network may also receive noise exposure data streamed directly from IoT devices.
At 1150, information analytics may be performed on the collected noise data. Note that data collected at a cloud-based application center may be analyzed based on requirement in substantially real time to generate alerts. This process may also persist the noise exposure data and/or provide real time (as well as periodic) analytics on the noise data. At 1160, a noise heat map display may be created. According to some embodiments, the system may continuously collect and store noise level exposure data, equipment information, location data, noise patterns, etc. along with any noise events and/or alerts. The system may then provide a site-level heat map dashboard that provides, daily, weekly, monthly data, etc. According to some embodiments the collected data may include location, time, noise, noise pattern, role, tasks, equipment usage, event type, etc. At 1170, a noise incident map display may be created. At 1180, a noise risk score may be automatically calculated (e.g., using a risk-score model based on noise incident maps and noise exposure data).
The embodiments described herein may be implemented using any number of different hardware configurations. For example,
The processor 1210 also communicates with a storage device 1230. The storage device 1230 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 1230 stores a program 1212 and/or a noise level exposure engine or application 1214 for controlling the processor 1210. The processor 1210 performs instructions of the programs 1212, 1214, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 1210 may automatically use a plurality of stationary noise sensors (each including a microphone to sense noise, a power source, and a communication device to transmit data about noise sensed by the microphone) to collect noise data. Similarly, the processor 1210 may use a plurality of mobile noise sensors (each including a microphone to sense noise, a power source, and a communication device to transmit data about noise sensed by the microphone) to collect noise data. A noise information hub may receive data from the stationary noise sensors and mobile noise sensors and provide indications associated with the received data via a cloud-based application. The processor 1210 may receive these indications and analyze them to determine noise level exposure information for each of a plurality of locations within a workplace. The processor 1210 may also transmit information to facilitate rendering of an interactive graphical operator interface that displays a map-based presentation (e.g., a heat map display) of the noise level exposure information and prior noise-related results (e.g., workers' compensation insurance claims for hearing damage) for each of the locations.
The programs 1212, 1214 may be stored in a compressed, uncompiled and/or encrypted format. The programs 1212, 1214 may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor 1210 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the enterprise analytics platform 1200 from another device; or (ii) a software application or module within the enterprise analytics platform 1210 from another software application, module, or any other source.
In some embodiments (such as shown in
Referring to
The noise level location identifier 1302 and enterprise name 1304 may be, for example, unique alphanumeric codes identifying a particular worksite location for an enterprise (e.g., associated with a latitude/longitude, X/Y coordinate, etc.). The date/time 1306 and noise level exposure data 1308 might indicate a recorded level of audible activity at a particular time for a given sensor (e.g., stationary or mobile sensor). The alert indication 1310 might indicate whether or not an alert signal was transmitted responsive to the noise level exposure data 1308. For example, as illustrated by the third entry in the table 1300, an alert 1310 might be generated when noise level exposure data exceeds “5.5” for a given location/employee.
According to some embodiments, one or more predictive models may be used to generate noise models or help with underwrite insurance policies and/or predict potential hearing damage based on prior events and claims. Features of some embodiments associated with a predictive model will now be described by first referring to
The computer system 1500 includes a data storage module 1502. In terms of its hardware the data storage module 1502 may be conventional, and may be composed, for example, by one or more magnetic hard disk drives. A function performed by the data storage module 1502 in the computer system 1500 is to receive, store and provide access to both historical claim transaction data (reference numeral 1504) and current claim transaction data (reference numeral 1506). As described in more detail below, the historical claim transaction data 1504 is employed to train a predictive model to provide an output that indicates potential noise level exposure patterns, and the current claim transaction data 1506 is thereafter analyzed by the predictive model. Moreover, as time goes by, and results become known from processing current claim transactions, at least some of the current claim transactions may be used to perform further training of the predictive model. Consequently, the predictive model may thereby adapt itself to changing event impacts and damage amounts.
Either the historical claim transaction data 1504 or the current claim transaction data 1506 might include, according to some embodiments, determinate and indeterminate data. As used herein and in the appended claims, “determinate data” refers to verifiable facts such as the an age of a home; a home type; an event type (e.g., fire or flood); a date of loss, or date of report of claim, or policy date or other date; a time of day; a day of the week; a geographic location, address or ZIP code; and a policy number.
As used herein, “indeterminate data” refers to data or other information that is not in a predetermined format and/or location in a data record or data form. Examples of indeterminate data include narrative speech or text, information in descriptive notes fields and signal characteristics in audible voice data files. Indeterminate data extracted from medical notes or accident reports might be associated with, for example, an amount of loss and/or details about damages.
The determinate data may come from one or more determinate data sources 1508 that are included in the computer system 1500 and are coupled to the data storage module 1502. The determinate data may include “hard” data like a claimant's name, date of birth, social security number, policy number, address; the date of loss; the date the claim was reported, etc. One possible source of the determinate data may be the insurance company's policy database (not separately indicated). Another possible source of determinate data may be from data entry by the insurance company's claims intake administrative personnel.
The indeterminate data may originate from one or more indeterminate data sources 1510, and may be extracted from raw files or the like by one or more indeterminate data capture modules 1512. Both the indeterminate data source(s) 1510 and the indeterminate data capture module(s) 1512 may be included in the computer system 1500 and coupled directly or indirectly to the data storage module 1502. Examples of the indeterminate data source(s) 1510 may include data storage facilities for document images, for text files (e.g., claim handlers' notes) and digitized recorded voice files (e.g., claimants' oral statements, witness interviews, claim handlers' oral notes, etc.). Examples of the indeterminate data capture module(s) 1512 may include one or more optical character readers, a speech recognition device (i.e., speech-to-text conversion), a computer or computers programmed to perform natural language processing, a computer or computers programmed to identify and extract information from narrative text files, a computer or computers programmed to detect key words in text files, and a computer or computers programmed to detect indeterminate data regarding an individual. For example, claim handlers' opinions may be extracted from their narrative text file notes.
The computer system 1500 also may include a computer processor 1514. The computer processor 1514 may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor 1514 may store and retrieve historical claim transaction data 1504 and current claim transaction data 1506 in and from the data storage module 1502. Thus the computer processor 1514 may be coupled to the data storage module 1502.
The computer system 1500 may further include a program memory 1516 that is coupled to the computer processor 1514. The program memory 1516 may include one or more fixed storage devices, such as one or more hard disk drives, and one or more volatile storage devices, such as RAM devices. The program memory 1516 may be at least partially integrated with the data storage module 1502. The program memory 1516 may store one or more application programs, an operating system, device drivers, etc., all of which may contain program instruction steps for execution by the computer processor 1514.
The computer system 1500 further includes a predictive model component 1518. In certain practical embodiments of the computer system 1500, the predictive model component 1518 may effectively be implemented via the computer processor 1514, one or more application programs stored in the program memory 1516, and data stored as a result of training operations based on the historical claim transaction data 1504 (and possibly also data received from a third party reporting service). In some embodiments, data arising from model training may be stored in the data storage module 1502, or in a separate data store (not separately shown). A function of the predictive model component 1518 may be to determine appropriate simulation models, results, and/or scores (e.g., a rating indicating how noisy a workplace is compared to other workplaces in similar industries). The predictive model component may be directly or indirectly coupled to the data storage module 1502.
The predictive model component 1518 may operate generally in accordance with conventional principles for predictive models, except, as noted herein, for at least some of the types of data to which the predictive model component is applied. Those who are skilled in the art are generally familiar with programming of predictive models. It is within the abilities of those who are skilled in the art, if guided by the teachings of this disclosure, to program a predictive model to operate as described herein.
Still further, the computer system 1500 includes a model training component 1520. The model training component 1520 may be coupled to the computer processor 1514 (directly or indirectly) and may have the function of training the predictive model component 1518 based on the historical claim transaction data 1504 and/or information about noise events, incidents, and alerts. (As will be understood from previous discussion, the model training component 1520 may further train the predictive model component 1518 as further relevant data becomes available.) The model training component 1520 may be embodied at least in part by the computer processor 1514 and one or more application programs stored in the program memory 1516. Thus the training of the predictive model component 1518 by the model training component 1520 may occur in accordance with program instructions stored in the program memory 1516 and executed by the computer processor 1514.
In addition, the computer system 1500 may include an output device 1522. The output device 1522 may be coupled to the computer processor 1514. A function of the output device 1522 may be to provide an output that is indicative of (as determined by the trained predictive model component 1518) particular noise heat maps, incidents, insurance underwriting parameters, and recommendations. The output may be generated by the computer processor 1514 in accordance with program instructions stored in the program memory 1516 and executed by the computer processor 1514. More specifically, the output may be generated by the computer processor 1514 in response to applying the data for the current simulation to the trained predictive model component 1518. The output may, for example, be a monetary estimate, a decibel level, and/or likelihood within a predetermined range of numbers. In some embodiments, the output device may be implemented by a suitable program or program module executed by the computer processor 1514 in response to operation of the predictive model component 1518.
Still further, the computer system 1500 may include a noise level exposure platform 1524. The noise level exposure platform 1524 may be implemented in some embodiments by a software module executed by the computer processor 1514. The noise level exposure platform 1524 may have the function of rendering a portion of the display on the output device 1522. Thus the noise level exposure platform 1524 may be coupled, at least functionally, to the output device 1522. In some embodiments, for example, the noise level exposure platform 1524 may direct workflow by referring, to an enterprise analytics platform 1526, employee recommendations, workplace modification recommendations, underwriting parameters, and/or alerts generated by the predictive model component 1518 and found to be associated with various results or scores. In some embodiments, this data may be provided to an insurer 1528 who may modify insurance parameters as appropriate.
Thus, embodiments may provide an automated and efficient way to facilitate monitoring and processing of noise level exposure data. The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.
Some embodiments have been described herein as being associated with noise level detection systems. Note, however, that embodiments may be associated with other types of workplace detection systems. For example,
According to some embodiments, the air quality information hub 1650 exchanges data with an air quality information database 1660 and/or an enterprise analytics platform via a communication network 1670. For example, a GUI 1652 of the air quality information hub 1650 might transmit information to facilitate a rendering of an air quality display 1690 and/or the creation of electronic alert messages, automatically created employee and/or site recommendations, etc. According to some embodiments, the air quality information hub 1650 may instead store this information in a local database.
The air quality information hub 1650 and/or enterprise analytics platform 1680 may receive a request for a display from a requestor device. For example, an employer might use his or her smartphone to submit the request to the air quality information hub 1650. Responsive to the request, the air quality information hub 1650 might access information from the air quality information database 1660 (e.g., associated with air quality level exposures over a period of time). The air quality information hub 1650 and/or enterprise analytics platform 1680 may then use the GUI 1652 to render operator displays 1690. According to some embodiments, an operator may access secure site 1610 information through a validation process that may include a user identifier, password, biometric information, device identifiers, geographic authentication processes, etc. According to some embodiments, the enterprise analytics platform 1680 may further access electronic records from an air quality impact data store 1662. The air quality impact data store 1662 might, for example, store information about prior air quality-related results associated with an enterprise (and each result might be associated with a location of the enterprise).
The air quality information hub 1650 and/or enterprise analytics platform 1680 might be, for example, associated with a PC, laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. The air quality information hub 1650 and/or enterprise analytics platform 1680 may, according to some embodiments, be associated with an insurance provider.
According to some embodiments, an “automated” air quality information hub 1650 may facilitate the provision of air quality exposure level information to an operator. For example, the air quality information hub 1650 may automatically generate and transmit electronic alert messages (e.g., when an air quality incident occurs) and/or site/employee recommendations.
As used herein, devices, including those associated with the air quality information hub 1650 and any other device described herein may exchange information via any communication network 1670 which may be one or more of a LAN, a MAN, a WAN, a proprietary network, a PSTN, a WAP network, a Bluetooth network, a wireless LAN network, and/or an IP network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.
The air quality information hub 1650 and/or enterprise analytics platform 1680 may store information into and/or retrieve information from the air quality information database 1660. The air quality information database 1660 might be associated with, for example, an employer, an insurance company, an underwriter, or a claim analyst and might also store data associated with past and current insurance claims (e.g., workers' compensation benefit insurance claims). The air quality information database 1660 may be locally stored or reside remote from the air quality information hub 1650. As will be described further below, the air quality information database 1660 may be used by the air quality information hub 1650 to generate and/or calculate air quality level exposure data. Note that in some embodiments, a third party information service may communicate directly with the air quality information hub 1650 and/or enterprise analytics platform 1680. According to some embodiments, the air quality information hub 1650 communicates information associated with a simulator and/or a claims system to a remote operator and/or to an automated system, such as by transmitting an electronic file to an underwriter device, an insurance agent or analyst platform, an email server, a workflow management system, a predictive model, a map application, etc.
Although a single air quality information hub 1650 and enterprise analytics platform 1680 is shown in
Note that the system 1600 of
According to some embodiments, data about air quality sensed by each of a plurality of “mobile” air quality sensors may be collected. Each mobile air quality sensor might include, for example, a microphone to sense air quality, a power source (e.g., associated with a battery and/or a re-chargeable battery), and a communication device, coupled to the microphone and the power source, to transmit data about air quality sensed by each of the plurality of mobile air quality sensors. As used herein, a sensor may be mobile if it often moves from one location to another (although the sensor might remain at one location for a period of time). By way of example only, a mobile air quality sensor might be associated with a smartphone, a tablet computer, an activity tracker, a headphone or earmuff device, an earplug, a hardhat, a work vest, work shoes, safety goggles, a lanyard or badge, a clipboard, work gloves, a self-navigating device, and a drone.
According to some embodiments, an air quality information hub may receive data from the plurality of stationary air quality sensors and the plurality of mobile air quality sensors. The air quality information hub may also provide indications associated with the received data via a communication network (e.g., via a cloud-based application).
According to some embodiments, an enterprise analytics platform may receive the indications associated with the received data via the communication network. Moreover, the enterprise analytics platform may analyze the received indications to determine air quality level exposure information for each of a plurality of locations within a site of an enterprise. At S260, the enterprise analytics platform may correlate air quality level exposure information with prior air quality-related results (e.g., what levels of air quality level exposure resulted in a higher likelihood of a particular result occurring?). The results might be associated with, for example, workers' compensation insurance claims, quarterly hearing tests, etc. According to some embodiments, the enterprise analytics platform may transmit information to facilitate rendering of an interactive graphical operator interface that displays a map-based presentation of the air quality level exposure information and prior air quality-related results for each of the plurality of locations. According to some embodiments, the interactive graphical operator interface further includes indications of air quality level exposure incidents or events.
According to some embodiments, an enterprise analytics platform may also automatically generate an electronic alert message based on the air quality level exposure information. Moreover, the enterprise may be associated with an employer and the electronic alert message might further be based on: an employee location, an employee age, an employee gender, an industry standard, an employee protective equipment status, a length of time, a potential cause of an air quality level event, and/or an indication of a remedial action. According to some embodiments, selection of a location via the interactive graphical operator interface results in a display of detailed air quality level exposure information about that location.
In some embodiments, the enterprise analytics platform may store air quality level exposure information representing a period of time. Moreover, the air quality level exposure information representing the period of time might be used to calculate an air quality level exposure rating for the enterprise. According to some embodiments, the air quality level exposure rating is an input to an insurance underwriting module that outputs at least one insurance based parameter (e.g., associated with an insurance premium, a deductible value, a co-payment, an insurance policy endorsement, and/or an insurance limit value).
Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with a noise incidents and/or events might be implemented as an augmented reality display and/or the databases described herein may be combined or stored in external systems). Moreover, although embodiments have been described with respect to noise level exposure information, embodiments may instead be associated with other types of worker protection. For example, embodiments might be used in connection with lifting injuries (e.g., which might result in back problems or muscle sprains), radiation levels, carbon monoxide levels, mold hazards, lead exposure, etc. Still further, the displays and devices illustrated herein are only provided as examples, and embodiments may be associated with any other types of user interfaces. For example,
The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims.