Patent Publication Number: US-2017372242-A1

Title: System to monitor and process noise level exposure data

Description:
FIELD 
     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). 
     BACKGROUND 
     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. 
     SUMMARY 
     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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is block diagram of a system according to some embodiments of the present invention. 
         FIG. 2  illustrates a method that might be performed in accordance with some embodiments. 
         FIG. 3  illustrates an interactive operator display in accordance with some embodiments. 
         FIG. 4  is a block diagram of a stationary noise sensor according to some embodiments. 
         FIG. 5  is an example of a mobile noise sensor according to some embodiments. 
         FIG. 6  illustrates an alert method that might be performed in accordance with some embodiments. 
         FIG. 7  illustrates an alert and dashboard display in accordance with some embodiments. 
         FIG. 8  illustrates a noise level exposure system status display according to some embodiments. 
         FIG. 9  is block diagram of an industrial workplace according to some embodiments of the present invention. 
         FIG. 10  illustrates an insurance rating method that might be performed in accordance with some embodiments. 
         FIG. 11  is a flow diagram associated with an Internet of Things approach according to some embodiments. 
         FIG. 12  is block diagram of a noise level exposure tool or platform according to some embodiments of the present invention. 
         FIG. 13  is a tabular portion of a noise information database according to some embodiments. 
         FIG. 14  illustrates an overall enterprise method that might be performed in accordance with some embodiments. 
         FIG. 15  illustrates a system associated with a predictive model according to some embodiments. 
         FIG. 16  is a block diagram of an air quality measurement system in accordance with some embodiments. 
         FIG. 17  illustrates an interactive operator air quality display in accordance with some embodiments. 
         FIG. 18  illustrates an interactive operator display on a portable device in accordance with some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     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.  FIG. 1  is block diagram of a system  100  associated with a site  110  where site equipment  120  may be operated by workers  132 ,  134  (e.g., creating noise) according to some embodiments of the present invention. In particular, the system  100  includes a noise information hub  150  that may receive information from a plurality of stationary noise sensors  140  (described with respect to  FIG. 4 ) and/or mobile noise sensors  142 ,  144  (described with respect to  FIG. 5 ). Note that the site equipment  120  and workers  132 ,  134  may be moved around to various locations within the site  110  (e.g., as indicated by axis  112 ). Further note that a mobile noise sensor might be associated with a worker (e.g., mobile sensor  142  might be worn by worker  142 ) or may be independently mobile (e.g., a self-driving sensor). 
     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&#39; 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  FIG. 1 , any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the noise information hub  150 , enterprise analytics platform  180 , and/or noise information database  160  might be co-located and/or may comprise a single apparatus. 
     Note that the system  100  of  FIG. 1  is provided only as an example, and embodiments may be associated with additional elements or components. According to some embodiments, the elements of the system  100  facilitate an exchange of information.  FIG. 2  illustrates a method  200  that might be performed by some or all of the elements of the system  100  described with respect to  FIG. 1 , or any other system, according to some embodiments of the present invention. The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a computer-readable storage medium may store thereon instructions that when executed by a machine result in performance according to any of the embodiments described herein. 
     At S 210 , 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 S 220 , 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 S 230 , 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 S 240 , an enterprise analytics platform may receive the indications associated with the received data via the communication network. At S 250 , 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 S 260 , 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&#39; compensation insurance claims, quarterly hearing tests, etc. At S 270 , 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&#39;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). 
       FIG. 3  illustrates an interactive operator display  300  in accordance with some embodiments. The display  300  includes a “heat map” type rendering including areas  310 ,  312  that signify particular levels of noise exposure. As used herein, the phrase “heat map” may refer to a graphical representation of data where individual values contained in a matrix are represented as colors or other human readable features. In the example of  FIG. 3 , a first area  310  (e.g., near particular site equipment) might represent a potentially dangerous level of noise exposure and/or a place where workers might need to take special precautions (e.g., by wearing sound-reducing headphones). Note that the display  300  may facilitate an understanding of how different sources of noise interact with each other (e.g., to amplify or otherwise adjust the effect of the noise). According to some embodiments, the display  300  may further include icons  320  associated with an occurrence of a noise incident (e.g., a location where it is known that an employee was exposed to a potentially harmful level of noise). In some embodiments, an operator of the display  300  may use a computer pointer  330  to select an area to receive more detailed information about noise level exposure associated with that location. According to some embodiments, the display  300  further includes indications of prior noise related results  340 , such as workers&#39; compensation insurance claims for hearing damage (“C”) that have been filed in connection with various locations. 
       FIG. 4  is a block diagram of a stationary noise sensor  400  according to some embodiments. The stationary noise sensor  400  (and other stationary noise sensors) may be used to collect data about noise level exposure. The stationary noise sensor  400  might include, for example, a microphone  420  to sense noise, a power source  430  (e.g., associated with a battery, a re-chargeable battery with an 8 hour runtime, and/or an AC power adapter  432 ), and a communication device  440  (e.g., with a wireless antenna  442 ), coupled to the microphone  420  and the power source  430 , to transmit data about noise level exposure. As used herein, the sensor  400  may be stationary if it is not typically to move between locations (although the sensor  400  might be occasionally moved from one location to another). Note that any of the sensors provided herein might be associated with an ability to identify noise sources, sound power levels, directivity, locations, and/or time frames. 
       FIG. 5  is an example of a mobile noise sensor  500  according to some embodiments. The mobile noise sensor  500  (and other mobile noise sensors) may be used to collect data about noise level exposure. According to some embodiments, the mobile noise sensor  500  may be a self-driving device (e.g., a movable robot or flying drone). In other embodiments, the mobile noise sensor  500  might be worn by or otherwise be associated with a worker. In the example of  FIG. 5 , the mobile noise sensor comprises headphones or earmuffs that may be worn by a worker while or she is at a site being monitored. Other examples of wearable noise sensors include a smartphone, a tablet computer, an activity tracker, a hardhat, a work vest, work shoes, safety goggles, a lanyard or badge, a clipboard, and/or work gloves. The mobile noise sensor  500  might include, for example, a pair of earpiece bodies  510 , joined by a band  550 , at least one of the bodies  510  having a first microphone  520  outside of the body  510  (e.g., to monitor noise external to the headphone) and a second microphone  522  within the body  510  (e.g., to monitor noise proximate to the worker&#39;s ear). The mobile noise sensor  500  may further include a power source  530  (e.g., associated with a battery and/or a re-chargeable battery) and a communication device  540 , coupled to the microphones  520 ,  522  and the power source  530 , to transmit data about noise via a wireless antenna  542 . As used herein, the sensor  500  may be mobile if it often moves from one location to another (although the sensor  500  might remain at one location for a period of time). Note that information from multiple noise sensors (stationary and/or mobile) may be used to triangulate, estimate, or “pinpoint” a source of a noise and/or noise levels at locations between sensors. Moreover, a comparison of data from the first microphone  520  and the second microphone  522  may be used to imply whether or not an employee is correctly wearing the headphone (e.g., if the two microphones  520 ,  522  are detecting similar levels of noise, he or she is probably not wearing the headphone correctly). 
       FIG. 6  illustrates an alert method  600  that might be performed in accordance with some embodiments. At S 610 , data about noise sensed by each of a plurality of stationary noise sensors may be collected. At S 620 , data about noise sensed by each of a plurality of mobile noise sensors may be collected. At S 630 , 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 S 640 , 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 S 650 , 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 S 650 , the process may continue at S 610  (e.g., collecting data). If the threshold is exceeded at S 650 , the enterprise analytics platform may automatically generate and transmit an electronic alert message at S 660  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 S 650  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”).  FIG. 7  illustrates an alert and dashboard display  700  that includes noise level exposures  710  for a plurality of site locations in accordance with some embodiments. The display  700  also includes an example of an alert message  720  that might be automatically transmitted to a supervisor and operator selectable options  730  (e.g., to view data associated with a particular time period, disability claim data, etc.). According to some embodiments, the display may further include an overall noise level exposure rating  740  and/or classification (e.g., “average”) and/or dashboard-type display elements  750  (e.g., location-based and/or employee-based display dials). 
       FIG. 8  illustrates a system status display  800  that includes both an overall noise exposure rating  840  and ratings  842 ,  844  associated with sub-regions, zones, business units, etc. of the enterprise. The system status display  800  also includes data about each individual noise sensor (both stationary and mobile), such as a sensor status (e.g., operational, failed, mobile, etc.) and a current batter power level associated with that sensor. The system status display  800  further includes device-level dashboard information  850  that may, according to some embodiments, be selected by an operator to see a greater level of detail about that particular device. According to some embodiments, the display  800  (or the device itself) might generate an alarm when a sensor device is not operating properly (e.g., by flashing a light, emitting a beep, etc.). 
     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. 
       FIG. 9  is block diagram of a system  900  associated with an industrial workplace or factory  910  where machinery  920  is operated by workers  932 ,  934  (e.g., creating noise) according to some embodiments. As before, the system  800  includes a noise information hub  950  that receives information from a plurality of stationary noise sensors  940  and/or mobile noise sensors  942 ,  944 . Note that the machinery  920  and workers  932 .  934  may move around to various locations within the factory  910  (e.g., as indicated by axis  912 ). Further note that a mobile noise sensor might be associated with a worker (e.g., mobile sensor headphones  942  are worn by worker  932 ) or may be independently mobile (e.g., a self-navigating drone  944 ). 
     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.  FIG. 10  illustrates an insurance rating method  1000  that might be performed in accordance with some embodiments. At S 1010 , information about a workplace may be collected (e.g., associated with a type of industry, a number of employees, a building size, etc.). At S 1020 , noise level exposure information may be collected (e.g., in accordance with any of the embodiments described herein). At S 1030 , noise level exposure information may be stored to represent a period of time to be used to calculate a noise level exposure rating for the workplace. For example, a numerical rating or a rating category might be automatically calculated (e.g., a workplace may receive a “yellow” light indicating a moderate risk of hearing damage). At S 1040 , the noise level exposure rating is input to an insurance underwriting module that outputs at least one insurance based parameter. For example, the insurance underwriting module might automatically calculate an insurance premium based at least in part on the noise level exposure rating. At S 1050 , the system transmits an indication of the 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 S 1060 , the system may automatically generate and transmit workplace and/or employee recommendations. For example, an enterprise analytics platform might automatically recommend that a certain type of worker (e.g., an assembly line worker) spend  10  minutes each hour in a relatively quiet area to reduce the risk of hearing loss. 
       FIG. 11  is a flow diagram  1100  associated with an Internet of Things (“IoT”) approach according to some embodiments. At  1110 , noise sensors (include, in some embodiments, wearable devices) may detect noise information in substantially real time (sensors may measure external sound levels, sound levels at a worker&#39;s ear, etc.). According to some embodiments, sensors may be mounted at fixed places in the workplace. Sound data may be continuously collected to understand the sound profile of the business. 
     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,  FIG. 12  illustrates an enterprise analytics platform  1200  that may be, for example, associated with the systems  100 ,  900  of  FIGS. 1 and 9 , respectively. The enterprise analytics platform  1200  comprises a processor  1210 , such as one or more commercially available Central Processing Units (CPUs) in the form of one-chip microprocessors, coupled to a communication device  1220  configured to communicate via a communication network (not shown in  FIG. 12 ). The communication device  1220  may be used to communicate, for example, with one or more remote noise sensors, noise information hubs, etc. Note that communications exchanged via the communication device  1220  may utilize security features, such as those between a public internet user and an internal network of the insurance enterprise. The security features might be associated with, for example, web servers, firewalls, and/or PCI infrastructure. The enterprise analytics platform  1200  further includes an input device  1240  (e.g., a mouse and/or keyboard to enter information about noise sensors and/or employees) and an output device  1250  (e.g., to output reports regarding system administration, noise alerts, workplace modification recommendations, insurance policy premiums, etc.). 
     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&#39; 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  FIG. 12 ), the storage device  1230  includes a noise information database  1300 , an enterprise information database  1260  (e.g., storing information about an industry type, a number of employees, work schedules, etc.), an employee information database  1270  (e.g., storing ages, genders, roles within a workplace, etc.), and an insurance policy database  1280  (e.g., storing information about past disability insurance claims, current premium values, etc.). An example of a database that may be used in connection with the enterprise analytics platform  1200  will now be described in detail with respect to  FIG. 13 . Note that the database described herein is only an example, and additional and/or different information may be stored therein. Moreover, various databases might be split or combined in accordance with any of the embodiments described herein. For example, the insurance policy database  1280  and/or noise information database  1300  might be combined and/or linked to each other within the noise level exposure engine  1214 . 
     Referring to  FIG. 13 , a table is shown that represents the noise information database  1300  that may be stored at the enterprise analytics platform  1200  according to some embodiments. The table may include, for example, entries identifying noise sample collections. The table may also define fields  1302 ,  1304 ,  1306 ,  1308 ,  1310  for each of the entries. The fields  1302 ,  1304 ,  1306 ,  1308 ,  1310  may, according to some embodiments, specify: a noise level location identifier  1302 , an enterprise name  1304 , a date/time  1306 , noise level exposure data  1308 , and an alert indication  1310 . The noise information database  1300  may be periodically created and updated, for example, based on information electrically received from noise sensors and/or a noise information hub via a cloud-based application. 
     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. 
       FIG. 14  illustrates an overall enterprise method  1400  that might be performed in accordance with some embodiments. At S 1410 , the enterprise may establish an insurance policy with an insured. For example, an insurance company may issue a workers&#39; compensation insurance policy to a business. At S 1420 , the enterprise (either directly or with the help of the insured) may collect noise level exposure information. For example, the insurance company may measure the decibel levels of sounds that workers are exposed to throughout a factory. At S 1430 , the enterprise may collect workers&#39; compensation insurance claim information (e.g., including a type of injury and/or potential causes of the injury). For example, workers&#39; compensation insurance claim details, including indications of whether or not hearing injuries are involved, may be collected. At S 1440 , the enterprise may process workers&#39; compensation insurance claims (e.g., making payments to workers as appropriate). At S 1450 , the enterprise may analyze noise level exposure information and workers&#39; compensation insurance claims. Note that hearing injuries may, in some cases, take several years to develop (and thus, many years of noise level exposure information and associated insurance claim information may be collected and analyzed). At S 1460 , the enterprise may adjust the insurance policy (e.g., including a decision to renew, or not renew, various insurance policies) and/or other (future) insurance policies. For example, the insurance company might lower (or raise) an existing premium, adjust underwriting guidelines for a particular industry, etc. According to some embodiments, the willingness and ability of an enterprise to implement and/or enforce noise-related data collection might be indicative of an overall level of risk associated with that enterprise (e.g., associated with other types of insurance policies). 
     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  FIG. 15 .  FIG. 15  is a partially functional block diagram that illustrates aspects of a computer system  1500  provided in accordance with some embodiments of the invention. For present purposes it will be assumed that the computer system  1500  is operated by an insurance company (not separately shown) to support noise level exposure data monitoring and processing. 
     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&#39;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&#39;s policy database (not separately indicated). Another possible source of determinate data may be from data entry by the insurance company&#39;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&#39; notes) and digitized recorded voice files (e.g., claimants&#39; oral statements, witness interviews, claim handlers&#39; 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&#39; 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,  FIG. 16  is block diagram of a system  1600  associated with a site  1610  where site equipment  1620  may be operated by workers  1632 ,  1634  (and which may impact air quality) according to some embodiments of the present invention. In particular, the system  1600  includes an air quality information hub  1650  that may receive information from a plurality of stationary air quality sensors  1640  and/or mobile air quality sensors  1642 ,  1644 . Note that the site equipment  1620  and workers  1632 ,  1634  may be moved around to various locations within the site  1610  (e.g., as indicated by axis  1612 ). Further note that a mobile air quality sensor might be associated with a worker (e.g., mobile sensor  1642  might be worn by worker  1642 ) or may be independently mobile (e.g., a self-navigating sensor). As used herein, the phrase “air quality” might refer to any condition that could potentially impact a workers&#39; health, such as carbon monoxide, airborne mold, chemicals, vapors, radiation, temperature, etc. 
     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&#39; 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  FIG. 16 , any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the air quality information hub  1650 , enterprise analytics platform  1680 , and/or air quality information database  1660  might be co-located and/or may comprise a single apparatus. 
     Note that the system  1600  of  FIG. 16  is provided only as an example, and embodiments may be associated with additional elements or components. According to some embodiments, the elements of the system  1600  facilitate an exchange of information. According to some embodiments, data about air quality sensed by each of a plurality of “stationary” air quality sensors may be collected. Each stationary air quality sensor might include, for example, a microphone to sense air quality, a power source (e.g., associated with a battery, a re-chargeable battery, and/or an AC power adapter), 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 stationary air quality 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). 
     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 S 260 , 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&#39; 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). 
       FIG. 17  illustrates an interactive operator air quality display  1700  in accordance with some embodiments. The air quality display  1700  includes a “heat map” type rendering including areas  1710 ,  1712  that signify particular levels of air quality exposure. In the example of  FIG. 17 , a first area  1710  might represent a potentially dangerous level of air quality exposure and/or a place where workers might need to take special precautions. According to some embodiments, the display  1700  may further include icons  1720  associated with an occurrence of an air quality incident (e.g., a location where it is known that an employee was exposed to a potentially harmful level of a chemical). In some embodiments, an operator of the display  1700  may use a computer pointer  1730  to select an area to receive more detailed information about air quality level exposure associated with that location. According to some embodiments, the display  1700  further includes indications of prior air quality related results  1740 , such as workers&#39; compensation insurance claims (“C”) that have been filed in connection with various locations. 
     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,  FIG. 18  illustrates a handheld virtual heat map display  1800  according to some embodiments. 
     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.