DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 05/04/2026
Claims 1-14 are presented for examination
Drawings
The drawings dated 12/08/2022 have been reviewed. They are accepted.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Abstract
The abstract dated 12/08/2022 has been reviewed. It has 81 words, and contains no legal phraseology. It is accepted
Finality
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Response to Arguments – Claim Objections and Interpretation
Applicant’s arguments, see pages , filed 05/04/2026, with respect to the objections and notes relating to claims 1-14 have been fully considered and are persuasive. The objections to claims 1-14 have been withdrawn.
Response to Arguments - 35 USC § 101
Applicant’s arguments, see pages 6-14, filed 05/04/2026, with respect to the rejection of claims 1-14 under 35 USC § 101 have been fully considered and are persuasive. The rejection of claims 1-14 under 35 USC § 101 has been withdrawn.
Particularly, the claims present a very particular hardware setup and hardware operations that, particularly in ordered combination, go beyond what is well-understood, routine, conventional activity. As such the claims are successfully integrated into a practical application.
Response to Arguments - 35 USC § 103
Applicant's arguments filed 05/04/2026 have been fully considered but they are not persuasive.
Applicant argues that the previously applied prior art does not “teach or suggest detecting medical sensor inputs connected at a medical-device communication interface, selecting clinical parameters of simulation signals based on those detected connected sensor inputs, decoupling the medical-device communication interface from a processor data input, and coupling simulation signals to that same processor data input in place of the detected sensor inputs.”
Examiner responds by explaining that, firstly, the claims do not require detecting medical sensor inputs connected at a medical-device communication interface, and therefore this argument as to whether or not the coupling/decoupling of medical sensors specially is disclosed in Holcomb or any other reference is moot.
Secondly, Duval-Arnold teaches selecting simulation parameters ([Col 1 line 55 -Col 2 line 3] “The input signal takes the form of at least one selected from a group consisting of a simulator controller signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. The non-transitory computer readable medium can be configured to encode the input signal to follow sensor-receiver protocols. The non-transitory computer readable medium can also be configured to change or adapt the input signal in real-time. The non-transitory computer readable medium is configured to simulate the output signal from at least one of the sensors selected from a group consisting of end tidal CO2 (ETCO2), pulse oximetry (SPO2), thermometer, blood pressure, quality of CPR (QCPR), and near-infrared spectroscopy (NIRS) sensors. The system can also include an interface for a user to input parameters into the system and a patient simulator.”)
While the selected parameters are not explicitly disclosed as being based on the detected sensor inputs in Duval-Arnold, this is remedied by Kiani, which describes detecting connected sensors and choosing tracked parameters based on the sensors that are detected ([Par 160] “The patient monitor 740 also includes a journal module 746 in the depicted embodiment. The journal module 740 may record medical events related to the patient monitor 740. These medical events can include clinician-initiated events, such as changes to alarm settings (e.g., maximum and minimum permitted parameter values), types of parameters monitored/sensors connected to the patient monitor 740, and the like.” [Par 153] “The monitoring module 742 can monitor physiological signals generated by one or more sensors coupled with a patient. The monitoring module 742 may process the signals to determine any of a variety of physiological parameters” [Par 74] “ Example alerts include no communication with pulse oximeter, alarm silenced on pulse oximeter, instrument low battery (pulse oximeter), and transmitter low battery. Example alarms include SpO.sub.2 levels and alarms, high and low SpO.sub.2, high and low PR, HbCO level and alarms, HbMET level and alarms, pulse rate and alarms, no sensor, sensor off patient, sensor error, low perfusion index, low signal quality, HbCO, HbMET, PI trend alarm, and desat index alarm.”)
Further note that “the clinical parameters of the simulation signals are selected based at least in part on the detected sensor inputs” is merely the act of selecting an appropriate simulated signal based on the what was detected; e.g. if a pulse oximeter was detected, simulating pulse oximetry. Therefore, while none of the references necessarily teach this operation on their own, between the process of performing simulations based on physiological parameters that are desired to be tracked, as disclosed in Duval-Arnold, and the process of choosing what physiological parameters are tracked based on the detected sensor inputs, as disclosed in Kiani, the combination of these references indeed teaches selecting the simulated physiological parameters based on the detected sensor input.
As to the decoupling of the sensor data and coupling of the simulation data, it would have been obvious to one of ordinary skill in the art that the generic switching capabilities of Holcomb would work identically between the oscilloscope and demo generator/ sensor input disclosed therein as it would between the claimed medical device and sensors/ simulation signal generator. Nothing in the claims other than the suggestion that the medical device is used within the medical field suggests any kind of medical-specific functionality is present in the way the communication operates that would not be possible in systems not designed within the medical context; in other words, there is nothing in the claims to suggest that communication system is more than a generic communication system used in a medical context. ([Par 20-21] “As further shown in FIG. 2, demonstration source multiplexor 209 selectively switches between the analog signal from digital-to-analog converter 205 and the serial demonstration patterns from serial demonstration generator 219, and provides the switched output to demonstration analog processor 213… The digital signals are connected from the MSO channel block 150 to MSO demonstration (demo) multiplexor 221 of demonstration processing circuit 200. MSO demonstration multiplexor 221 selectively switches between the stimulus signals provided from demonstration signal generators 201 and 203 and the digital signal from MSO channel block 150, and provides the switched output to acquisition memory and trigger circuit 121.”)
As Holcomb is indeed analogous art, being within the field of enabling simulation/demonstration modes on monitoring devices, it is directly applicable to the claimed system.
Applicant argues that the previously cited prior art does not teach “interrupting a communication channel between the medical device and one or more components of a medical facility monitoring network.” because the “claimed limitation concerns isolating the monitoring device from one or more monitoring- network components during simulation, whereas Kiani's cited disclosure presupposes continued network communication so that alarm messages, acknowledgements, and remote silencing commands can be exchanged.”
Examiner responds by explaining that this narrow interpretation of the claim limitation is not what is required given the broadest reasonable interpretation of the claim language. Particularly, given its broadest reasonable interpretation in view of the plain meaning of the language, the limitation does not actually require disconnection or isolation of the medical device from the facility network; interrupting a communication channel between the medical device and one or more components of a medical facility monitoring network merely requires that at least one communication traveling along that channel to at least one other node on the network is stopped. As such, Kiani’s remote alarm silencing capability, i.e. its ability to stop the communication of that alarm to the network, reads on the claim limitations.
If true isolation/disconnection from the network is intended to be captured in the claim limitations, it is suggested to amend the claims to instead use language such as “disconnecting a communication channel between the medical device and one or more components of a medical facility monitoring network.” Note that support for such an amendment can be found on [Page 22 Par 2] of the specification and would likely overcome the rejection.
Applicant argues that one of ordinary skill in the art would not have been motivated to combine Holcomb with Duval-Arnould and Kiani
Examiner responds by explaining that one of ordinary skill in the art would have had significant motivation. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art would have been motivated to make this combination in order to enable simulation modes within the device without requiring additional hardware. As noted by Holcomb, simulation/demonstration modes for many devices require additional hardware and controllers, which can be expensive and cumbersome to operate ([Par 2-3] “To demonstrate the capabilities of an oscilloscope to a potential customer during a sales process, a separate demonstration (demo) board may be manually connected to the oscilloscope and used as a signal source to provide multiple signal types for display. For complex signal types, it may be necessary to connect various external cables from the demo board to the oscilloscope. The oscilloscope must also be properly configured. In some instances, the demo boards may be reconfigurable. Demo boards may be expensive, which can be cost prohibitive because the demo boards are usually distributed to sales staff. Also, because of the complexity of oscilloscopes, it can be difficult to properly train and inform customers of the various oscilloscope operational modes. Written manuals may be provided to walk customers through self-training steps which may include connecting signals from a separate demo board or signal source to the oscilloscope inputs. In either case of demonstration or training, additional equipment is typically needed, increasing cost, time and difficulty. There is thus a need to demonstrate an oscilloscope without the use of a separate demo board. There is also a need to provide customer training using a broad set of waveform types without the use of a separate demo board or signal source.”) To this end, Holcomb presents a system including an integrated selectable simulation/demonstration mode capable of generating simulated signals without the necessity of external hardware ([Abstract] “An oscilloscope includes at least one demonstration signal generator integrated as part of the oscilloscope. The demonstration signal generator generates stimulus signals that consist of digital samples of various different stored waveforms without the need of a separate demonstration board or signal source. The demonstration signal generator may loop through different sections of the stored waveforms to generate respective stimulus signals that include sequences of digital samples from the different waveforms in combination, to provide a broad range of stimulus signals. The stimulus signals may be displayed on the oscilloscope or output from the oscilloscope as demonstration mode stimulus signals to demonstrate the capabilities of the oscilloscope to customers or for training.” [Par 20] “As further shown in FIG. 2, demonstration source multiplexor 209 selectively switches between the analog signal from digital-to-analog converter 205 and the serial demonstration patterns from serial demonstration generator 219, and provides the switched output to demonstration analog processor 213.”) Although Holcomb describes these issues and the associated technical solution within the context of oscilloscopes, it would have been easily recognized by one of ordinary skill in the art that the same issues present in the field of medical simulation (requirement of external, sometimes expensive and cumbersome hardware to perform simulation) could be solved in an identical way through the system of Holcomb (i.e. a selectively enabled, integral simulation generator.) Overall, one of ordinary skill in the art would have recognized that combining Duval-Arnould with Holcomb would allow the simulated signals to be generated within the medical device itself, requiring less hardware to perform simulations and therefore making the system simpler to use.
This is directly applicable to Duval-Arnould, which requires the use of external hardware, with the simulation system disclosed therein being an external signal generator ([Abstract] “The present invention is used to incorporate sensors and sensor simulators into training and clinical demonstrations. A system in accordance with the present invention includes a hardware component configured to transmit an output signal associated with a typical clinical sensor such as sensors for end-tidal CO.sub.2, pulse oximetry, temperature, blood pressure, near-infrared spectroscopy (NIRS) sensors, and CPR sensors to a clinical monitor or similar device.” [Fig. 1]) As such, one of ordinary skill in the art would have recognized that the ability to integrate this functionality into the monitoring device itself through the use of a setup such as described in Holcomb would make the entire system easier to use and operate, as well as cutting out the amount of bulky or expensive external hardware that would otherwise be required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
(1) Claims 1-4, 6-7, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Duval-Arnould (US 10580324 B2) in view of Holcomb (US 20120274313 A1) in further view of Kiani (US 20110105854 A1)
Claim 1. Duval-Arnould teaches A method for implementing a simulation mode on a medical device, ([Abstract] “The present invention is directed to systems for interfacing between sensors and sensor simulators and clinical monitors and devices. The present invention is used to incorporate sensors and sensor simulators into training and clinical demonstrations.”) the medical device being configured in a standard mode to receive a set of one or more sensor inputs at a communication interface, each carrying a signal representative of a clinical parameter, to couple the sensor inputs to a processor, and the processor configured to generate a user output at a user interface, based on the one or more sensor inputs, ([Col 1 line 20-23] “Clinical monitors and smart defibrillators interact with patients, using a number of different sensors to collect necessary clinically relevant physiological findings and measurements of provider performance.”) the method comprising: ([Col 1 line 40- Col 2 line 3] “The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device. In accordance with an aspect of the present invention the clinical device can take the form of at least one selected from a group consisting of a clinical monitor and a defibrillator. The input signal takes the form of at least one selected from a group consisting of a simulator controller signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. The non-transitory computer readable medium can be configured to encode the input signal to follow sensor-receiver protocols. The non-transitory computer readable medium can also be configured to change or adapt the input signal in real-time. The non-transitory computer readable medium is configured to simulate the output signal from at least one of the sensors selected from a group consisting of end tidal CO2 (ETCO2), pulse oximetry (SPO2), thermometer, blood pressure, quality of CPR (QCPR), and near-infrared spectroscopy (NIRS) sensors. The system can also include an interface for a user to input parameters into the system and a patient simulator.” [Col 3 line 33-64] “FIG. 1 illustrates a schematic diagram of an exemplary system and method according to an embodiment of the present invention. As illustrated in FIG. 1, step 1 includes generating a signal to replace output from a typical clinical sensor. The typical clinical sensor output signal can be replaced in a variety of different ways with various simulated outputs. Examples of such simulated outputs include but are not limited to a simulator control signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. Also as illustrated in FIG. 1, Step 2 includes encoding the simulated signal output to follow sensor-receiver protocols, if necessary. The computing device or non-transitory computer readable medium can also be used to complete this step. In step 3, the simulated signal is sent to the monitor/device. The non-transitory computer readable medium can be used to export the signal to a hardware component configured to communicate with both the non-transitory computer readable medium and the clinical monitor, medical device or other device used in a clinical setting and known to one of skill in the art. The monitor or device receives the signal and can display it in accordance with the typical operation of that particular machine” ([Col 2 line 55-58] “FIG. 2 illustrates a circuit diagram for hardware for accessing clinical sensor output data and feeding the data to a computing device according to an embodiment of the present invention.” [Col 1 line 20-23] “Clinical monitors and smart defibrillators interact with patients, using a number of different sensors to collect necessary clinically relevant physiological findings and measurements of provider performance.”) wherein the clinical parameters of the simulation signals are selected ([Col 1 line 55 -Col 2 line 3] “The input signal takes the form of at least one selected from a group consisting of a simulator controller signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. The non-transitory computer readable medium can be configured to encode the input signal to follow sensor-receiver protocols. The non-transitory computer readable medium can also be configured to change or adapt the input signal in real-time. The non-transitory computer readable medium is configured to simulate the output signal from at least one of the sensors selected from a group consisting of end tidal CO2 (ETCO2), pulse oximetry (SPO2), thermometer, blood pressure, quality of CPR (QCPR), and near-infrared spectroscopy (NIRS) sensors. The system can also include an interface for a user to input parameters into the system and a patient simulator.”) ([Col 2 line 55-58] “FIG. 2 illustrates a circuit diagram for hardware for accessing clinical sensor output data and feeding the data to a computing device according to an embodiment of the present invention.” [Col 1 line 20-23] “Clinical monitors and smart defibrillators interact with patients, using a number of different sensors to collect necessary clinically relevant physiological findings and measurements of provider performance.”) ([Col 3 line 57-64] “The non-transitory computer readable medium can be used to export the signal to a hardware component configured to communicate with both the non-transitory computer readable medium and the clinical monitor, medical device or other device used in a clinical setting and known to one of skill in the art. The monitor or device receives the signal and can display it in accordance with the typical operation of that particular machine.”)
Duval-Arnould does not explicitly teach detecting the set of sensor inputs connected at the communication interface; interrupting the coupling of the sensor inputs to the processor interrupting the coupling of the sensor inputs to the processor, by decoupling the communication interface from a data input of the processor; wherein data is selected based at least in part on the detected sensor inputs; wherein the method further comprises interrupting a communication between one or more components of a medical facility monitoring network.
Holcomb makes obvious interrupting the coupling of the sensor inputs to the processor interrupting the coupling of the sensor inputs to the processor, by decoupling the communication interface from a data input of the processor; ([Par 20-21] “As further shown in FIG. 2, demonstration source multiplexor 209 selectively switches between the analog signal from digital-to-analog converter 205 and the serial demonstration patterns from serial demonstration generator 219, and provides the switched output to demonstration analog processor 213… The digital signals are connected from the MSO channel block 150 to MSO demonstration (demo) multiplexor 221 of demonstration processing circuit 200. MSO demonstration multiplexor 221 selectively switches between the stimulus signals provided from demonstration signal generators 201 and 203 and the digital signal from MSO channel block 150, and provides the switched output to acquisition memory and trigger circuit 121.”)
Holcomb is analogous art because it is within the field of enabling simulation/demonstration modes on monitoring devices. It would have been obvious to one of ordinary skill in the art to combine Holcomb with Duval-Arnould before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to enable simulation modes within the device without requiring additional hardware. As noted by Holcomb, simulation/demonstration modes for many devices require additional hardware and controllers, which can be expensive and cumbersome to operate ([Par 2-3] “To demonstrate the capabilities of an oscilloscope to a potential customer during a sales process, a separate demonstration (demo) board may be manually connected to the oscilloscope and used as a signal source to provide multiple signal types for display. For complex signal types, it may be necessary to connect various external cables from the demo board to the oscilloscope. The oscilloscope must also be properly configured. In some instances, the demo boards may be reconfigurable. Demo boards may be expensive, which can be cost prohibitive because the demo boards are usually distributed to sales staff. Also, because of the complexity of oscilloscopes, it can be difficult to properly train and inform customers of the various oscilloscope operational modes. Written manuals may be provided to walk customers through self-training steps which may include connecting signals from a separate demo board or signal source to the oscilloscope inputs. In either case of demonstration or training, additional equipment is typically needed, increasing cost, time and difficulty. There is thus a need to demonstrate an oscilloscope without the use of a separate demo board. There is also a need to provide customer training using a broad set of waveform types without the use of a separate demo board or signal source.”) To this end, Holcomb presents a system including an integrated selectable simulation/demonstration mode capable of generating simulated signals without the necessity of external hardware ([Abstract] “An oscilloscope includes at least one demonstration signal generator integrated as part of the oscilloscope. The demonstration signal generator generates stimulus signals that consist of digital samples of various different stored waveforms without the need of a separate demonstration board or signal source. The demonstration signal generator may loop through different sections of the stored waveforms to generate respective stimulus signals that include sequences of digital samples from the different waveforms in combination, to provide a broad range of stimulus signals. The stimulus signals may be displayed on the oscilloscope or output from the oscilloscope as demonstration mode stimulus signals to demonstrate the capabilities of the oscilloscope to customers or for training.” [Par 20] “As further shown in FIG. 2, demonstration source multiplexor 209 selectively switches between the analog signal from digital-to-analog converter 205 and the serial demonstration patterns from serial demonstration generator 219, and provides the switched output to demonstration analog processor 213.”) Although Holcomb describes these issues and the associated technical solution within the context of oscilloscopes, it would have been easily recognized by one of ordinary skill in the art that the same issues present in the field of medical simulation (requirement of external, sometimes expensive and cumbersome hardware to perform simulation) could be solved in an identical way through the system of Holcomb (i.e. a selectively enabled, integral simulation generator.) Overall, one of ordinary skill in the art would have recognized that combining Duval-Arnould with Holcomb would allow the simulated signals to be generated within the medical device itself, requiring less hardware to perform simulations and therefore making the system simpler to use.
The combination of Duval-Arnould and Holcomb does not explicitly teach detecting the set of sensor inputs connected at the communication interface; wherein data is selected based at least in part on the detected sensor inputs; wherein the method further comprises interrupting a communication between one or more components of a medical facility monitoring network.
Kiani makes obvious detecting the set of sensor inputs connected at the communication interface; wherein data is selected based at least in part on the detected sensor inputs; ([Par 160] “The patient monitor 740 also includes a journal module 746 in the depicted embodiment. The journal module 740 may record medical events related to the patient monitor 740. These medical events can include clinician-initiated events, such as changes to alarm settings (e.g., maximum and minimum permitted parameter values), types of parameters monitored/sensors connected to the patient monitor 740, and the like.” [Par 153] “The monitoring module 742 can monitor physiological signals generated by one or more sensors coupled with a patient. The monitoring module 742 may process the signals to determine any of a variety of physiological parameters” [Par 74] “ Example alerts include no communication with pulse oximeter, alarm silenced on pulse oximeter, instrument low battery (pulse oximeter), and transmitter low battery. Example alarms include SpO.sub.2 levels and alarms, high and low SpO.sub.2, high and low PR, HbCO level and alarms, HbMET level and alarms, pulse rate and alarms, no sensor, sensor off patient, sensor error, low perfusion index, low signal quality, HbCO, HbMET, PI trend alarm, and desat index alarm.”) wherein the method further comprises interrupting a communication between one or more components of a medical facility monitoring network. ([Par 89] “In another embodiment (not shown), end user devices 128, 152 include one way POCSAG Pagers having a 2 line display with audible and vibrate mode, of suitable size and durability for severe mechanical environments typical of hospital general floor settings. In yet another embodiment, the end user devices 128, 152 include two way paging systems, such as Motorola Flex and WLAN pagers. One advantage of two-way paging is the ability to confirm message receipt and the ability to remotely silence alarms.”)
Kiani is analogous art because it is within the field of medical systems, particularly the field of medical device and sensor interconnection. It would have been obvious to one of ordinary skill in the art to combine it with Duval-Arnould and Holcomb before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to make the system easier to use. Kiani notes that the graphical form of many patient monitoring systems can make them hard to read and further makes focusing on certain pieces of important extremely difficult. ([Par 183] “Currently available graphical user interfaces for nurses' station computers tend to show a plurality of wave forms or changing physiological parameter numbers for each patient. This method of displaying patient information can be cluttered, confusing, and even hypnotic in some situations. Nurses working on a night shift, for instance, may find it difficult to concentrate on an alarm when several other patients' indicators on the display have changing numbers, changing waveforms, or the like.”) To that end, Kiani presents a system that simplifies the monitoring GUI, allowing important information to be understood at a glance ([Par 84] “Moreover, the graphical status indicator 914 simplifies the first level of analysis that nurses tend to perform. In currently available devices, nurses often have to analyze waveforms at the nurses' station to determine the health status of a patient. However, using the screens 900, a nurse need not interpret any waveforms or changing parameters of the patient, but instead can rely on the graphical status indicator 914”) Overall, one of ordinary skill in the art would have recognized that combining Kiani with Duval-Arnould and Holcomb would make the system easier to use, particularly in longer simulations or simulations late at night when trainees may be fatigued.
Claim 2. Duval-Arnould teaches wherein the clinical parameters simulated by the simulation signals include at least each of the clinical parameters of the ([Col 3 line 33-64] “FIG. 1 illustrates a schematic diagram of an exemplary system and method according to an embodiment of the present invention. As illustrated in FIG. 1, step 1 includes generating a signal to replace output from a typical clinical sensor. The typical clinical sensor output signal can be replaced in a variety of different ways with various simulated outputs. Examples of such simulated outputs include but are not limited to a simulator control signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. Also as illustrated in FIG. 1, Step 2 includes encoding the simulated signal output to follow sensor-receiver protocols, if necessary. The computing device or non-transitory computer readable medium can also be used to complete this step. In step 3, the simulated signal is sent to the monitor/device. The non-transitory computer readable medium can be used to export the signal to a hardware component configured to communicate with both the non-transitory computer readable medium and the clinical monitor, medical device or other device used in a clinical setting and known to one of skill in the art. The monitor or device receives the signal and can display it in accordance with the typical operation of that particular machine” [Col 1 line 55 -Col 2 line 3] “The input signal takes the form of at least one selected from a group consisting of a simulator controller signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. The non-transitory computer readable medium can be configured to encode the input signal to follow sensor-receiver protocols. The non-transitory computer readable medium can also be configured to change or adapt the input signal in real-time. The non-transitory computer readable medium is configured to simulate the output signal from at least one of the sensors selected from a group consisting of end tidal CO2 (ETCO2), pulse oximetry (SPO2), thermometer, blood pressure, quality of CPR (QCPR), and near-infrared spectroscopy (NIRS) sensors. The system can also include an interface for a user to input parameters into the system and a patient simulator.”)
Kiani makes obvious detected sensor inputs ([Par 160] “The patient monitor 740 also includes a journal module 746 in the depicted embodiment. The journal module 740 may record medical events related to the patient monitor 740. These medical events can include clinician-initiated events, such as changes to alarm settings (e.g., maximum and minimum permitted parameter values), types of parameters monitored/sensors connected to the patient monitor 740, and the like.”)
Claim 3. Duval-Arnould teaches wherein coupling the simulation signals comprises communicating with a datastore, the datastore storing reference simulation data for ([Col 3 line 39-49] “Examples of such simulated outputs include but are not limited to a simulator control signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal… The simulated signal can be produced or stored using a computing device or non-transitory computer readable medium associated with the system.” [Col 4 line 31-Col 5 line 3] “All inputs can be encoded and sent to a clinical monitor/defibrillator or can be saved in simple XML format for later use. Files saved in XML format can later be sent to a clinical monitor/defibrillator using a C# software application. Prototypes have been developed and successfully tested for all inputs described below… 2. User Input through Separate User Interface A simulated ETCO2 waveform can be generated via our C# waveform generator that creates a continuous waveform of interchanging exponential rise and decay based on set parameters. This simulated waveform can be sampled to retrieve a stream of ETCO2 waveform data points; these data points will be encoded using our encoding software. 3. Retrieval of ETCO2 Waveform from Zoll CodeNet Record Zoll R-Series defibrillators, which are currently used in all pediatric centers in the Johns Hopkins Hospital and are in the process of being phased into adult centers, generate a CodeNet report each time the defibrillator is powered on. These reports include the ETCO2 waveform for the entire event, which is logged from the CAPNOSTAT5 ETCO2 sensor that plugs into the Zoll R-Series defibrillator. The waveform and corresponding respiratory rates from the Zoll CodeNet report could be used as numeric input for the encoding software.”)
Kiani makes obvious a datastore storing a plurality of different signals. ([Par 133] “In certain embodiments, systems and methods are provided for rapidly storing and acquiring physiological trend data. For instance, physiological information obtained from a medical patient can be stored in a round-robin database. The round-robin database can store the physiological information in a series of records equally spaced in time. Parameter descriptors may be used to identify parameter values in the records. The parameter values can be dynamically updated by changing the parameter descriptors to provide for a flexible database. In addition, the size of files used in the database can be dynamically adjusted to account for patient condition.” [Par 377] “The bedside patient monitors may then output an indication of a physiological parameter value (e.g., SpO2, pulse rate, blood pressure, etc.) and its trending over time. Physiological information such as the raw physiological signals, processed physiological signals, and/or calculated physiological parameter values, for example, for each of the patients can then be transmitted to, and stored by, for example, a central repository. In some embodiments, this information is stored by a networked database such as, for example, the round-robin database 722 described herein. In some embodiments, the central repository can store medical monitoring information for the patients in a particular domain (e.g., a hospital ward) over a period of time such as a week, or a month, for example.”)
Claim 4. Duval-Arnould teaches wherein the reference simulation data includes historical sensor signal data recorded for one or more patients over a time window. ([Col 3 line 39-43] “Examples of such simulated outputs include but are not limited to … a real patient waveform recorded by a clinical monitor or defibrillator” [Col 4 line 31 – Col 5 line 35] “All inputs can be encoded and sent to a clinical monitor/defibrillator or can be saved in simple XML format for later use. Files saved in XML format can later be sent to a clinical monitor/defibrillator using a C# software application. Prototypes have been developed and successfully tested for all inputs described below… 3. Retrieval of ETCO2 Waveform from Zoll CodeNet Record Zoll R-Series defibrillators, which are currently used in all pediatric centers in the Johns Hopkins Hospital and are in the process of being phased into adult centers, generate a CodeNet report each time the defibrillator is powered on. These reports include the ETCO2 waveform for the entire event, which is logged from the CAPNOSTAT5 ETCO2 sensor that plugs into the Zoll R-Series defibrillator. The waveform and corresponding respiratory rates from the Zoll CodeNet report could be used as numeric input for the encoding software. … 6. Digitized Waveform Custom C# software has been developed to convert JPG and BMP images that contain a clinical waveform (ETCO2, pulse oximeter, ECG) to a digital stream of data points to be sent as an input to a clinical monitor or defibrillator. This digital stream of data points can be directly sent to encoding software and, then, to a clinical monitor/defibrillator, or the data points can be saved in a simple XML format. Using the waveform digitizer, inputs to the defibrillator/clinical monitor can now include hand-drawn waveforms and printed patient records.”)
Claim 6. . Duval-Arnould teaches wherein the medical device is a patient monitor device, ([Col 1 line 52-54] “In accordance with an aspect of the present invention the clinical device can take the form of at least one selected from a group consisting of a clinical monitor and a defibrillator.”) a
Kiani makes obvious a patient monitor device adapted to monitor at least one of a fetal heart rate or a maternal heart rate. ([Par 153-154] “The monitoring module 742 can monitor physiological signals generated by one or more sensors coupled with a patient. The monitoring module 742 may process the signals to determine any of a variety of physiological parameters. For example, the monitoring module 742 can determine physiological parameters such as pulse rate … In addition, the monitoring module 742 may obtain physiological information from acoustic sensors in order to determine respiratory rate, inspiratory time, expiratory time, inspiration-to-expiration ratio, inspiratory flow, expiratory flow, tidal volume, minute volume, apnea duration, breath sounds, rales, rhonchi, stridor, and changes in breath sounds such as decreased volume or change in airflow. In addition, in some cases the monitoring module 742 monitors other physiological sounds, such as heart rate (e.g., to help with probe-off detection), heart sounds (e.g., S1, S2, S3, S4, and murmurs), and changes in heart sounds such as normal to murmur or split heart sounds indicating fluid overload. Moreover, the monitoring module 742 may monitor a patient's electrical heart activity via electrocardiography (ECG) and numerous other physiological parameters.” [Examiner’s note: a monitoring device “adapted to monitor … a maternal heart rate” is merely a normal heart rate/pulse monitor, i.e. a monitor that can detect the heart rate of a normal adult, such as an ECG. Further note that removing the alternative and requiring the monitoring of a fetal heart rate in the claim would likely overcome this rejection.])
Claim 7. Holcomb teaches wherein the method comprises receiving a control input ([Par 21] “The digital signals are connected from the MSO channel block 150 to MSO demonstration (demo) multiplexor 221 of demonstration processing circuit 200. MSO demonstration multiplexor 221 selectively switches between the stimulus signals provided from demonstration signal generators 201 and 203 and the digital signal from MSO channel block 150, and provides the switched output to acquisition memory and trigger circuit 121.” [Par 25-26] “Referring to FIG. 3, the demonstration signal generator, such as demonstration signal generators 201 and 203, includes counting circuit (counter) 302 and stimulus memory 304 that together may function effectively as a direct digital synthesis (DDS) generator. Stimulus memory 304 stores digitized values of various different waveforms, such as sine waves, square waves, ramps, DC, noise, glitches, runt pulses, modulated waveforms and serial data. Under control of computer 170, counting circuit 302 generates and provides addresses to stimulus memory 304. Computer 170 may operate in response to user selection. … For example, counting circuit 302 and stimulus memory 304 may be controlled to function in a ping-pong mode to generate a stimulus signal consisting of a sequence of digital samples that alternate between digitized values of different stored waveforms. That is, under control of computer 170, a pair of start-stop points of different stored waveforms may be designated along with a frame count. The ping-pong mode is useful for generating stimulus signals having narrow, low frequency pulses and for generating stimulus signals having infrequent events such as glitches. Computer 170 may be programmed by user selection to provide a sequence of instructions to counting circuit 302, such as the following: [0027] Start1=0 [0028] Stop1=1000 [0029] Frame1=10 [0030] Start2=2000 [0031] Stop2=3000 [0032] Frame2=1.” [Examiner’s note: this counting circuit controls how the simulated data is generated])
Kiani makes obvious input from an external device ([Par 270] “As disclosed herein, the clinician tokens 2022, 2024 may include an input module (e.g., 1416). One use for this input module is to remotely disable an alarm once the clinician has received notification of the alarm and is en route to the patient.”)
Claim 12. Duval-Arnould teaches A non-transitory computer-readable medium comprising executable instructions, configured when run on a processor to cause the processor to perform the method according to claim 1. ([Col 1 line 40-51] “ The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device.”)
Claim 13. The elements of claim 13 are substantially the same as those of claim 1. Therefore, the elements of claim 13 are rejected due to the same reasons as outlined above for claim 1. Further, Duval-Arnould makes obvious the additional elements of: A controller, arranged for communicating with a medical device, for implementing a simulation mode on the medical device, ([Abstract] “The present invention is directed to systems for interfacing between sensors and sensor simulators and clinical monitors and devices. The present invention is used to incorporate sensors and sensor simulators into training and clinical demonstrations.” [Col 1 line 40-51] “ The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device.”) the medical device configured in a normal operating mode to receive a set of one or more sensor inputs at a communication interface, each carrying a signal representative of a clinical parameter, supply the sensor inputs to a processor, and to generate a user output using the processor, ([Col 1 line 20-23] “Clinical monitors and smart defibrillators interact with patients, using a number of different sensors to collect necessary clinically relevant physiological findings and measurements of provider performance.”) the controller configured to: communicate with the medical device to cause the medical device to … ([Abstract] “The present invention is directed to systems for interfacing between sensors and sensor simulators and clinical monitors and devices. The present invention is used to incorporate sensors and sensor simulators into training and clinical demonstrations.” [Col 1 line 40-51] “ The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device.”)
Claim 14. Duval-Arnould teaches wherein the controller is integrated in a peripheral hardware module, the hardware module comprising a connection interface, and adapted to operatively couple to the medical device via the connection interface. ([Col 1 line 40-51] “ The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device.”)
(2) Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Duval-Arnould (US 10580324 B2) in view of Holcomb (US 20120274313 A1) in further view of Kiani (US 20110105854 A1) as well as Cadwell (US 20200160741 A1)
Claim 5. Duval-Arnould teaches wherein the reference simulation data ([Col 3 line 39-43] “Examples of such simulated outputs include but are not limited to … a real patient waveform recorded by a clinical monitor or defibrillator” [Col 4 line 31 – Col 5 line 35] “All inputs can be encoded and sent to a clinical monitor/defibrillator or can be saved in simple XML format for later use. Files saved in XML format can later be sent to a clinical monitor/defibrillator using a C# software application. Prototypes have been developed and successfully tested for all inputs described below… 3. Retrieval of ETCO2 Waveform from Zoll CodeNet Record Zoll R-Series defibrillators, which are currently used in all pediatric centers in the Johns Hopkins Hospital and are in the process of being phased into adult centers, generate a CodeNet report each time the defibrillator is powered on. These reports include the ETCO2 waveform for the entire event, which is logged from the CAPNOSTAT5 ETCO2 sensor that plugs into the Zoll R-Series defibrillator. The waveform and corresponding respiratory rates from the Zoll CodeNet report could be used as numeric input for the encoding software. … 6. Digitized Waveform Custom C# software has been developed to convert JPG and BMP images that contain a clinical waveform (ETCO2, pulse oximeter, ECG) to a digital stream of data points to be sent as an input to a clinical monitor or defibrillator. This digital stream of data points can be directly sent to encoding software and, then, to a clinical monitor/defibrillator, or the data points can be saved in a simple XML format. Using the waveform digitizer, inputs to the defibrillator/clinical monitor can now include hand-drawn waveforms and printed patient records.”)
The combination of Duval-Arnould, Holcomb, and Kiani does not explicitly teach wherein simulation data includes a plurality of simulation data subsets, each simulation data subset including data
Cadwell makes obvious wherein simulation data includes a plurality of simulation data subsets, each simulation data subset including data ([Par 13] “generate simulation data indicative of the physiological responses at each channel in the first subset using predefined relationships between the plurality of channels and based on the one or more simulated stimuli; identify a second subset of the plurality of channels from the first subset, wherein each of the channels in the second subset has simulation data indicative of a physiological response that exceeds one or more predefined thresholds;”)
Cadwell is analogous art because it is within the field of medical simulation. It would have been obvious to one of ordinary skill in the art to combine it with Duval-Arnould, Holcomb, and Kiani before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to better train users for a wider array of potential medical operations, particularly neuromonitoring. As mentioned by Cadwell, for particular simulator uses, such as neurological simulations, current simulator systems require specialized hardware that can be difficult to obtain and cannot perform accurate simulation under a variety of scenarios ([Par 5-7] “Currently, there are some training simulators available in the market for providing training to neurodiagnostic and IONM trainees. Simulation is a powerful tool for learning about rare patient events, and about common technical and operational problems, as well as how to run an IONM instrument and perform monitoring effectively. Users requiring training include both technical and medically-trained professionals. However, these training simulators requires the use of hardware including their own IONM devices. The simulators can simulate plug-in errors, but cannot simulate the effects of anesthesia, positioning, temperature, interference from other devices, surgical events and/or comorbidities. Currently available IONM training simulators do not simulate realistic waveforms in their software applications and do not simulate the effect of likely events encountered in a clinical environment on recorded waveforms. For example, latency shifts and amplitude changes in a patient's monitoring data may be caused by environmental factors (and not surgery), such as limb positioning, temperature, and other machines hooked to a patient. Such environmental factors interfere with the currently available simulator's ability to accurately simulate patient data.” [Par 12] “Hence, there is need for a software-based medical training simulator for neurodiagnostic testing and IONM which does not require connection to any neurodiagnostic or IONM hardware, thereby reducing the barrier to access for training centers and individuals. There is also need for a training simulator that provides simulations of a wide range of technical, anesthetic and surgical events likely to be encountered during typical use of the simulator.”) To this end, Cadwell presents a method for accurate, accessible neuromonitoring simulation. ([Par 13] “The present specification discloses a system for simulating a patient's physiological responses to one or more stimuli over a simulation timeframe, wherein the system comprises programmatic instructions stored in a tangible, non-transitory computer readable medium, wherein the programmatic instructions define a plurality of channels, each of said channels being virtually representative of an anatomical site of the patient, and wherein, when executed, the programmatic instructions: identify at least one of the plurality of channels as a stimulation site; identify a first subset of the plurality of channels as reference sites; generate simulation data indicative of the physiological responses at each channel in the first subset using predefined relationships between the plurality of channels and based on the one or more simulated stimuli;” [Par 70] “The present specification provides a software-based medical training simulator for neurodiagnostic testing and intraoperative neurophysiological monitoring. This software simulator differentiates itself from currently available training tools because it does not require connection to any neurodiagnostic or IONM hardware, thereby reducing the barrier to access for training centers and individuals. The software simulator comprises simulations of a wide range of technical, anesthetic, and surgical events likely to be encountered during typical use of the simulator.”) Overall, one of ordinary skill in the art would have recognized that combining Cadwell with Duval-Arnould, Holcomb, and Kiani would result in a system that is capable of performing a wider array of simulations without the need for extensive additional hardware, allowing for a wider application of the system.
(3) Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Duval-Arnould (US 10580324 B2) in view of Holcomb (US 20120274313 A1) in further view of Kiani (US 20110105854 A1) as well as UNI® Control Software for Gaumard® Simulators (Hereinafter Gaumard)
Claim 8. Duval-Arnould teaches ([Col 1 line 40- Col 2 line 3] “The foregoing needs are met, to a great extent, by the present invention which provides a system for interfacing with a clinical device during a simulated training including a non-transitory computer readable medium configured to generate an input signal configured to mimic an output signal from a clinical sensor and encode the input signal into a format recognized by the clinical device. The system also includes a hardware component configured to interface the non-transitory computer readable medium to the clinical device. The hardware component provides communication between the non-transitory computer readable medium and the clinical device. In accordance with an aspect of the present invention the clinical device can take the form of at least one selected from a group consisting of a clinical monitor and a defibrillator. The input signal takes the form of at least one selected from a group consisting of a simulator controller signal, a computer-generated waveform, a real patient waveform recorded by a clinical monitor or defibrillator, and a recorded waveform from an intercepted clinical sensor signal. The non-transitory computer readable medium can be configured to encode the input signal to follow sensor-receiver protocols. The non-transitory computer readable medium can also be configured to change or adapt the input signal in real-time. The non-transitory computer readable medium is configured to simulate the output signal from at least one of the sensors selected from a group consisting of end tidal CO2 (ETCO2), pulse oximetry (SPO2), thermometer, blood pressure, quality of CPR (QCPR), and near-infrared spectroscopy (NIRS) sensors. The system can also include an interface for a user to input parameters into the system and a patient simulator.”)
Holcomb makes obvious wherein the method further includes selectively implementing a ([Par 21] “The digital signals are connected from the MSO channel block 150 to MSO demonstration (demo) multiplexor 221 of demonstration processing circuit 200. MSO demonstration multiplexor 221 selectively switches between the stimulus signals provided from demonstration signal generators 201 and 203 and the digital signal from MSO channel block 150, and provides the switched output to acquisition memory and trigger circuit 121.” [Par 25-26] “Referring to FIG. 3, the demonstration signal generator, such as demonstration signal generators 201 and 203, includes counting circuit (counter) 302 and stimulus memory 304 that together may function effectively as a direct digital synthesis (DDS) generator. Stimulus memory 304 stores digitized values of various different waveforms, such as sine waves, square waves, ramps, DC, noise, glitches, runt pulses, modulated waveforms and serial data. Under control of computer 170, counting circuit 302 generates and provides addresses to stimulus memory 304. Computer 170 may operate in response to user selection. … For example, counting circuit 302 and stimulus memory 304 may be controlled to function in a ping-pong mode to generate a stimulus signal consisting of a sequence of digital samples that alternate between digitized values of different stored waveforms. That is, under control of computer 170, a pair of start-stop points of different stored waveforms may be designated along with a frame count. The ping-pong mode is useful for generating stimulus signals having narrow, low frequency pulses and for generating stimulus signals having infrequent events such as glitches. Computer 170 may be programmed by user selection to provide a sequence of instructions to counting circuit 302, such as the following: [0027] Start1=0 [0028] Stop1=1000 [0029] Frame1=10 [0030] Start2=2000 [0031] Stop2=3000 [0032] Frame2=1.”)
Kiani makes obvious ([Par 270] “As disclosed herein, the clinician tokens 2022, 2024 may include an input module (e.g., 1416). One use for this input module is to remotely disable an alarm once the clinician has received notification of the alarm and is en route to the patient.”)
The combination of Duval-Arnould, Holcomb, and Kiani does not explicitly teach a further simulation mode; wherein in the further simulation mode simulation operations are performed based on input.
Gaumard makes obvious a further simulation mode; wherein in the further simulation mode simulation operations are performed based on input. ([Page 51 Col 1 Par 2 – Col 2 Par 6] “After the startup screen, the profile and operating mode selection menu is displayed. The UNI control software has two modes of operation: Manual and Automatic. Each mode includes a Quick Start profile with preprogrammed scenarios exercises created in conjunction with experienced healthcare instructors and working medical professionals. Continue to the next section to learn more about each operating mode and the profiles included. After selecting an operating mode and profile, click “Load” to continue. In the “Manual” operating mode, the facilitator fully controls the vital signs and physiologic responses. … When first starting out with the simulator, it is recommended that you use the Quick Start profile, which was created in conjunction with experienced healthcare instructors and working medical professionals. The Quick Start profile has applicable Palettes that are useful for simulating common medical emergencies. For many applications, it serves a convenient starting point that can be customized to it most simulation objectives. It Includes a library of predetermined scenarios. The Automatic mode assists the facilitator by automatically adjusting vital signs in response to caregiver participation, pharmacologic intervention, and manual input. For example, when facilitator increases the heart rate, the Auto mode will calculate the response and adjust the blood pressure automatically. To activate the operating mode as an upgrade option, go to “Menu” section”)
Gaumard is analogous art because it is within the field of medical simulation. It would have been obvious to one of ordinary skill in the art to combine it with Duval-Arnould, Holcomb, and Kiani before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to enable greater manual control over the response of the simulated signals. While systems like Duval-Arnould do teach some manual signal control, these methods can be tedious and require the generation of individual sensor waveforms one at a time ([Col 4 line 53-58] “A simulated ETCO2 waveform can be generated via our C# waveform generator that creates a continuous waveform of interchanging exponential rise and decay based on set parameters. This simulated waveform can be sampled to retrieve a stream of ETCO2 waveform data points; these data points will be encoded using our encoding software.” [Col 5 line 26-35] “Custom C# software has been developed to convert JPG and BMP images that contain a clinical waveform (ETCO2, pulse oximeter, ECG) to a digital stream of data points to be sent as an input to a clinical monitor or defibrillator. This digital stream of data points can be directly sent to encoding software and, then, to a clinical monitor/defibrillator, or the data points can be saved in a simple XML format. Using the waveform digitizer, inputs to the defibrillator/clinical monitor can now include hand-drawn waveforms and printed patient records”) To this end, Gaumard presents a system including a manual control mode that allows for the rapid, easy adjustment of vital signs and simulated signals from an easy integrated menu system ([Page 6 Figs. 1-4])
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Further, Duval-Arnould notes the desirability of and easy integration with mannequin control systems such as Gaumard ([Col 4 line 39-51] “All high-technology simulator mannequins have a mannequin-specific software platform that is loaded on a PC or tablet. This software-based user interface can be used to control realistic and clinically measurable mannequin characteristics, such as heart rhythm, and to control non-measurable vital parameters that are projected on a simulator-specific monitor that acts as the bedside monitor in simulated events. The waveform and/or value set for a specific vital sign, such as ETCO2, on the simulator software could be used as an input into the encoding software. This transfer of simulated ETCO2 data from the simulation software to our encoding software is made possible via the use of the SimMan SDK.”) Overall, one of ordinary skill in the art would have recognized that combining Gaumard with Duval-Arnould, Holcomb, and Kiani would result in a system that allowed for a much greater level of user control in an easy and concise manner.
(4) Claims 9 -11 are rejected under 35 U.S.C. 103 as being unpatentable over Duval-Arnould (US 10580324 B2) in view of Holcomb (US 20120274313 A1) in further view of Kiani (US 20110105854 A1) as well as Krishnan (US 20150089590 A1)
Claim 9. Duval-Arnould teaches wherein the method comprises a step of establishing a communication channel w([Col 5 line 13-25] “A simulated ETCO2 waveform that directly reflects the actions of a provider's performance of manual patient ventilation can be generated via our C# waveform generator; the characteristics of this generated waveform, including visual and numeric respiratory rate and ETCO2 amplitude, are dependent upon the output of a ventilation detection sensor. The pressure sensor in this technology outputs to a microcontroller, which provides minimal analysis and wireless communication between the sensor and the waveform generator software on a computer. This input allows for the realistic, automated display of provider performance on the clinical monitor/defibrillator during simulation.”)
Kiani makes obvious establishing a communication channel with the external device, ([Par 212] “The clinician token 1410 can likewise include a communication module 1412, which can be, for example, a transmitter, a receiver, or a transceiver, though other types of communication modules may also be used. As is the case with the patient monitoring device 1400, the communication module 1412 included with the clinician token 1410 may be a short range transceiver, such as, for example, a Bluetooth transceiver. The patient monitoring device 1400 is capable of detecting the presence of a clinician based on, for example, recognition of one or more communication signals from a clinician token 1410. A communication signal from the clinician token 1410 may come, for example, in response to a communication initiated by the patient monitoring device 1400, or the communication signal from the clinician token 1410 may be initiated by the clinician token itself. Many different methods can be used for initiating, for example, wireless communication between remote devices.”)
The combination of Duval-Arnould, Holcomb, and Kiani does not explicitly teach wherein the process comprises exchanging one or more handshake messages.
Krishnan makes obvious wherein the system comprises exchanging one or more handshake messages. ([Par 110] “Another useful option is to use an unknown EP temporarily only until the session lasts. In this case, the secret code entered by the patient is called session secret or session secret code, as it lasts only for that session. Patient enters the same session secret in EP and the patient-Smartphone or other mobile device. Again, MD5 hash value of 128-bit is derived from the session secret and converted into AES standard key. The key is then saved into the device's corresponding database. As shown in FIG. 2, as soon as the session key is generated from the secret code, Smartphone or other mobile device sends out temporary key, K.sub.S for EP and K.sub.S for ICD inside the TicketICD, appended with Smartphone or other mobile device ID, a nonce, N1, encrypted with pre-shared secret key, K.sub.EP. The EP sends the TicketICD appended with a challenge timestamp T that is encrypted with, K.sub.S. The ICD extracts the key K.sub.S from the TicketICD and responds back to the EP with timestamp T+1, encrypted with the temporary session key, K.sub.S. The EP can request information from the ICD after this handshake.”)
Krishnan is analogous art because it is within the field of medical data processing. It would have been obvious to one of ordinary skill in the art to combine it with Duval-Arnould, Holcomb, and Kiani One of ordinary skill in the art would have been motivated to make this combination in order to better secure data, particularly important patient data when basing simulations on such. As noted by Krishnan, providing security for healthcare operations has become more and more complex in recent years ([Par 2] “Information security in the field of health care has become an increasingly complex and important topic in recent years. There has been a thorough investigation regarding threats and vulnerabilities to patients with implantable medical devices, and new design solutions have been described. Information security in the field of healthcare has gained a new urgency and need, and there has been tremendous development in healthcare-related communication networking and information security.”) Logically, this increase in complexity can lead to an increase in the chance of security violations. To this end, Krishnan presents a method for secure communication between medical devices and other networked devices ([Par 34] “ This application describes a computer security protocol by introducing a Smartphone or other mobile device, which acts as a security management "hotspot" between External Programmer (EP) and Implantable Cardiac Defibrillator (ICD), using symmetric key cryptography. The Smartphone or other mobile device will only allow access to registered programmers or allow unregistered ones with the patient's consent. Several scenarios are described where Smartphone or other mobile device plays a central role in access control including the emergency case.”) While Krishnan envisions this security system in the context of connections with implantable devices such as pacemakers, one of ordinary skill in the art would have recognized that this security protocol would work identically between other devices, and that combining Krishnan with Duval-Arnould, Holcomb, and Kiani would result in a system that is significantly more secure.
Claim 10. Krishnan teaches wherein the one or more handshake messages include authentication information for establishing a secure communication channel. ([Par 110] “Another useful option is to use an unknown EP temporarily only until the session lasts. In this case, the secret code entered by the patient is called session secret or session secret code, as it lasts only for that session. Patient enters the same session secret in EP and the patient-Smartphone or other mobile device. Again, MD5 hash value of 128-bit is derived from the session secret and converted into AES standard key. The key is then saved into the device's corresponding database. As shown in FIG. 2, as soon as the session key is generated from the secret code, Smartphone or other mobile device sends out temporary key, K.sub.S for EP and K.sub.S for ICD inside the TicketICD, appended with Smartphone or other mobile device ID, a nonce, N1, encrypted with pre-shared secret key, K.sub.EP. The EP sends the TicketICD appended with a challenge timestamp T that is encrypted with, K.sub.S. The ICD extracts the key K.sub.S from the TicketICD and responds back to the EP with timestamp T+1, encrypted with the temporary session key, K.sub.S. The EP can request information from the ICD after this handshake.”)
Claim 11. Duval-Arnould teaches wherein the further communication interface includes a local wireless network interface, and ([Col 5 line 13-25] “A simulated ETCO2 waveform that directly reflects the actions of a provider's performance of manual patient ventilation can be generated via our C# waveform generator; the characteristics of this generated waveform, including visual and numeric respiratory rate and ETCO2 amplitude, are dependent upon the output of a ventilation detection sensor. The pressure sensor in this technology outputs to a microcontroller, which provides minimal analysis and wireless communication between the sensor and the waveform generator software on a computer. This input allows for the realistic, automated display of provider performance on the clinical monitor/defibrillator during simulation.”)
Kiani makes obvious wherein the communication channel is facilitated by a local wireless network server comprising a wireless local area network (WLAN) server. ([Par 86] “In one embodiment, the journaling function of the server 136 constitutes a transaction-based architecture. Certain transactions of the physiological monitoring system 100 are journaled such that a timeline of recorded events may later be re-constructed to evaluate the quality of healthcare given. These transactions include state changes relating to physiological information from the patient monitoring devices 100, to the patient monitoring devices 110, to the hospital WLAN 126 connection, to user operation, and to system behavior.” [Par 77-78] “The network interface module 106 manages the connectivity overhead for initiating and maintain connectivity with end user devices over the shared network. In certain embodiments, the network interface module 106 manages connectivity by acting as a microserver or web server. In such instances, the network interface module 106 is a network connection enabled device. As a web server, the network interface module 106 establishes direct connections to the Internet 150, such that an end user may access web pages stored on the storage device 114 of the network interface module 106. … the network interface module 106 sends data over the shared network through an access point 124 or other wireless or wired transmitter. Alternatively, the network interface module 106 may communicate physiological information directly to end users over the Internet 150. End users such as clinicians carrying notifier devices, e.g., end user devices 128, 152 connected to the hospital WLAN 126 may receive real-time viewing of physiological patient parameters and waveforms on demand or in the event of an alarm or alert.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael P Mirabito whose telephone number is (703)756-1494. The examiner can normally be reached M-F 10:30 am - 6:30 pm.
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/M.P.M./ Examiner, Art Unit 2187
/EMERSON C PUENTE/ Supervisory Patent Examiner, Art Unit 2187
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