DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 6, 8-9, 13-15, 18, and 28-29 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mallas (US 20160278711 A1).
Regarding claim 8, Mallas discloses a method for monitoring a patient's physical condition (abstract), the method comprising receiving an acquired impedance respiration signal associated with the patient's cardiac activity ([0007]: “receiving a stream of respiration samples of a sensed respiration signal that collectively characterize respiration data for a patient. In addition, heart rate data is received that specifies a heart rate for the patient that is measured concurrently with the sensed respiration signal.”); and applying a non-linear, heart rate adaptive, cardiac artifact filter to obtain a filtered impedance respiration signal ([0007]: “Each current respiration sample in the stream is continuously adaptively filtered to result in a corresponding filtered respiration signal that removes cardiac artifacts. The filtering subtracts an earlier respiration sample having a delay equal to a period corresponding to the heart rate of the patient from the then current respiration sample.”).
Regarding claim 9, Mallas discloses further comprising displaying the filtered impedance respiration signal (Fig 4).
Regarding claim 13, Mallas discloses the acquired impedance respiration signal comprises an electrocardiogram ("ECG") signal ([0057]: “In particular, ECG and IR data from Lead I were collected from a human subject using a patient monitor”).
Regarding claim 14, Mallas discloses wherein; the acquired impedance respiration signal comprises an acquired electrocardiogram (“ECG”) signal ([0006]: “which uses ECG information in order to remove the cardiac artifact from the measured signal and to prevent such false detections”); and applying the non-linear, heart rate adaptive, cardiac artifact filter to obtain the filtered impedance respiration signal includes: determining a varying heart rate of a monitored patient from the acquired electrocardiogram (“ECG”) signal ([0007]: “heart rate data is received that specifies a heart rate for the patient that is measured concurrently with the sensed respiration signal”); acquiring an impedance respiration signal ([0009]: “The respiration signal can comprise an impedance respiration waveform”); and removing a frequency component from the acquired impedance respiration signal (Fig 4), the frequency component corresponding to the varying heart rate ([0053]: “The power of the cardiac artifact can be estimated by integrating the power density of the respiration waveform across a frequency region centered on the heart rate as computed by the ECG”), to smooth the impedance respiration signal and filter out at least one cardiac artifact (Fig 4).
Regarding claim 6, Mallas discloses analyzing the filtered impedance respiration signal for patient conditions ([0013]: “The adaptive filtering can be deactivated when the heart rate data indicates a ventricular arrhythmia.”, [0057]: “where the subject is experiencing shallow breathing.”)
Regarding claim 15, Mallas discloses A physiological monitoring device (abstract), comprising: a processor-based resource ([0014]: “at least one programmable data processor forming part of at least one computing device”; and a memory encoded with instructions that, when executed by the processor-based resource, performs a method comprising ([0018]: “The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein”): receiving an acquired impedance respiration signal associated with the patient's cardiac activity ([0007]: “receiving a stream of respiration samples of a sensed respiration signal that collectively characterize respiration data for a patient. In addition, heart rate data is received that specifies a heart rate for the patient that is measured concurrently with the sensed respiration signal.”); and applying a non-linear, heart rate adaptive, cardiac artifact filter to obtain a filtered impedance respiration signal ([0007]: “Each current respiration sample in the stream is continuously adaptively filtered to result in a corresponding filtered respiration signal that removes cardiac artifacts. The filtering subtracts an earlier respiration sample having a delay equal to a period corresponding to the heart rate of the patient from the then current respiration sample.”).
Regarding claim 18, Mallas discloses a system for physiologically monitoring a patient, the system comprising (abstract), comprising: a plurality of electrocardiogram (“ECG”) sensors ([0031]: “an ECG circuit 170 that directly or indirectly receives the outputs of the electrodes 160, 162, 164.”); a physiological monitoring device communicating with the plurality of ECG sensors, the physiological monitor performing a method comprising ([0030]: “interface 150 that permits for wired or wireless communication with one or more electrodes 160, 162, and 164 and/or a remote medical device and/or a remote computing system or network to transmit/receive data”): receiving an acquired impedance respiration signal associated with the patient's cardiac activity ([0007]: “receiving a stream of respiration samples of a sensed respiration signal that collectively characterize respiration data for a patient. In addition, heart rate data is received that specifies a heart rate for the patient that is measured concurrently with the sensed respiration signal.”); and applying a non-linear, heart rate adaptive, cardiac artifact filter to obtain a filtered impedance respiration signal ([0007]: “Each current respiration sample in the stream is continuously adaptively filtered to result in a corresponding filtered respiration signal that removes cardiac artifacts. The filtering subtracts an earlier respiration sample having a delay equal to a period corresponding to the heart rate of the patient from the then current respiration sample.”).
Regarding claim 28, Mallas discloses the acquired impedance respiration signal comprises an electrocardiogram ("ECG") signal ([0057]: “In particular, ECG and IR data from Lead I were collected from a human subject using a patient monitor”).
Regarding claim 29, Mallas discloses the acquired impedance respiration signal comprises an acquired electrocardiogram ("ECG") signal([0057]: “In particular, ECG and IR data from Lead I were collected from a human subject using a patient monitor”).; and applying the non-linear, heart rate adaptive, cardiac artifact filter to obtain the filtered impedance respiration signal includes: determining a varying heart rate of a monitored patient from the acquired electrocardiogram ("ECG") signal ([0007]: “heart rate data is received that specifies a heart rate for the patient that is measured concurrently with the sensed respiration signa”); acquiring an impedance respiration signal; and removing a frequency component from the acquired impedance respiration signal, the frequency component corresponding to the varying heart rate, to smooth the impedance respiration signal and filter out at least one cardiac artifact (Fig 6).
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.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mallas in view of Lou et al. (US 20160043704 A1).
Regarding claim 2, Mallas discloses wherein: the frequency component corresponding to the varying heart rate constitutes noise in the impedance respiration signal ([0056]: “the frequency components of both the true respiration signal and the cardiac artifact can be seen”); and removing the frequency component from the impedance respiration signal comprises, on a sample-by-sample basis: determining a present estimate of the noise and a past estimate of the noise ([0037-0038]: “where rn is the current respiration sample and is an older sample that occurred N past, where N samples in the is the period of the ECG. The theoretical foundation of the filter follows.”).
Mallas fails to disclose estimating the next estimate of the noise from the present estimate and the past estimate; applying an adaptive filtering adjustment factor to the next estimate of the noise, the adaptive filtering adjustment factor having a scaled value of the error estimate limited to a maximum value of 1 to obtain an adjusted next estimate of the noise; and subtracting the adjusted next estimate of the noise from the next sample of the impedance respiration signal.
Lou discloses estimating the next estimate of noise from a present estimate and a past estimate (0061. In the above, h>0 is a factor to control the parameter update speed and W(k) is the estimation of the unknown parameter W at the k-th sample, in other words: W(k)-w(k) 0 007, and x(k)=C&(k). 0062. When the adaptive observer model 320 is expanded, the noise component of a particular sample” see k+1 and k-1 within the disclosed formulas); applying an adaptive filtering adjustment factor to the next estimate of the noise, the adaptive filtering adjustment factor having a scaled value of the error estimate limited to a maximum value of 1 to obtain an adjusted next estimate of the noise ([0075]: “frequency identification,
outputs a binary value based on the filtered signal according
to the following function:”); and subtracting the adjusted next estimate of the noise from the next sample of the impedance respiration signal ([0067]: “by subtracting an estimated noise 230 from a noisy ECG signal 220.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Mallas to include the adaptive filtering method disclosed by Lou in order to improve the accuracy of the obtained signal by accounting for unknown parameters (Lou [0005]).
Regarding claim 3, Lou further discloses wherein the adjusted next estimate of the noise is mathematically represented as:
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n≡the sample number; T≡the sampling period; and d≡the error estimate (paras [0062-0063], [0068], and [0075], Table 1).
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mallas in view of Lou in further view of Durnin (US 20190282125 A1).
Regarding claim 4, Mallas as modified by Lou discloses the method of claim 2, and Lou further discloses predicting noise with respect to the current and previous sample point ([0062-0063], [0068]), but fails to disclose
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Durnin discloses the formula
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([0073], equation 7, see wherein beta is taken with respect to breath frequency and pulse rate).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the formula disclosed by Mallas as modified by Lou to include the adjustment of the noise as disclosed by Durnin in order to improve the accuracy of the calculated signal (Durnin [0020]).
Regarding claim 5, Durnin discloses Fs is about 0.1 Hz ([0026]: “0.1 Hz (e.g., a period of about 10 seconds”). However, Durnin does not explicitly disclose the Fs being between 0.25 – 5 Hz. It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the rate of Fs from between 0.25-5 as applicant appears to have placed no criticality on the claimed range ([0007]: “for example, 15 to 300 bpm (“beats per minute’), or, 0.25-5 Hz, although these numbers may vary in a given case.”) and since it has been held that “[i]n the case where the claimed ranges ‘overlap or lie inside ranges disclosed by the prior art' a prima facie case of obviousness exists”. In re Wertheim, 541 F.2d 257, 191 USPQ 90 (CCPA 1976); In re Woodruff, 919 F.2d 1575, 16 USPQ2d 1934 (Fed. Cir. 1990).
Claim(s) 7, 12, 27, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mallas in view of Pering (US 5503160 A).
Regarding claim 7, Mallas discloses the method of claim 6, but fails to disclose analyzing the filtered impedance respiration signal includes detecting an alarm condition; and the method further comprises issuing an alarm.
Pering discloses analyzing a filtered impedance respiration signal includes detecting an alarm condition (claim 10); and the method further comprises issuing an alarm (claim 11).
As Mallas discloses application of their method in alarm systems ([0062]: “the filter successfully prevents the monitor from detecting false respiration rates and from issuing false alarms caused by the cardiac artifact.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to further modify the method disclosed by Mallas to include activation of an alarm as disclosed by Pering to allow for timely monitoring of critical conditions.
Regarding claim 12, Mallas discloses the method of claim 6, but fails to disclose analyzing the filtered impedance respiration signal includes detecting an alarm condition; and the method further comprises issuing an alarm.
Pering discloses analyzing a filtered impedance respiration signal includes detecting an alarm condition (claim 10); and the method further comprises issuing an alarm (claim 11).
As Mallas discloses application of their method in alarm systems ([0062]: “the filter successfully prevents the monitor from detecting false respiration rates and from issuing false alarms caused by the cardiac artifact.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to further modify the method disclosed by Mallas to include activation of an alarm as disclosed by Pering to allow for timely monitoring of critical conditions.
Regarding claim 27, Mallas discloses the device of claim 15, but fails to disclose analyzing the filtered impedance respiration signal includes detecting an alarm condition; and the method further comprises issuing an alarm.
Pering discloses analyzing a filtered impedance respiration signal includes detecting an alarm condition (claim 10); and the method further comprises issuing an alarm (claim 11).
As Mallas discloses application of their method in alarm systems ([0062]: “the filter successfully prevents the monitor from detecting false respiration rates and from issuing false alarms caused by the cardiac artifact.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to further modify the method disclosed by Mallas to include activation of an alarm as disclosed by Pering to allow for timely monitoring of critical conditions.
Regarding claim 30, Mallas discloses the device of claim 15, but fails to disclose analyzing the filtered impedance respiration signal includes detecting an alarm condition; and the method further comprises issuing an alarm.
Pering discloses analyzing a filtered impedance respiration signal includes detecting an alarm condition (claim 10); and the method further comprises issuing an alarm (claim 11).
As Mallas discloses application of their method in alarm systems ([0062]: “the filter successfully prevents the monitor from detecting false respiration rates and from issuing false alarms caused by the cardiac artifact.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to further modify the method disclosed by Mallas to include activation of an alarm as disclosed by Pering to allow for timely monitoring of critical conditions.
Claim(s) 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mallas in view of Shadid et al. (US 20210098093 A1).
Regarding claim 19, Mallas discloses the system of claim 18 and further discloses disclose a computing system communicating with the physiological monitoring device ([0065]: “A client and server are generally remote from each other and typically interact through a communication network.”). However, they fail to disclose a records repository further comprising a plurality of electronic medical records ("ERMs") communicating with the physiological monitoring device through the computing system; wherein the physiological monitoring device:pushes information to at least one of the ERMs; and pulls information from at least one of the ERMs, which may be the same or different from the ERM to which the physiological monitoring device pushes information.
Shadid discloses a records repository further comprising a plurality of electronic medical records ("ERMs") communicating with the physiological monitoring device through the computing system ([0019]: “a computing device configured to…establish a medical record data”); wherein the physiological monitoring device: pushes information to at least one of the ERMs ([0103]: “and a resources module 630 similar to the IHM module 302, 302a and the databases 306(2) and 306(3) and configured to store and update medication information for the patient”); and pulls information from at least one of the ERMs, which may be the same or different from the ERM to which the physiological monitoring device pushes information ([0019]: “access information about a treatment related to the medical record data; obtain biometric data related to the treatment;”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Mallas to include the ERMs disclosed by Shadid in order to aid in identifying trends in patient data (Shadid [0003]).
Regarding claim 20, Shadid further discloses further comprising: a central monitoring station communicating with the physiological monitoring device through the computing system ([0132]: “The IHM module 302, 302a may continue to monitor the biometric data of the mother and transmit the data to the delivery facility and personal in real time.”); wherein a caregiver monitors the patient's condition from the central monitoring station ([0132]: “a monitors pregnancies and allows for remote monitoring of a mother and her baby via the monitoring module”).
Regarding claim 21, Shadid further discloses further comprising: a central monitoring station communicating with the physiological monitoring device through the computing system ([0132]: “The IHM module 302, 302a may continue to monitor the biometric data of the mother and transmit the data to the delivery facility and personal in real time.”); wherein a caregiver monitors the patient's condition from the central monitoring station ([0102]: “allows for the remote monitoring of a person's current biometric data collected by the monitoring module 510 in real time and, based on the patient's up-to-date medical condition stored a database and based on the patient's current biometric d”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kreger (US 20030018248 A1) – discloses a method of filtering a respiratory signal
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791
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