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 .
DETAILED ACTION
Claims 1-41 are pending.
Claim Objections
Claim 24 is objected to because of the following informalities: typographical error. “The system of any claim 21” in line 1 should be “The system of claim 21”.
“data sensor” in line 5 should be “sensor”. Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description: 200, 202, 204 in paras. 0118,0118.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
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 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10, 21-29, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Clifton et al. (U.S. Patent Application Publication 20190069808 A1, hereinafter “Clifton”) in view of Davies et al. (“Rapid extraction of respiratory waveforms from photoplethysmography: A deep corr-encoder approach”, Biomedical Signal Processing and Control, Volume 85, August 2023, 104992, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.104992, downloaded from
(https://www.sciencedirect.com/science/article/pii/S1746809423004251 on 6/23/26, hereinafter “Davies”).
Regarding Claim 1, Clifton teaches a computational method to construct a capnogram from a physiological waveform, comprising:
obtaining, utilizing a computational processing system (par 0069 Fig 13 processing unit 4; par 0070 Fig 14 data processing station 10), the physiological waveform (par 0055 Fig 9 step S1, a plurality of time windows of data are obtained, par 0056 the data representing physiological measurements [over time, a waveform] made on a subject);
filtering, utilizing the computational processing system, the physiological waveform to yield a respiration waveform (par 0007 Fig 9 step S3 low-pass filtering is used to detect the respiratory signal).
However, Clifton appears not to expressly teach
constructing, using the computational processing system, the capnogram based on the respiration waveform.
Davies teaches constructing, using the computational processing system (section 4 sixth paragraph Intel i7-1165G7 processor (2.8 GHz)), the capnogram based on the respiration waveform (Abstract a corr-encoder model encodes respiratory information contained within a photoplethysmography waveform, and decodes it into a reconstructed capnography waveform; section 1.1 second paragraph the model extracts respiratory information that is available in PPG to generate capnography waveform using the respiration waveform).
Clifton and Davies are analogous art as they each pertain to methods of measurement and analysis of physiological waveforms. It would have been obvious to a person of ordinary skill in the art to modify the method of Clifton with the inclusion of the capnogram construction of Davies. The motivation would have been in order to provide a framework that takes PPG as an input and outputs highly accurate respiratory waveforms, using a simple deep learning model that is computationally cheap to run, making implementation in wearables highly feasible (Davies section 1 first paragraph).
Regarding Claim 2, Clifton as modified teaches the method of claim 1, wherein
obtaining the physiological waveform comprises capturing physiological signals from a sensor (Clifton par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG), wherein
the physiological signals are utilized to generate the physiological waveform via the computational processing system (Clifton par 0055 Fig 9 step 1 a waveform is generated from the physiological signals; par 0053,par 0070 Fig 14 system 8 is configured to distribute processing of the [waveform] to calculate the frequency of the periodic physiological process between the device 2 and the data processing station 10).
Regarding Claim 3, Clifton as modified teaches the method of claim 2, wherein
the sensor is one of:
a blood pressure transducer catheter, a blood pressure cuff, an ultrasound transducer, an MRI scanner, ECG leads, or a PPG (Clifton par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG).
Regarding Claim 4, Clifton as modified teaches the method of claim 2, wherein
filtering the physiological waveform (Clifton par 0004 respiration rate extraction [par 0007 using filtering of the PPG waveform] is continuously performed while sensing the physiological signals) and constructing the capnogram are performed while obtaining the physiological waveform (Davies section 1.2 the capnogram is obtained [decoded from the photoplethysmogram measured during breathing]).
Clifton and Davies are analogous art as they each pertain to methods of measurement and analysis of physiological waveforms. It would have been obvious to a person of ordinary skill in the art to modify the method of Clifton with the inclusion of the capnogram construction of Davies. The motivation would have been in order to provide a framework that takes PPG as an input and outputs highly accurate respiratory waveforms, using a simple deep learning model that is computationally cheap to run, making implementation in wearables highly feasible (Davies section 1 first paragraph).
Regarding Claim 5, Clifton as modified teaches the method of claim 2, wherein
a medical monitoring system comprises or is in communication with the computational processing system and the sensor (Clifton par 0004 a PPG monitor comprises the, par 0069 Fig 13, sensing system 6 and processing system 4).
Regarding Claim 6, Clifton as modified teaches the method of claim 1, wherein
obtaining the physiological waveform comprises capturing physiological signals from multiple sensors from multiple locations (Clifton par 0098 multiple lead ECG data capturing physiological signals from multiple sensors from multiple locations); wherein
the physiological signals from the multiple sensors are combined to generate the physiological waveform via the computational processing system (Clifton par 0021 multi-parameter and smart fusion methods that are capable of taking estimations from multiple different [physiological signals] and merging them into a single waveform).
Regarding Claim 7, Clifton as modified teaches the method of claim 1, wherein
the physiological waveform comprises:
a blood pressure waveform, a signal proportional to a blood pressure waveform, or a signal derived from a blood pressure waveform;
a blood flow waveform, a signal proportional to a blood flow waveform, or a signal derived from a blood flow waveform;
an electrocardiogram, a signal proportional to an electrocardiogram, or a signal derived from an electrocardiogram; or
a plethysmogram, a signal proportional to a plethysmogram, or a signal derived from a plethysmogram (Clifton par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG); par 0004 photoplethysmography (PPG)).
Regarding Claim 8, Clifton as modified teaches the method of claim 1, wherein
filtering the physiological waveform comprises using a lowpass filter (Clifton par 0007 one of three low-pass filters with varying cut-off frequencies may be used to detect the respiratory waveform).
Regarding Claim 9, Clifton as modified teaches the method of claim 8, wherein
the lowpass filter has a cutoff frequency that is below a heart rate within the physiological waveform (Clifton par 0007 teaches allowing heart rate signal frequencies to be filtered through from the PPG waveform using a bandpass filter [i.e. X cycles/sec] then low-pass filtering the waveform [passing frequencies that are low relative to the heart rate, i.e. filtering using a cut-off frequency below that of the heart rate frequency).
Regarding Claim 10, Clifton as modified teaches the method of claim 8, wherein
filtering the physiological waveform comprises:
determining, using the computational processing system, a heart rate from the physiological waveform (Clifton par 0007 teaches allowing heart rate signal frequencies to be filtered through from the PPG waveform using a bandpass filter [i.e. X cycles/ sec]); and
setting, using the computational processing system, a lowpass filter cutoff at a frequency less than the heart rate determined from the physiological waveform (Clifton par 0007 teaches allowing heart rate signal frequencies to be filtered through from the PPG waveform using a bandpass filter [i.e. X cycles/sec] then low-pass filtering the waveform [passing frequencies that are low relative to the heart rate, i.e. filtering using a cut-off frequency below that of the heart rate frequency).
Regarding Claim 21, Clifton teaches a medical monitoring system for constructing a capnogram, comprising:
a sensor (par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG), a computational processing system (par 0069 Fig 13 processing unit 4; par 0070 Fig 14 data processing station 10), and a set of instructions stored in memory or in non-transitory media (par 0053 a computer program product/media comprising the computer program may provide code to instruct the computer to perform the steps), wherein
the sensor is capable of capturing physiological signals (par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG), and
the sensor is in connection with the computational processing system such that physiological signal data captured by the sensor can be transmitted to the computational processing system (Clifton par 0004 a PPG monitor comprises the, par 0069 Fig 13, sensing system 6 and processing system 4 and so the sections inter-communicate), wherein
the set of instructions direct the computational processing system to:
receive a physiological waveform from the physiological sensor data derived from the sensor (par 0055 Fig 9 step S1, a plurality of time windows of data are obtained, par 0056 the data representing physiological measurements [over time, a waveform] made on a subject);
filter the physiological waveform to yield a respiration waveform par 0007 Fig 9 step S3 low-pass filtering is used to detect the respiratory signal).
However, Clifton appears not to expressly teach
construct a capnogram based on the respiration waveform.
Davies teaches construct a capnogram based on the respiration waveform (Abstract a corr-encoder model encodes respiratory information contained within a photoplethysmography waveform, and decodes it into a reconstructed capnography waveform; section 1.1 second paragraph the model extracts respiratory information that is available in PPG to generate capnography waveform using the respiration waveform).
Clifton and Davies are analogous art as they each pertain to methods of measurement and analysis of physiological waveforms. It would have been obvious to a person of ordinary skill in the art to modify the method of Clifton with the inclusion of the capnogram construction of Davies. The motivation would have been in order to provide a framework that takes PPG as an input and outputs highly accurate respiratory waveforms, using a simple deep learning model that is computationally cheap to run, making implementation in wearables highly feasible (Davies section 1 first paragraph).
Claim 22 presents the limitations of Claim 4 in a different claim category, and therefore Claim 22 is rejected with a rationale similar to Claim 4, mutatis mutandis.
Claim 23 presents the limitations of Claim 3 in a different claim category, and therefore Claim 23 is rejected with a rationale similar to Claim 3, mutatis mutandis.
Claim 24 presents the limitations of Claim 2 in a different claim category, and therefore Claim 24 is rejected with a rationale similar to Claim 2, mutatis mutandis.
Claim 25 presents the limitations of Claim 6 in a different claim category, and therefore Claim 25 is rejected with a rationale similar to Claim 6, mutatis mutandis.
Claim 26 presents the limitations of Claim 7 in a different claim category, and therefore Claim 26 is rejected with a rationale similar to Claim 7, mutatis mutandis.
Claim 27 presents the limitations of Claim 8 in a different claim category, and therefore Claim 27 is rejected with a rationale similar to Claim 8, mutatis mutandis.
Claim 28 presents the limitations of Claim 9 in a different claim category, and therefore Claim 28 is rejected with a rationale similar to Claim 9, mutatis mutandis.
Claim 29 presents the limitations of Claim 10 in a different claim category, and therefore Claim 29 is rejected with a rationale similar to Claim 10, mutatis mutandis.
Regarding Claim 41, Clifton as modified teaches the system of claim 21, wherein
the medical monitoring system is a hemodynamic monitoring system (Clifton par 0004 a PPG monitor comprises the, par 0069 Fig 13, sensing system 6 and processing system 4) or an electrocardiography system (Clifton par 0069 Fig 13 obtaining physiological signals on the subject using a sensing system 6 (e.g. ECG or PPG).
Claims 20 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Clifton in view of Davies and further in view of Klein et al. (U.S. Patent Application Publication 20170303830 A1, hereinafter “Klein”).
Regarding Claim 20, Clifton as modified teaches the method of claim 1. However, Clifton as modified appears not to expressly teach further comprising
displaying the capnogram on a display screen, wherein
the computational processing system is in connection with the display screen.
Klein teaches further comprising
displaying the capnogram on a display screen (par 0233 the display may include a Capnometer to display the capnogram), wherein
the computational processing system is in connection with the display screen (par 0233 a patient data unit is in connection with the display; par 0092 processing units communicate with the display).
Clifton Davies and Klein are analogous art as they each pertain to methods of measurement and analysis of physiological waveforms. It would have been obvious to a person of ordinary skill in the art to modify the method of Clifton/Davies with the inclusion of the capnogram display of Klein. The motivation would have been in order to provide sedation monitoring and detection of indicators of over sedation, such as low respiratory rate, apnea, and low oxygen saturation (Klein par 0233).
Claim 40 presents the limitations of Claim 20 in a different claim category, and therefore Claim 40 is rejected with a rationale similar to Claim 20, mutatis mutandis.
Allowable Subject Matter
Claims 11-19 and 30-39 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 11:
While closest prior art Clifton (20190069808 A1) and Davies (paper) teach portion of the limitations of Claim 11, the prior art of record fails to teach or fairly suggest the particular limitations of Claim 11, namely "constructing the capnography waveform comprises: selecting, using the computational processing system, mathematical bases of capnogram waveform morphologies; and constructing, using the computational processing system, a capnography waveform cycle using the selected mathematical bases and the respiration waveform" in combination with all other limitations of the claim and of claims on which the claim depends.
Claim 30:
While closest prior art Clifton (20190069808 A1) and Davies (paper) teach portion of the limitations of Claim 30, the prior art of record fails to teach or fairly suggest the particular limitations of Claim 30, namely "the set of instructions further direct the computational processing system to: select mathematical bases of capnogram waveform morphologies mathematical bases of capnogram waveform morphologies; and construct a capnography waveform cycle using the selected mathematical bases and the respiration waveform" in combination with all other limitations of the claim and of claims on which the claim depends.
Claims 12-19 and 31-39 would be allowable dependent on the allowability of their parent claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK EDWARDS whose telephone number is (571)270-7731. The examiner can normally be reached on Mon-Fri 9a-5p EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Eason can be reached on 571-270-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARK EDWARDS/Primary Examiner, Art Unit 2624
Prosecution Insights