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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/06/2026 has been entered.
Status of Claims
This Office Action is in response to the remarks and amendments filed on February 6th, 2026. No claims have been canceled as such claims 1-15 are pending consideration in this Office Action.
Response to Amendment
The objections to the claims are withdrawn in light of the amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-14 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-14 recites at least one step or instruction for detecting/calculating a respective heartbeat time per heartbeat for a sequence of heartbeats through analysis of cardiogenic and respiratory signals which can be calculated by mind and/or hand, therefore is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-14 recites an abstract idea.
Specifically, Claim 1 recites
A process for detecting a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient, the process comprising the steps of:
providing a signal processing unit comprising a first detector and a second detector (additional element) and providing a sensor arrangement comprising a sensor array (additional element) configured to measure a variable, which variable correlates with at least one of a cardiac activity of the patient and an intrinsic breathing activity of the patient (insignificant extra solutional activity; MPEP 2106.05(g) for observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
generating a first sum signal using measured values of the sensor array, wherein the generated first sum signal comprises a superimposition of a cardiogenic signal and a respiratory signal, wherein the cardiogenic signal correlates with the cardiac activity of the patient and the respiratory signal correlates with the intrinsic breathing activity of the patient (insignificant extra solutional activity; MPEP 2106.05(g) for observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
calculating, in real time during the intrinsic breathing activity of the patient, with the first detector, a first detection result for the respective characteristic heartbeat time by analyzing the first sum signal (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) with the first detector (additional element);
calculating, in real time during the intrinsic breathing activity of the patient,(observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) with the second detector (additional element), a second detection result for the respective characteristic heartbeat time by at least one of:
analyzing another sum signal that is different from the first sum signal (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
analyzing the first sum signal by applying a different method of analysis than that applied by (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) the first detector (additional element); and
analyzing another sum signal that is different from the first sum signal and applying a different method of analysis than that applied by (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) the first detector (additional element); and
with the signal processing unit (additional element), calculating a representation for the respective characteristic heartbeat time by using the first detection result and the second detection result (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG).
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Specifically, Claim 12 recites
A signal processing unit for detecting a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient,
the signal processing unit comprising: a first detector; and a second detector (additional element),
wherein the signal processing unit (additional element) is configured:
to receive measured values from a sensor arrangement comprising a sensor array (additional element; insignificant extra solutional activity; MPEP 2106.05(g) for observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG),
wherein the sensor array (additional element) is configured to measure a variable, which variable correlates with at least one of a cardiac activity of the patient and an intrinsic breathing activity of the patient (insignificant extra solutional activity; MPEP 2106.05(g) for observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG); and
to generate a first sum signal using received measured values, wherein the generated first sum signal comprises a superimposition of a cardiogenic signal and of a respiratory signal, wherein the cardiogenic signal correlates with the cardiac activity of the patient and the respiratory signal correlates with the intrinsic breathing activity of the patient (insignificant extra solutional activity; MPEP 2106.05(g) for observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG),
wherein the first detector (additional element) is configured to calculate a first detection result for the respective characteristic heartbeat time, in real time, during the intrinsic breathing activity of the patient by analyzing the first sum signal (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG),
wherein the second detector (additional element) is configured to calculate a respective second detection result for the respective characteristic heartbeat time, in real time, during the intrinsic breathing activity of the patient (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) by one of:
analyzing another sum signal that is different from the first sum signal (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG);
analyzing the first sum signal by applying a different method of analysis (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) than that applied by the first detector (additional element); and
analyzing another sum signal that is different from the first sum signal by applying a different method of analysis (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG) than that applied by the first detector (additional element),
wherein the signal processing unit (additional element) is configured to calculate a representation for the respective characteristic heartbeat time (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG), and
wherein the signal processing unit (additional element) is configured to calculate the representation of the respective characteristic heartbeat time by using the first detection result and the second detection result (observation, judgment or evaluation, which is grouped as a mental process under the 2019 PEG).
Further, dependent Claims 2-11, 13, and 14 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claim 1 (and their respective dependent Claims 2-11, 13, and 14) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claim 1), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of:
signal processing unit, first detector, second detector, sensor arrangement, and sensor array as recited in independent claim 1 and its dependent claims
signal processing unit, first detector, second detector, sensor arrangement, and sensor array as recited in independent claim 12 and its dependent claims
are generically recited computer elements in independent Claims 1 and 12 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1 and 12 (and their respective dependent claims) are not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (signal processing unit as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 and 12 (and their respective dependent claims) are not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1 and 12 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-14 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of:
signal processing unit, first detector, second detector, sensor arrangement, and sensor array as recited in independent claim 1 and its dependent claims
signal processing unit, first detector, second detector, sensor arrangement, and sensor array as recited in independent claim 12 and its dependent claims
The above-identified additional elements are generically claimed computer/structural components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, the signal processing unit is “a data-processing signal processing unit 5, wherein the signal processing unit 5 has reading access and writing access to a memory 9 at least from time to time” [0086] and “according to the present invention is configured to receive measured values from the sensor array or from each sensor array and to generate a sum signal from the measured values received or at least from some of the received measured values, wherein the signal processing unit preferably processes received measured values” [0013].
Accordingly, in light of Applicant’s specification, the claimed term signal processing unit is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the signal processing unit. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activities’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-14 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method and apparatus of Claims 1-14 are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR). In other words, none of Claims 1-14 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1 and 12 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-14 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-14 amounts to significantly more than the abstract idea itself.
Accordingly, Claims 1-14 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
It is noted that independent claim 15 is not rejected under 101 as the end of the claim states “wherein the ventilator is configured to ventilate a patient including carrying out ventilation as a function of the representation for the respiratory signal”; therefore, it integrates the abstract idea into practical application. Claims 1-14 do not include said limitation and as such are not integrated into practical application.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-6 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Eger (US 20180344194) in view of Pan-Tompkins (Article: A Real-Time QRS Detection Algorithm).
Regarding claim 1, Eger discloses
A process for representing a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient (using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows; Paragraph 0104, Lines 1-10), the process comprising the steps of:
providing a signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) comprising a first detector (Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]) and a second detector (Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal) and a sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) comprising at least one sensor array (Fig. 1; SE sensor pair 1 and 2; SE sensor pair 3 and 4; SE sensor pair 5 and 6; SE sensor pair 7 and 8; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8; Paragraph 0081, Lines 1-8; each location has a pair or array of sensors) configured to measure a variable (Figs. 1, 3, and 5; detected EMG/EKG signal; Paragraph 0081, Lines 1-3), which correlates with cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and/or with intrinsic breathing activity of the patient (expiratory activity and inspiratory breathing activity; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8);
generating at least one sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) using measured values (detected EMG/EKG signal; Paragraph 0085, Lines 1-7) of the sensor array (Fig. 1; electromyography sensor pairs SE5 and SE6, SE1 and SE2, SE7 and SE8 as well as SE3 and SE4; Paragraph 0085, Lines 3-5),
wherein the sum signal generated or every generated sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) comprises a superimposition of a cardiogenic signal (Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12) and a respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12),
wherein the cardiogenic signal (EKG signal; Paragraph 0081, Lines 1-3) correlates with the cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and the respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) correlates with the intrinsic breathing activity of the patient (Fig. 17; inspiratory and expiratory activity; Paragraph 0146, Lines 1-5);
calculating, in real-time during the intrinsic breathing activity of the patient, with the first detector (Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]), a first detection result (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) for the characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by analyzing the or one sum signal (Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
calculating, in real-time during the intrinsic breathing activity of the patient, with the second detector (Figs. 5, 15, 18; Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]; filter detects/separates EKG signal and detection steps detects heart signal components), a second detection result (Fig. 5 and 15; Dlx and EMSx for EMS2; Paragraph 0104, Lines 1-15) for the characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by one of:
analyzing another sum signal (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) that is different from the sum signal analyzed by the first detector (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and
analyzing another sum signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and
with the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculating a representation (Paragraph 0107, Lines 1-16) for the characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by using the first detection result (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) and the second detection result (Fig. 5 and 15; Dlx and EMSx for EMS4; Paragraph 0104, Lines 1-15).
While Eger itself does not explicitly disclose calculating a characteristic heartbeat time, it does disclose using the Pan-Tompkins algorithm to detect the presence of an EKG signal or a QRS complex within the EMG signal [0106].
Pan-Tompkins further discloses the algorithm
calculating characteristic heartbeat times by analyzing the or one sum signal (the algorithm comprises three processes: learning phase 1, leaning phase 2, and detection; where leaning phase 2 discusses using RR-intervals of heartbeats to help detect QRS complexes; further a dual-threshold technique which calculates RR-interval averages that must be update for each heartbeat; pages 231-232; therefore, in order to calculate RR-interval averages, the time of the R-peaks (individual heartbeat time) must be calculated/measured).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the algorithm of suppressing heartbeat time windows of Eger (which uses the Pan-Tompkins algorithm) to further include the use of RR interval average and limit values of the algorithm of Pan-Tompkins to more reliably detect QRS complexes and adapt each of its parameters with time so as to be able to operate properly for ECG morphology changes in a patient (Pan-Tompkins: pages 231-232, see page 232, col. 1, paragraphs 2-3).
It directly follows that the resultant algorithm of Eger combined with the RR-interval averaging of Pan-Tompkins would meet the claimed structural limitations of using a computer with algorithms/filters to calculate/detect QRS complexes of a heart component (Eger: [0106]-[0107]) using RR-interval averages (R-peak times of heartbeats; Pan-Tompkins: pages 231-232) by analyzing the EMS signal (Eger: [0106]-[0107]).
Regarding claim 2, the modified method of Eger further discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 1,
wherein a cardiogenic signal segment (Eger: see modified Figs. 5 and 15 below; labeled as segment), being a predefined cardiogenic signal segment (Eger: see modified Figs. 5 and 15 below; labeled as segment), is given or the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) determines the cardiogenic signal segment (Eger: see modified Figs. 5 and 15 below; labeled as segment) by using a sample (Eger: see modified Fig. 15 below; EMS1-EMS4 (samples); Paragraph 00143, Lines 1-9),
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wherein the cardiogenic signal segment (Eger: see modified Figs. 5 and 15 above; labeled as segment) approximately describes a temporal course of the cardiac activity of the patient (see modified Fig. 5 and Fig. 15 above; time windows; Paragraph 0104, Lines 3-15) in a course of a single heartbeat (Eger: see modified Fig. 5 and Fig. 15 above; each segment is a single heartbeat which can be seen by each 0 value in Dlx),
wherein the sample (Eger: see modified Fig. 15 below; EMS1-EMS4 (samples); Paragraph 00143, Lines 1-9) comprises a plurality of segments (Eger: see modified Figs. 5 and 15 above; labeled as the plurality of segments) of the sum signal (Eger: see modified Fig. 15 above; digital EMG signals, EMS1; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) or of the other sum signal (Eger: see modified Fig. 15 above; digital EMG signals, EMS2; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered), wherein each segment of the sample (Eger: see modified Figs. 5 and 15 above; labeled as segment) refers to a respective time period (Eger: see modified Fig. 5 and Fig. 15 above; time windows; Paragraph 0104, Lines 3-15), in which a single heartbeat is carried out (Eger: see modified Fig. 5 and Fig. 15 above; each segment is a single heartbeat which can be seen by each 0 value in Dlx), and
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) determines the cardiogenic signal (Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12) using the detected characteristic heartbeat times (Eger: see modified Fig. 5 and Fig. 15 above; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232) and the predefined or determined cardiogenic signal segment (Eger: see modified Figs. 5 and 15 above; labeled as segment).
Regarding claim 3, the modified method of Eger further discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 1,
wherein the sensor arrangement (Eger: Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) comprises a first sensor array (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6; Paragraph 0085, Lines 1-5) and a second sensor array (Eger: Fig. 1; electromyography sensor pairs SE3 and SE4; Paragraph 0085, Lines 1-5),
wherein the first sensor array (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6; Paragraph 0085, Lines 1-5) comprises at least one first sensor (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6; Paragraph 0085, Lines 1-5; has two sensors) and
the second sensor array (Eger: Fig. 1; electromyography sensor pairs SE1 and SE2; Paragraph 0085, Lines 1-5) comprises at least and second sensor (Eger: Fig. 1; electromyography sensor pairs SE1 and SE2; Paragraph 0085, Lines 1-5; has two sensors), wherein at least one of:
the second sensor (Eger: Fig. 1; electromyography sensor pairs SE1 and SE2; Paragraph 0080, Lines 1-4) is arranged at position in relation to the heart (Eger: Fig. 1; lower diaphragm; Paragraph 0080, Lines 1-4) that is different from a position of the of the first sensor (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6; Paragraph 0079, Lines 1-5) in relation to the heart (Eger: Fig. 1; intercostal muscle; Paragraph 0079, Lines 1-5): and
the second sensor applies a different measuring method than that applied by the first sensor, and
wherein the sum signal is a first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) that is generated by using measured values of the first sensor array (Eger: Figs. 1 and 15; electromyography sensor pairs SE5 and SE6; Paragraph 0079, Lines 1-5 and Paragraph 0143, Lines 1-10) and the other sum signal is a second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) that is generated by using measured values of the second sensor array (Eger: Figs. 1 and 15; electromyography sensor pairs SE1 and SE2; Paragraph 0080, Lines 1-4 and Paragraph 0143, Lines 1-10);
wherein the first detector (Eger: Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]) calculates the first detection result (Eger: Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) for each characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232) by analyzing the first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12); and
wherein the second detector (Eger: Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]) calculates the respective second detection result (Eger: Fig. 5 and 15; Dlx and EMSx for EMS2; Paragraph 0104, Lines 1-15) for each characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232) by analyzing the second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12).
Regarding claim 4, the modified method of Eger further discloses
a process in accordance (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) with claim 3,
wherein the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculates a first representation (Eger: Fig. 17; HE1; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) and a second representation (Eger: Fig. 17; HE2; Paragraph 0146, Lines 1-5) for the respiratory signal (Eger: Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12);
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculates the first representation for the respiratory signal (Eger: Fig. 17; HE1; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) by using the first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12)) and the respective representation (Paragraph 0107, Lines 1-16) for each characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal); and
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculates the second representation for the respiratory signal (Eger: Fig. 17; HE2; Paragraph 0146, Lines 1-5) by using the second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) and the respective representation (Eger: Paragraph 0107, Lines 1-16) for each characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal).
Regarding claim 5, the modified method of Eger further discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 1, wherein:
the first detector (Eger: Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]; filter detects/separates EKG signal and detection steps detects heart signal components) comprises a first real time detector (Eger: Fig. 18; current filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) and an additional first detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9) and
the second detector (Eger: Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm during FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]; each filter is for a different signal) comprises a second real time detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) and an additional second detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9);
a calculation period (Eger: Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15) is predefined,
the first real time detector (Eger: Fig. 18; current filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) calculates a first real time detection result (Eger: output signals; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in the calculation period (Eger: Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15);
the additional first detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9) calculates an additional first detection result (Eger: output signals for new filter coefficients; Paragraph 0136, Lines 1-9) for the characteristic heartbeat time (Eger: time range; Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal);
the second real time detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) calculates a second real time detection result (Eger: output signals; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in the calculation period (Eger: Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15);
the additional second detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) calculates an additional second detection result (Eger: output signals for new filter coefficients; Paragraph 0136, Lines 1-9) for the characteristic heartbeat time (Eger: time range; Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal);
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) calculates a respective real time representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for each characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in the calculation period (Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15) by using the first real time detection result (Eger: output signal E11/E1 for filter F11; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) and the second real time detection result (Eger: output signal E12/EQ for filter FPQ; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4): and
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) calculates a respective additional representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for each characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) by using the first additional detection result (Eger: output signals E11/E1 for new filter coefficients of F11; Paragraph 0136, Lines 1-9) and the second additional detection result (Eger: output signals E12/EQ for new filter coefficients of FPQ; Paragraph 0136, Lines 1-9).
Regarding claim 6, Eger further discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows) in accordance with claim 5, wherein:
wherein the sum signal is a first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) and the other sum signal is a second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
the first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) and the second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) are generated by using measured values of the sensor arrangement (Eger: Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3);
the second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) is generated by using measured values of a different sensor (Eger: Figs. 1 and 15; electromyography sensor pairs SE1 and SE2; Paragraph 0080, Lines 1-4 and Paragraph 0143, Lines 1-10) and/or based on a different method for processing measured values than that (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6; Paragraph 0079, Lines 1-5 and Paragraph 0143, Lines 1-10) used to generate the first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
the first real time detector (Eger: Fig. 18; current filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) calculates the first real time detection result (Eger: output signal E11/E1 for filter F11; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) by using the first sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12); and
the second real time detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) calculates the second real time detection result (Eger: output signal E12/EQ for filter FPQ; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) by using the second sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12).
Regarding claim 11, Eger further discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 1,
wherein the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculates at least one representation for the respiratory signal (Eger: Fig. 17; HE1-HE4; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) by using the sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) or the other sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
the step of calculating the representation for the respiratory signal (Eger: Fig. 17; HE1-HE4; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) comprises, with the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7), compensating by calculation an effect of the cardiogenic signal (Eger: Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12; separation and filtering the EKG signals leads to reduced cross-talk and obtaining signals indicating inspiratory/expiratory muscle activity) on the sum signal (Eger: Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) or on the other sum signal (Eger: Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) using the detected characteristic heartbeat times (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15 and Paragraph 0147, Lines 1-12; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal).
Regarding claim 12, Eger further discloses
a signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) for representing a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient (Paragraph 0104, Lines 1-10),
the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) comprising:
a first detector (Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm and FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; [0106]-[0107]); and
a second detector (Figs. 5, 15, 18; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins’s algorithm and FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal),
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured:
to receive measured values (detected EMG/EKG signal; Paragraph 0085, Lines 1-7) from a sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) comprising at least one sensor array (Fig. 1; SE sensor pair 1 and 2; SE sensor pair 3 and 4; SE sensor pair 5 and 6; SE sensor pair 7 and 8; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8; Paragraph 0081, Lines 1-8; each location has a pair or array of sensors),
wherein the sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) is configured to measure a variable (Figs. 1, 3, and 5; detected EMG/EKG signal; Paragraph 0081, Lines 1-3), which correlates with at least one of cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and intrinsic breathing activity of the patient (expiratory activity and inspiratory breathing activity; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8); and
to generate at least one sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) using received measured values (detected EMG/EKG signal; Paragraph 0085, Lines 1-7),
wherein the sum signal or every sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) comprises a respective superimposition of a cardiogenic signal (Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12) and of a respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12),
wherein the cardiogenic signal (EKG signal; Paragraph 0081, Lines 1-3) correlates with the cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and the respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) correlates with the intrinsic breathing activity of the patient (Fig. 17; inspiratory and expiratory activity; Paragraph 0146, Lines 1-5),
wherein the first detector (Figs. 5, 15, 18; FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components) is configured to calculate a first detection result (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) for each characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by analyzing the sum signal (Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12),
wherein the second detector (Figs. 5, 15, 18; FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components) is configured to calculate a respective second detection result (Fig. 5 and 15; Dlx and EMSx for EMS2; Paragraph 0104, Lines 1-15) for the characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by one of:
analyzing another sum signal (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) that is different from the sum signal analyzed by the first detector (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12);
analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and
analyzing another sun signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector,
wherein the signal processing unit ((Fig. 1; computer R; Paragraph 0089, Lines 1-7)) is configured to calculate a representation (Paragraph 0107, Lines 1-16) for the respective characteristic heartbeat time (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15), and
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to calculate the representation (Paragraph 0107, Lines 1-16) of the respective characteristic heartbeat time (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by using the first detection result (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) and the second detection result (Fig. 5 and 15; Dlx and EMSx for EMS4; Paragraph 0104, Lines 1-15).
While Eger itself does not explicitly disclose the algorithm configured to calculate a characteristic heartbeat time, it does disclose using the Pan-Tompkins algorithm to detect the presence of an EKG signal or a QRS complex within the EMG signal [0106].
Pan-Tompkins further discloses the algorithm
configured to calculate characteristic heartbeat times by analyzing the or one sum signal (the algorithm comprises three processes: learning phase 1, leaning phase 2, and detection; where leaning phase 2 discusses using RR-intervals of heartbeats to help detect QRS complexes; further a dual-threshold technique which calculates RR-interval averages that must be update for each heartbeat; pages 231-232; therefore, in order to calculate RR-interval averages, the time of the R-peaks (individual heartbeat time) must be calculated/measured).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the algorithm of suppressing heartbeat time windows of Eger (which uses the Pan-Tompkins algorithm) to further include the use of RR interval average and limit values of the algorithm of Pan-Tompkins to more reliably detect QRS complexes and adapt each of its parameters with time so as to be able to operate properly for ECG morphology changes in a patient (Pan-Tompkins: pages 231-232, see page 232, col. 1, paragraphs 2-3).
It directly follows that the resultant algorithm of Eger combined with the RR-interval averaging of Pan-Tompkins would meet the claimed structural limitations of using a computer with algorithms/filters to calculate/detect QRS complexes of a heart component (Eger: [0106]-[0107]) using RR-interval averages (R-peak times of heartbeats; Pan-Tompkins: pages 231-232) by analyzing the EMS signal (Eger: [0106]-[0107]).
Regarding claim 13, the modified device of Eger further discloses
a signal processing unit (Eger: computer R which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 12, wherein:
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) comprises a first real time detector (Eger: Figs. 5, 15, 18; current filter coefficient of FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components; [0106]-[0107]) and an additional first detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9) as the first detector (Eger: Figs. 5, 15, 18; FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm, filter detects/separates EKG signal, and detection steps detects heart signal components) and
comprises a second real time detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) and an additional second detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9) as the second detector (Eger: Figs. 5, 15, 18; FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal; presence of an EKG signal or a QRS complex within the EMG signal is detected using Pan-Tompkins algorithm, filter detects/separates EKG signal, and detection steps detects heart signal components, [0106]-[0107]);
the first real time detector (Eger: Fig. 18; current filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) is configured to calculate a respective first real time detection result (Eger: output signals; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in a predefined calculation period (Eger: Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15),
the second real time detector (Eger: Figs. 5, 15, 18; current FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal) is configured to calculate a respective second real time detection result (Eger: output signals; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in the calculation period (Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15),
the additional first detector (Eger: Fig. 18; new, updated filter coefficient of FIR filter F11; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 15-18 and Paragraph 0136, Lines 1-9; Paragraph 0138, Lines 1-9) is configured to calculate an additional first detection result (Eger: output signals for new filter coefficients; Paragraph 0136, Lines 1-9) for each characteristic heartbeat time (Eger: time range; Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal),
the additional second detector (Eger: Fig. 18; current filter coefficient of FIR filter FPQ; Paragraph 0128, Lines 5-15; Paragraph 0135, Lines 1-14; Paragraph 0138, Lines 1-9) is configured to calculate an additional second detection result (Eger: output signals; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) for each characteristic heartbeat time (Eger: time range; Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal),
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) is configured:
to calculate a respective real time representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for the characteristic heartbeat time of a heartbeat (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) in the calculation period (Eger: Fig. 18; determination step FBE; Paragraph 0128, Lines 5-15) by using the first real time detection result (Eger: output signal E11/E1 for filter F11; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4) and the second real time detection result (Eger: output signal E12/EQ for filter FPQ; Paragraph 0133, Lines 3-7 and Paragraph 0134, Lines 1-4); and
to calculate a respective additional representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) by using the first additional detection result (Eger: output signals E11/E1 for new filter coefficients of F11; Paragraph 0136, Lines 1-9) and the second additional detection result (Eger: output signals E12/EQ for new filter coefficients of FPQ; Paragraph 0136, Lines 1-9).
Regarding claim 14, the modified method of Eger further discloses
A signal processing unit (Eger: computer R which can represent/show the EKG signal of the heart and respiratory activity with time windows: Pan-Tompkins: RR-interval averages for QRS complex detection) according to claim 12, in combination with a sensor arrangement (Eger: Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) comprising at least one sensor array (Eger: Fig. 1; SE sensor pair 1 and 2; SE sensor pair 3 and 4; SE sensor pair 5 and 6; SE sensor pair 7 and 8; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8; Paragraph 0081, Lines 1-8; each location has a pair or array of sensors) configured:
to measure at least one variable (Eger: Figs. 1, 3, and 5; detected EMG/EKG signal; Paragraph 0081, Lines 1-3), which correlates with the cardiac activity (Eger: heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and/or with the patient's own breathing activity (Eger: expiratory activity and inspiratory breathing activity; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8),
wherein the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to receive measured values (Eger: detected EMG/EKG signal; Paragraph 0085, Lines 1-7) from the sensor array or each sensor array (Eger: Fig. 1; electromyography sensor pairs SE5 and SE6, SE1 and SE2, SE7 and SE8 as well as SE3 and SE4; Paragraph 0085, Lines 3-5); and
to generate the sum signal or each sum signal (Eger: Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) by using received measured values (Eger: detected EMG/EKG signal; Paragraph 0085, Lines 1-7).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Eger (US 20180344194) in view of Pan-Tompkins (Article: A Real-Time QRS Detection Algorithm) and further in view of Capodilupo (US 11185292).
Regarding claim 9, Eger discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows; Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 1,
Eger does not disclose wherein at least one of the first detector and the second detector calculates a quality indicator comprising an indicator of a reliability that the detection result or results provided by the at least one of the first detector and the second detector coincides with the characteristic heartbeat time; and the signal processing unit calculates the representation for a characteristic heartbeat time by using the detection result or results and the quality indicators.
Capodilupo discloses a model of data quality with a method to estimate heart rate
wherein at least one of the first detector and the second detector (Figs. 8 and 11; heart rate estimators and quality estimator engine 1112; Col. 27, Lines 40-62 and Col. 39, Lines 16-19) calculates a quality indicator (Fig. 11; quality metric; Col. 39, Lines 1-12) comprising an indicator of a reliability that the detection result or results (likelihood of being accurate; Col. 39, Lines 16-19) provided by the at least one of the first detector and the second detector (Figs. 8 and 11; heart rate estimators and quality estimator engine 1112; Col. 27, Lines 40-62 and Col. 39, Lines 16-19) coincides with the characteristic heartbeat time (window of measurements for heartrate data; Col. 38, Lines 19-23; Col. 39, Lines 12-15 and Col. 40, Lines 14-21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the algorithm, detection step, and filters of the modified method of Eger with the method of Capodilupo to be able to determine the accuracy of the physiological data based on the likelihood of being accurate (Capodilupo: Col. 38, Lines 5-15).
It directly follows that the resultant detection step and filters of Eger combined with the method of Capodilupo would meet the claimed structural limitations since:
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) calculates the representation (Eger: Paragraph 0107, Lines 1-16) for a characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) by using the detection result or results (Eger: Fig. 5 and 15; Dlx and EMSx for EMS1 and EMS2; Paragraph 0104, Lines 1-15) and the quality indicators (Capodilupo: Fig. 11; quality metric; Col. 39, Lines 1-12).
Regarding claim 10, the modified process of Eger further discloses
a process (using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows) in accordance with claim 9,
wherein the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7) additionally calculates a quality indicator (Capodilupo: Fig. 11; quality metric; Col. 39, Lines 1-12) for the representation for a characteristic heartbeat time (Eger: Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) as a function of the quality indicators (Capodilupo: Col. 40, Lines 29-34) for the detection results (Eger: Fig. 5 and 15; Dlx and EMSx for EMS1 and EMS2; Paragraph 0104, Lines 1-15; Capodilupo: heart rate data; Col. 39, Lines 16-19).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Eger (US 20180344194) in view of Pan-Tompkins (Article: A Real-Time QRS Detection Algorithm) and further in view of a second embodiment of Eger (US 20180344194).
Regarding claim 15, Eger further discloses
a system (a device V1 which can represent/show the EKG signal of the heart and it’s time windows; Paragraph 0104, Lines 1-10) comprising:
a ventilator (Fig. 1; ventilator BG; Paragraph 0089, Lines 1-7), and
an arrangement (Fig. 1; device V; Paragraph 0073, Lines 1-5) comprising
a signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) for representing a respective characteristic heartbeat time per heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) for a sequence of heartbeats of a patient (Fig. 5 and Fig. 15; graph view of EMG signals EMS1-EMS4),
the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) comprising:
a first detector (Figs. 5, 15, 18; FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components), and a second detector (Figs. 5, 15, 18; FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal),
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured:
to receive measured values from a sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) comprising at least one sensor array (Fig. 1; SE sensor pair 1 and 2; SE sensor pair 3 and 4; SE sensor pair 5 and 6; SE sensor pair 7 and 8; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8; Paragraph 0081, Lines 1-8; each location has a pair or array of sensors),
wherein the sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3) is configured to measure at least one variable (Figs. 1, 3, and 5; detected EMG/EKG signal; Paragraph 0081, Lines 1-3), which correlates with at least one of cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and intrinsic breathing activity of the patient (expiratory activity and inspiratory breathing activity; Paragraph 0079, Lines 1-5; Paragraph 0080, Lines 1-8): and
to generate at least one sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) using received measured values (detected EMG/EKG signal; Paragraph 0085, Lines 1-7),
wherein the sum signal or every sum signal (Fig. 1; digital EMG signals, EMS1-EMS4; Paragraph 0085, Lines 1-7 and Paragraph 0147, Lines 1-12; digital signals have EKG and inspiratory/expiratory signals until filtered) comprises a respective superimposition of a cardiogenic signal (Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12) and of a respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12),
wherein the cardiogenic signal (EKG signal; Paragraph 0081, Lines 1-3) correlates with the cardiac activity of the patient (heart signal component; Paragraph 0081, Lines 1-3 and Paragraph 0104, Lines 1-5) and the respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) correlates with the patient’s own breathing activity ((Fig. 17; inspiratory and expiratory activity; Paragraph 0146, Lines 1-5),
wherein the first detector (Figs. 5, 15, 18; FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components) is configured to calculate a first detection (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) result for each characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by analyzing the sum signal (Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12),
wherein the second detector (Figs. 5, 15, 18; FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components) is configured to calculate a second detection result (Fig. 5 and 15; Dlx and EMSx for EMS2; Paragraph 0104, Lines 1-15) for each characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by at least one of:
analyzing another sum signal (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) that is different from the sum signal analyzed by the first detector (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12),
analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector, and
analyzing another sum signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector,
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to calculate at least one respective representation (Paragraph 0107, Lines 1-16) for each characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15), and
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to calculate the representation or each representation (Paragraph 0107, Lines 1-16) of a characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15) by using at least one respective first detection result (Fig. 5 and 15; Dlx and EMSx for EMS1; Paragraph 0104, Lines 1-15) and at least one respective second detection result (Fig. 5 and 15; Dlx and EMSx for EMS4; Paragraph 0104, Lines 1-15); and
the sensor arrangement (Fig. 1; electromyograph sensors SE1-SE8; Paragraph 0076, Lines 1-3),
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to calculate a representation (Fig. 17; HE1-HE4; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) for the respiratory signal (Figs. 15-17; inspiratory and expiratory signals; Paragraph 0146, Lines 1-5 and Paragraph 0147, Lines 1-12) by using the sum signal (Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) or the other sum signal (Fig. 15; digital EMG signal EMS2; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12),
wherein the signal processing unit (Fig. 1; computer R; Paragraph 0089, Lines 1-7) is configured to compensate an effect of the cardiogenic signal (Figs. 15-17; EKG signal of EMS1-EMS4; Paragraph 0147, Lines 1-12; separation and filtering the EKG signals leads to reduced cross-talk and obtaining signals indicating inspiratory/expiratory muscle activity) on the used sum signal or the other sum signal (Fig. 15; digital EMG signal EMS1; Paragraph 0085, Lines 1-7; Paragraph 0106, Lines 1-6; Paragraph 0147, Lines 1-12) by using the detected characteristic heartbeat (Fig. 5 and Fig. 15; time windows; Paragraph 0104, Lines 3-15 and Paragraph 0147, Lines 1-12).
While Eger itself does not explicitly disclose the algorithm configured to calculate a characteristic heartbeat time, it does disclose using the Pan-Tompkins algorithm to detect the presence of an EKG signal or a QRS complex within the EMG signal [0106].
Pan-Tompkins further discloses the algorithm
configured to calculate characteristic heartbeat times by analyzing the or one sum signal (the algorithm comprises three processes: learning phase 1, leaning phase 2, and detection; where leaning phase 2 discusses using RR-intervals of heartbeats to help detect QRS complexes; further a dual-threshold technique which calculates RR-interval averages that must be update for each heartbeat; pages 231-232; therefore, in order to calculate RR-interval averages, the time of the R-peaks (individual heartbeat time) must be calculated/measured).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the algorithm of suppressing heartbeat time windows of Eger (which uses the Pan-Tompkins algorithm) to further include the use of RR interval average and limit values of the algorithm of Pan-Tompkins to more reliably detect QRS complexes and adapt each of its parameters with time so as to be able to operate properly for ECG morphology changes in a patient (Pan-Tompkins: pages 231-232, see page 232, col. 1, paragraphs 2-3).
It directly follows that the resultant algorithm of Eger combined with the RR-interval averaging of Pan-Tompkins would meet the claimed structural limitations of using a computer with algorithms/filters to calculate/detect QRS complexes of a heart component (Eger: [0106]-[0107]) using RR-interval averages (R-peak times of heartbeats; Pan-Tompkins: pages 231-232) by analyzing the EMS signal (Eger: [0106]-[0107]).
Eger does not disclose wherein the ventilator is configured to ventilate a patient including carry out ventilation as a function of the representation for the respiratory signal.
A second embodiment of Eger discloses an evaluation and signal processing block with computer R2
wherein the ventilator (Fig. 1; ventilator BG; Paragraph 0157, Lines 1-10) is configured to ventilate a patient (patient; Paragraph 0157, Lines 1-10 and Claim 3, Lines 1-4) including carry out ventilation (ventilates the patient; Paragraph 0154, Lines 1-8 and Claim 3, Lines 1-4) as a function of the representation for the respiratory signal (function of the data signals; Claim 3, Lines 1-4; Data signals are based on inspiratory and expiratory activity; Paragraph 0149, Lines 1-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer of the first embodiment of Eger with the computer of the second embodiment of Eger to be able to actuate the ventilator based on data signals received from the patient (Eger: Paragraph 0154, Lines 1-8).
Response to Arguments
Applicant's arguments filed 02/06/2026 have been fully considered but they are not persuasive.
On pages 14-16 of the remarks, Applicant traverses the 101 rejection of claims 1-15. The arguments regarding the 101 rejections of claims 1-14 have not been found persuasive.
More specifically, on page 14 of the remarks, Applicant argues that it is not possible for the human mind to represent a respective heartbeat time, it is impossible or at least not feasible for the human mind to determine a heartbeat time since such a determination requires constant monitoring by a sensor, and the human mind is incapable of determining instantaneously how long a heartbeat lasts. However, the data being produced by the sensor is merely insignificant extra solutional activity. The abstract idea is drawn to calculating a heartbeat time and there are known methods for determining calculating a heartbeat time quickly either mentally or on paper (see Article: Calculating a Heart Rate from an ECG; London Health Sciences Centre). For example, the big box method is a quick way to calculate heart rate (divide 300 by the number of boxes between R-peaks) which can be used to calculate the RR interval (RR interval = 60/heart rate). An example would be that there are five large boxes between the R peaks; therefore, heart rate = 300/5 = 60 bpm and RR interval equals 1 second. Therefore, it is possible for the human mind to calculate the respective heartbeat time. Regardless, the abstract idea of calculating a heartbeat time as claimed in claims 1-14 are merely calculations which are being executed by a processor which are known (see 103 rejections above using Eger in view of Pan-Tompkins). In order to integrate the abstract idea into practical application, the calculations needs to used in a closed loop system such as the limitation claim 15 regarding the ventilator being controlled based on the calculations.
On pages 14-15 of the remarks, Applicant further argues that the characteristic heartbeat time per heartbeat for a sequence of heartbeat of a patient must be represented in real time, i.e. without delay, as the information is critical in a medical setting. If a person were to represent the characteristic heartbeat time per heartbeat on paper by hand, it would take way too long and would not provide the instantaneous representation that is required by medical staff. In addition the human mind is not able to look at the heart rate or data and calculate the time difference between the QRS complexes quickly enough for a medical setting where time is of the essence and waiting to calculate a time difference between QRS complexes by the human mind would take far too long as no medical professional would wait for a person to make such a calculation as such delay may lead to complications or death. Further, that it is not possible to directly measure the heartbeat times as the cardiac signals are superimposed by the respiratory signals. As stated above, it is possible to calculate a characteristic heartbeat time per heartbeat mentally or on paper quickly, but regardless such calculation is a mathematical concept that can be executed on a generic processor and therefore be done in real-time (see 103 rejection above using Eger which detects and suppresses QRS complexes/EKG signals on an EMS signal that has respiratory signals [0106]-[0107] in view of Pan-Tompkins algorithm which detects QRS complexes in real time and uses/calculates RR interval averages, see pages 231-232). In order to overcome such limitation, the abstract idea needs to either be integrated into a particular practical application (e.g. calculations control ventilator) or fundamentally improve computer technology. In this case of claims 1 and 12, the claims are merely calculating the characteristic heartbeat time and a representation of said time without integrating claims into practical application, nor is it an improvement of computer technology.
On pages 15-16 of the remarks, Applicant argues that the limitations of claim 15 overcome the 101 rejection as it is integrated into practical application has been considered persuasive due to the calculations being used to control the ventilator, thereby, integrating the abstract idea into practical application.
On pages 16-19 of the remarks, Applicant argues that neither the prior art of Eger, second embodiment of Eger, or Capodilupo et al. discloses calculating a first detection result for a respective characteristic heartbeat time (exact time point) by analyzing a first sum signal with the first detector as claimed. While the Examiner disagrees that Eger does not disclose said claim limitation as a characteristic heartbeat time does not necessarily need to be an exact time point or time point as a characteristic heartbeat time can be broadly interpreted as the amount of time for a characteristic heartbeat or a time at which a characteristic heartbeat occurred. Regardless, Eger which discusses using the Pan-Tompkins algorithm (A Real-Time QRS Detection Algorithm) to help detect and suppress the heart signal components has been now been modified using the article of Pan-Tompkins to provide obviousness of further including the RR-interval averaging and limit values of said algorithm to adapt to changing characteristic of the signal, therefore, increasing reliable QRS detection. Therefore, the argument regarding the characteristic heartbeat time is moot.
Other noted heartbeat detection algorithms/prior art that calculate heartbeat times include (see pertinent art for further explanation): Sattler (US 20140142395), Blake (US 20160367157), and Parfenova (US 20190175858).
Allowable Subject Matter
Claims 7-8 would be allowable if rewritten to overcome the 101 rejections, 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding claim 7, closest prior art record is Eger (US 20180344194) in view of Pan-Tompkins (Article: A Real-Time QRS Detection Algorithm) and further in view of Kinast (US 9408542).
In particular Eger discloses
a process (Eger: using a device which can represent/show the EKG signal of the heart and respiratory activity with time windows; Pan-Tompkins: RR-interval averages for QRS complex detection) in accordance with claim 5, wherein:
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) calculates, for N heartbeats (Eger: I samples/iterations; Paragraph 0130, Lines1-6 and Paragraph 0136, Lines 1-7), a respective additional representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for each of the respective characteristic heartbeat times (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be updated for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) of the N heartbeats (Eger: I samples/iterations; Paragraph 0130, Lines1-6 and Paragraph 0136, Lines 1-7); wherein N is an integer greater than 1 (Eger: I samples/iterations (I+1); Paragraph 0130, Lines1-6 and Paragraph 0136, Lines 1-7) .
the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4) calculates the first detector (Eger: Figs. 5, 15, 18; FIR filter F11 and detection step DST; Paragraph 0103, Lines 1-4; Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; filter detects/separates EKG signal and detection steps detects heart signal components) and for the second detector (Eger: Figs. 5, 15, 18; FIR filter FPQ and detection step DST; Paragraph 0103, Lines 1-4 Paragraph 0128, Lines 1-8; Paragraph 0147, Lines 1-8; each filter is for a different signal)) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal) and the respective additional representation (Eger: Figs. 16; E11-E13; Paragraph 0144, Lines 1-3; Paragraph 0129, Lines 1-8 and Paragraph 0130, Lines 1-5; calculates separated signals using signal segments and samples of each EMS signal) for the characteristic heartbeat time (Eger: time range; Paragraph 0134, Lines 1-4 and Paragraph 0135, Lines 15-18; Pan-Tompkins: calculates RR-interval averages that must be update for each heartbeat to make QRS complex detection more accurate; pages 231-232; calculates R peak time (characteristic heartbeat time) for each signal)
Eger does not disclose wherein the calculated additional representation for the characteristic heartbeat times has a higher reliability than the calculated representation for each characteristic heartbeat time; the signal processing unit calculates a respective statistical deviation indicator for the deviation between the detection result for the first detector and for the second detector for the characteristic heartbeat time and the respective additional representation for the characteristic heartbeat time, which additional representation is calculated with higher reliability; wherein the signal processing unit calculates the statistical deviation indicator by using the N additional representations; and wherein for the first detector and / or for the second detector, the signal processing unit corrects, by calculation, each additional detection result provided by the first detector and the second detector based on the statistical deviation indicator calculated for the first detector and for the second detector.
Kinast discloses a parameter calculation system for blood pressure and PPWT where
the signal processing unit (Fig. 12; signal averaging process using parameter calculation system 100; Col. 25, Lines 51-57) calculates a respective statistical deviation indicator for the deviation (Figs. 11-12; standard deviation of heart rate data; Col. 5, Line 62; Col. 25, Lines 58-67 and Col. 26, Lines 1-3) between the detection result (heart rate signal; Col. 25, Lines 16-23) for the characteristic heartbeat time (Fig. 11; time (shown in minutes); Col. 25, Lines 16-23),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer of Eger with the signal averaging process of Kinast to be able determine dispersion or variability in the heart rate data which can reflect more or less noise in the heart rate data (Col. 25, Lines 58-67 and Col. 26, Lines 1-3).
It directly follows that the resultant computer of Eger combined with the signal averaging process of Kinast would meet the claimed structural limitations since:
The modified method of Eger and Kinast combined disclose
wherein the signal processing unit (Eger: Fig. 1; computer R; Paragraph 0089, Lines 1-7; Paragraph 0031, Lines 1-4; Kinast: Fig. 12; signal averaging process using parameter calculation system 100; Col. 25, Lines 51-57) calculates the statistical deviation indicator (Kinast: Figs. 11-12; standard deviation of heart rate data; Col. 5, Line 62; Col. 25, Lines 58-67 and Col. 26, Lines 1-3) by using the N additional representations (Eger: I samples/iterations; Paragraph 0130, Lines1-6 and Paragraph 0136, Lines 1-7).
However, Eger, Pan-Tompkins, and Kinast fail to teach, disclose, or render obvious
wherein the calculated additional representation for the characteristic heartbeat times has a higher reliability than the calculated representation for each characteristic heartbeat time; which additional representation is calculated with higher reliability; wherein for the first detector and / or for the second detector, the signal processing unit corrects, by calculation, each additional detection result provided by the first detector and the second detector based on the statistical deviation indicator calculated for the first detector and for the second detector.
Claim 8 is also allowable due to its dependency on claim 7 if rewritten to overcome the rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sattler (US 20140142395) – an analogous apparatus and method for data processing of physiological signals (can be used for a respirator) which removes ECG artifacts from an EMG signal through analysis of the fast signal path. See figs. 5a-5c which “removes the R waves of the ECG (FIG. 5A, t=50, t=120, t=290)” [0075] and can use a standard QRS recognition method (e.g., Pan-Tompkins [see J. Pan and W. J. Tompkins: A real time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, Vol. BME-32, No. 3, March 1985]) to make a better distinction between useful signal and R waves [0076]
Blake (US 20160367157) – discloses a method for real-time detection, analysis and application of heart rate variability using a one or more electrodes of a wearable device where it detects a specific wave characteristic from the each of the plurality of heart-beat related waves, triggering an interrupt when the detected specific wave characteristic crosses a trigger point, recording a time value each time when the interrupt is triggered, and determining an interval from the recorded time value.
Parfenova (US 20190175858) – A cardiac cycle detector which provides a control signal to an air flow generator to control timing of providing pressurized air to the human based on the estimated cardiac signal; where in some embodiments, estimations for the timing of the next cardiac cycle are made, several cardiac cycles are detected, and reference points are selected within the cycles (R-peaks in the ECG signal, for instance). The duration of the cycles can be averaged to determine the estimate for the timing of the reference point of the next cycle. Alternatively, a trend analysis of the duration of the last several cycles can be performed in order to estimate the duration of the next cycle more accurately and therefore to determine the timing of the next reference point more precisely. The estimated timing of the next reference point of the cardiac cycle may be adjusted to reflect its position within the ongoing breath cycle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYDNEY REYES RUSSELL whose telephone number is (703)756-4567. The examiner can normally be reached M-F 930am -6pm.
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, Brandy Lee can be reached at (571) 270-7410. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.R.R./Examiner, Art Unit 3785
/VICTORIA MURPHY/Primary Patent Examiner, Art Unit 3785
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