DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 15-17 are objected to because of the following informalities:
Claim 15 line 1 should include a comma “,” between “1” and “further”;
Claim 16 line 1 should include a comma “,” between “2” and “further”;
Claim 17 line 1 should include a comma “,” between “3” and “further”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 12-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 12 recites the limitation "the load input signal" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the load input signal" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the load input signal" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
A method for identifying breath variability events from patient respiratory data obtained during the operation of a mechanical ventilator; the method comprising the steps of:
during operation of the ventilator system while configured according to a predetermined prescription, continuously monitoring and saving patient respiratory data from one or more sensors;
extracting predetermined treatment parameters from the prescription, and using the treatment parameters to determine an epoch and a frequency band;
extracting from the patient respiratory data an input signal;
extracting from the input signal, at least one signal parameter;
determining a Spectral Energy (Es) of the input signal based on the at least one signal parameter, epoch and a frequency band;
resampling the Spectral Energy;
determining a Spectral Entropy (SE) based on the resampled Spectral Energy (Es); and
displaying the Spectral Entropy in graphical form to visually identify one or more breath variability events over the epoch, based on the determined Spectral Entropy.
Dependent claim 18 recites:
A ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 1.
Dependent claim 19 recites:
A ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 2.
Dependent claim 20 recites:
A ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 3.
Step 1:
The examiner finds claim 1-17 drawn to a method and claims 18-20 drawn to machines.
Step 2A Prong 1:
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018).
The claimed steps of identifying, extracting, determining, and resampling recite mental processes capable of being performed in the human mind and/or mathematical concepts.
The examiner notes the steps of “obtaining”, “monitoring”, “saving”, and displaying are being interpreted as insignificant extra-solution activities that amount to necessary data gathering/storage/output.
The step of “identifying” breath variability events disclosed in independent claim 1 is a mental process capable of being performed in the human mind. For example, the human mind is capable of recognizing if a patient is breathing rapidly, slowly, normal/abnormal rhythm, shallow, etc. The step of “extracting” parameters and data disclosed in independent claim 1 can be reasonably interpreted as both a mental process and/or a mathematical concept. As a mental process, the human mind is capable of extracting key points and summarizing a large text. As a mathematical concept, extraction is the process of isolating, identifying, or deriving specific elements, structures, or properties from a larger mathematical object, system, or set of data. The step of “determining” a spectral energy of the input signal disclosed in independent claim 1 is an example of a mental process/and or a mathematical concept. As a mental process, the human mind is capable of recognizing a musical note, a speech vowel, or an animal call. As a mathematical concept, applying Fourier transforms or performing time-frequency analysis can extract spectral energy at every frequency. The step of “resampling” the spectral energy is an example of a mathematical concept. Resampling is a statistical technique that involves repeatedly drawing samples from a dataset to analyze the results and gain insights into the properties of the population from which the data was drawn.
The claimed steps of identifying, extracting, determining, and resampling can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for estimation that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind,
Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind,
University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims 2-17, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1-20 is not integrated into a practical application because:
• The abstract idea amounts to simply implementing the abstract idea on a computing device. For example, the recitations regarding the generic computing components for identifying, extracting, determining, and resampling merely invoke a computer as a tool.
• The data-gathering step (obtaining and monitoring) and the data-output step (displaying) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
• There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computing device that is used as a tool for identifying, extracting, determining, and resampling.
• The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to estimate bio-information.
• The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computing device for identifying, extracting, determining, and resampling. The claims do not apply the obtained prediction to a particular machine. Rather, the data is merely output in a post-solution step.
Step 2B:
The additional elements are identified as follows: sensor and mechanical ventilator.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
• Applicant’s specification (par. 0009) which discloses that a mechanical ventilator are “standardly available” to provide metrics detailing a patient’s breathing history;
• Tarassenko (US 20100298730 A1) which disclose the use of sensors are conventional to monitoring patient breathing rate [par. 0046]; and
• The non-patent literature of record in the application.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 9-10, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ji (US 11925754 B2) in view of Rahman (US 20240423498 A1), Diab (EP 1905352 B1), and Schute (US 20240225557 A1).
Regarding claim 1, Ji teaches a method for identifying breath variability events from patient respiratory data obtained during the operation of a mechanical ventilator; the method comprising the steps of:
during operation of the ventilator system while configured according to a predetermined prescription, continuously monitoring [col. 2 lns. 33-35 “…a monitoring system continuously monitors the patient in real time and sends monitoring data back to the processor…”] and saving patient respiratory data [col. 2 lns. 16-18 “…the memory is used to store the target data calculated by the processor and the designed treatment plan of the patient”] from one or more sensors [col. 2 ln. 33 “monitoring system”];
extracting predetermined treatment parameters from the prescription [abstract “The input system is configured to input the patient's parameters and treatment requirements to realize recordings of original data of a designed treatment plan”].
Ji teaches using the treatment parameters, but fails to teach using the treatment parameters to determine an epoch and a frequency band; extracting from the patient respiratory data an input signal; extracting from the input signal, at least one signal parameter; determining a Spectral Energy (Es) of the input signal based on the at least one signal parameter, epoch and a frequency band; resampling the Spectral Energy; determining a Spectral Entropy (SE) based on the resampled Spectral Energy (Es); and displaying the Spectral Entropy in graphical form to visually identify one or more breath variability events over the epoch, based on the determined Spectral Entropy.
Rahman teaches using the treatment parameters to determine an epoch [see Fig. 4] and a frequency band [0045 “…performing tidal volume estimates 333 based on input features, which include breathing features 325 and may include one or more of time-domain features 335 and frequency-domain features”];
extracting from the patient respiratory data an input signal [0040 “To extract breathing features, particular embodiments use a peak-detection algorithm to find the peaks and valleys in the filtered signal (whether ADR signal or BCG signal)”];
extracting from the input signal, at least one signal parameter [0041 “Once the correct peaks and valleys are identified, particular embodiments can extract breathing features including breathing depth (amplitude), rate, and symmetry”];
determining a Spectral Energy (Es) of the input signal based on the at least one signal parameter, epoch and a frequency band [0047 “detecting breathing features (e.g., phase duration, rate, phase amplitude) from the identified breathing phases, and these features are fed (along with, optionally, one or more of time-domain motion features 750 (e.g., Zero Crossing Rate (ZCR), Area Under Envelope (AUE)) and frequency-domain motion features 760 (e.g., Mel Frequency Cepstral Coefficients (MFCC), Chroma, entropy, energy, spectral density)) to tidal volume estimation model 770…”];
determining a Spectral Entropy (SE) based on the resampled Spectral Energy (Es) [0042 “Time-based features may include e.g., mean and standard error of the input motion signal, and frequency-based features may include, e.g., spectral power and entropy…”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji and incorporate the teachings of Rahman to include using the treatment parameters to determine an epoch and a frequency band, extracting from the patient respiratory data an input signal, extracting from the input signal, at least one signal parameter, determining a Spectral Energy (Es) of the input signal based on the at least one signal parameter, epoch and a frequency band, and determining a Spectral Entropy (SE) based on the resampled Spectral Energy (Es). Doing so configures the system to provide powerful tools for feature extraction, signal characterization, and monitoring of the signal to provide an accurate and efficient analysis of the patient’s condition.
The combination of Ji and Rahman teach determining a spectral energy, but fail to teach resampling the Spectral Energy.
Diab teaches resampling spectral energy [0298 “Energy in the replicated spectral regions can be removed by a sequence of filters incorporating various degrees of resampling and iterated filters of reduced degree”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji and Rahman and incorporate the teachings of Diab to include resampling the Spectral Energy. Doing so configures the system to use a data analysis technique that improves model reliability, estimates variability, prevent overfitting, and helps optimize performance, especially when data is limited.
The combination of Ji and Rahman teach calculating a spectral entropy, but fail to teach displaying the Spectral Entropy in graphical form to visually identify one or more breath variability events over the epoch, based on the determined Spectral Entropy.
Schute teaches displaying the Spectral Entropy in graphical form to visually identify one or more breath variability events over the epoch [0077 “The output unit may include a display for displaying (…) the spectral entropy time series…”, see also 0077 “The signals and information may be presented in a table, a chart, a diagram, or any other types of textual, tabular, or graphical presentation formats”], based on the determined Spectral Entropy [0077].
Regarding claim 2, Ji, Rahman, Diab, and Schute teach the method of claim 1, further comprising the step of analyzing the determined Spectral Entropy to automatically identify and classify said one or more breath variability events as an irregularity [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Regarding claim 3, Ji, Rahman, Diab, and Schute teach the method of claim 1, further comprising the step of analyzing the determined Spectral Entropy to quantify the degree of regularity associated with one of the one or more breath variability events [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Regarding claim 4, Ji, Rahman, Diab, and Schute teach the method of claim 2, further comprising the step of analyzing the determined Spectral Entropy to quantify the degree of regularity associated with one of the one or more breath variability events [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Regarding claim 9, Ji, Rahman, Diab, and Schute teach the method of claim 2, further comprising the step of displaying the one or more identified events [Schute 0077 “The output unit may include a display for displaying the (…) information about the detected physiological events”] and providing a classification associated with the one or more identified events [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Regarding claim 10, Ji, Rahman, Diab, and Schute teach the method of claim 4, further comprising the step of displaying the one or more identified events [Schute 0077 “The output unit may include a display for displaying the (…) information about the detected physiological events”] and providing a classification associated with the one or more identified events [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Regarding claim 18, Ji, Rahman, Diab, and Schute teach a ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 1 [see claim 1 rejection above].
Regarding claim 19, Ji, Rahman, Diab, and Schute teach a ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 2 [see claim 2 rejection above].
Regarding claim 20, Ji, Rahman, Diab, and Schute teach a ventilator system for providing mechanical ventilation to a target person according to a prescription, configured to operate in accordance with the method of Claim 3 [see claim 3 rejection above].
Claims 5-8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ji, Rahman, Diab, and Schute as applied to claims 1-4 above, and further in view of Veschambre (US 20220362498 A1).
Regarding claim 5, Ji, Rahman, Diab, and Schute teach the method of claim 1, wherein Ji teaches an input signal, but fails to explicitly teach the input signal is selected from the group consisting of: a patient flow signal (Qp) in a pressure-controlled ventilation, a total flow signal (Qt) in a pressure-controlled ventilation, and a pressure signal (P) in a volume-controlled ventilation.
Veschambre teaches the input signal is a total flow signal (Qt) in a pressure-controlled ventilation [0030 “…a total flow signal provided by a flow sensor of a generator of an RPT”, see also 0030 “These detected events provide an evaluation of a patient's condition and may be applied in an automated control system such as for making therapy adjustments such as a change to a pressure control parameter (e.g., pressure set point) or flow control parameter (e.g., a flow set point) involved in the control of a respiratory therapy device”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Veschambre to include the input signal is a total flow signal (Qt) in a pressure-controlled ventilation. Doing so configures the system to protect the lungs, optimize oxygenation, and maintain effective ventilation while adapting to the patient’s changing respiratory mechanics.
Regarding claim 6, Ji, Rahman, Diab, and Schute teach the method of claim 2, wherein Ji teaches an input signal, but fails to explicitly teach the input signal is selected from the group consisting of: a patient flow signal (Qp) in a pressure-controlled ventilation, a total flow signal (Qt) in a pressure-controlled ventilation, and a pressure signal (P) in a volume-controlled ventilation.
Veschambre teaches the input signal is a total flow signal (Qt) in a pressure-controlled ventilation [0030 “…a total flow signal provided by a flow sensor of a generator of an RPT”, see also 0030 “These detected events provide an evaluation of a patient's condition and may be applied in an automated control system such as for making therapy adjustments such as a change to a pressure control parameter (e.g., pressure set point) or flow control parameter (e.g., a flow set point) involved in the control of a respiratory therapy device”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Veschambre to include the input signal is a total flow signal (Qt) in a pressure-controlled ventilation. Doing so configures the system to protect the lungs, optimize oxygenation, and maintain effective ventilation while adapting to the patient’s changing respiratory mechanics.
Regarding claim 7, Ji, Rahman, Diab, and Schute teach the method of claim 3, wherein Ji teaches an input signal, but fails to explicitly teach the input signal is selected from the group consisting of: a patient flow signal (Qp) in a pressure-controlled ventilation, a total flow signal (Qt) in a pressure-controlled ventilation, and a pressure signal (P) in a volume-controlled ventilation.
Veschambre teaches the input signal is a total flow signal (Qt) in a pressure-controlled ventilation [0030 “…a total flow signal provided by a flow sensor of a generator of an RPT”, see also 0030 “These detected events provide an evaluation of a patient's condition and may be applied in an automated control system such as for making therapy adjustments such as a change to a pressure control parameter (e.g., pressure set point) or flow control parameter (e.g., a flow set point) involved in the control of a respiratory therapy device”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Veschambre to include the input signal is a total flow signal (Qt) in a pressure-controlled ventilation. Doing so configures the system to protect the lungs, optimize oxygenation, and maintain effective ventilation while adapting to the patient’s changing respiratory mechanics.
Regarding claim 8, Ji, Rahman, Diab, and Schute teach the method of claim 4, wherein Ji teaches an input signal, but fails to explicitly teach the input signal is selected from the group consisting of: a patient flow signal (Qp) in a pressure-controlled ventilation, a total flow signal (Qt) in a pressure-controlled ventilation, and a pressure signal (P) in a volume-controlled ventilation.
Veschambre teaches the input signal is a total flow signal (Qt) in a pressure-controlled ventilation [0030 “…a total flow signal provided by a flow sensor of a generator of an RPT”, see also 0030 “These detected events provide an evaluation of a patient's condition and may be applied in an automated control system such as for making therapy adjustments such as a change to a pressure control parameter (e.g., pressure set point) or flow control parameter (e.g., a flow set point) involved in the control of a respiratory therapy device”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Veschambre to include the input signal is a total flow signal (Qt) in a pressure-controlled ventilation. Doing so configures the system to protect the lungs, optimize oxygenation, and maintain effective ventilation while adapting to the patient’s changing respiratory mechanics.
Regarding claim 11, Ji, Rahman, Diab, Schute, and Veschambre teach the method of claim 6, further comprising the step of displaying the one or more identified events [Schute 0077 “The output unit may include a display for displaying the (…) information about the detected physiological events”] and providing a classification associated with the one or more identified events [Schute 0077 “…classifying a breathing pattern, or detecting a physiological event or condition”].
Claims 12-13 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ji, Rahman, Diab, and Schute as applied to claims 1-4 above, and further in view of Nakata (US 20140194793 A1).
Regarding claim 12, Ji, Rahman, Diab, and Schute teach the method of claim 2, wherein the combination of Ji and Scute teach the system determines a breath variability event and a display, but fails to teach isolating the input signal and visually displaying the isolated portion of the load input signal.
Nakata teaches isolating the input signal [0079 “…a demodulation algorithm executed by one or more processors to isolate a signal corresponding to a physiological movement of the subject or a part of the subject…”] and visually displaying the isolated portion of the load input signal [0079 “…providing information related to at least the rate of the physiological movement of the subject or a part of the subject to an output unit that is configured to output the information”, see also 0063 “…the output system includes a display unit configured to display the information”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Nakata to include isolating the input signal and visually displaying the isolated portion of the load input signal. Doing so configures the system to provide for an efficient means to present data to a user so that a rapid inference on the data may be conducted.
Regarding claim 13, Ji, Rahman, Diab, and Schute teach the method of claim 4, wherein the combination of Ji and Scute teach the system determines a breath variability event and a display, but fails to teach isolating the input signal and visually displaying the isolated portion of the load input signal.
Nakata teaches isolating the input signal [0079 “…a demodulation algorithm executed by one or more processors to isolate a signal corresponding to a physiological movement of the subject or a part of the subject…”] and visually displaying the isolated portion of the load input signal [0079 “…providing information related to at least the rate of the physiological movement of the subject or a part of the subject to an output unit that is configured to output the information”, see also 0063 “…the output system includes a display unit configured to display the information”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Nakata to include isolating the input signal and visually displaying the isolated portion of the load input signal. Doing so configures the system to provide for an efficient means to present data to a user so that a rapid inference on the data may be conducted.
Regarding claim 15, Ji, Rahman, Diab, and Schute teach method of claim 1, wherein Diab teaches calculating an RMS, but fails to explicitly teach determining the RMS, standard deviation or variance of the input signal in the epoch; and displaying the RMS, standard deviation or variance as a relative measure of variability.
Nakata teaches determining a standard deviation of the input signal in the epoch [0245 “…the subset of frames can include samples obtained over a period of time longer than the expected cycle period of irregular respiration (…) the irregularity of the breath-to-breath interval, or breath duration, can be estimated from one or more of the following: the standard deviation of the breath-to-breath interval…”] and displaying the standard deviation as a relative measure of variability [0341 “…the device may display trends in the respiratory rate on a graph that has the rate on the y-axis and time on the x-axis. In various embodiments, the device may also indicate the mean and standard deviation of the rate”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Nakata to include determining a standard deviation of the input signal in the epoch and displaying the standard deviation as a relative measure of variability. Doing so provides a clear, quantifiable measure of how much the signal’s amplitude fluctuates around its mean, which directly reflects the spread or dispersion of the data, which can represent noise, modulation depth, or instability.
Regarding claim 16, Ji, Rahman, Diab, and Schute teach method of claim 2, wherein Diab teaches calculating an RMS, but fails to explicitly teach determining the RMS, standard deviation or variance of the input signal in the epoch; and displaying the RMS, standard deviation or variance as a relative measure of variability.
Nakata teaches determining a standard deviation of the input signal in the epoch [0245 “…the subset of frames can include samples obtained over a period of time longer than the expected cycle period of irregular respiration (…) the irregularity of the breath-to-breath interval, or breath duration, can be estimated from one or more of the following: the standard deviation of the breath-to-breath interval…”] and displaying the standard deviation as a relative measure of variability [0341 “…the device may display trends in the respiratory rate on a graph that has the rate on the y-axis and time on the x-axis. In various embodiments, the device may also indicate the mean and standard deviation of the rate”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Nakata to include determining a standard deviation of the input signal in the epoch and displaying the standard deviation as a relative measure of variability. Doing so provides a clear, quantifiable measure of how much the signal’s amplitude fluctuates around its mean, which directly reflects the spread or dispersion of the data, which can represent noise, modulation depth, or instability.
Regarding claim 17, Ji, Rahman, Diab, and Schute teach method of claim 3, wherein Diab teaches calculating an RMS, but fails to explicitly teach determining the RMS, standard deviation or variance of the input signal in the epoch; and displaying the RMS, standard deviation or variance as a relative measure of variability.
Nakata teaches determining a standard deviation of the input signal in the epoch [0245 “…the subset of frames can include samples obtained over a period of time longer than the expected cycle period of irregular respiration (…) the irregularity of the breath-to-breath interval, or breath duration, can be estimated from one or more of the following: the standard deviation of the breath-to-breath interval…”] and displaying the standard deviation as a relative measure of variability [0341 “…the device may display trends in the respiratory rate on a graph that has the rate on the y-axis and time on the x-axis. In various embodiments, the device may also indicate the mean and standard deviation of the rate”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, and Schute and incorporate the teachings of Nakata to include determining a standard deviation of the input signal in the epoch and displaying the standard deviation as a relative measure of variability. Doing so provides a clear, quantifiable measure of how much the signal’s amplitude fluctuates around its mean, which directly reflects the spread or dispersion of the data, which can represent noise, modulation depth, or instability.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ji, Rahman, Diab, Schute, and Veschambre as applied to claim 6 above, and further in view of Nakata.
Regarding claim 14, Ji, Rahman, Diab, Schute, and Veschambre teach the method of claim 6, wherein the combination of Ji and Scute teach the system determines a breath variability event and a display, but fails to teach isolating the input signal and visually displaying the isolated portion of the load input signal.
Nakata teaches isolating the input signal [0079 “…a demodulation algorithm executed by one or more processors to isolate a signal corresponding to a physiological movement of the subject or a part of the subject…”] and visually displaying the isolated portion of the load input signal [0079 “…providing information related to at least the rate of the physiological movement of the subject or a part of the subject to an output unit that is configured to output the information”, see also 0063 “…the output system includes a display unit configured to display the information”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take the teachings of Ji, Rahman, Diab, Schute, and Veschambre and incorporate the teachings of Nakata to include isolating the input signal and visually displaying the isolated portion of the load input signal. Doing so configures the system to provide for an efficient means to present data to a user so that a rapid inference on the data may be conducted.
Conclusion
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/JONATHAN M HANEY/ Examiner, Art Unit 3791
/JUSTIN XU/ Primary Examiner, Art Unit 3791
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