CTNF 18/755,304 CTNF 95793 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 5 is 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. 07-34-03 AIA The term “ about ” in claim 5 is a relative term which renders the claim indefinite. The term “ about ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is not clear what range of breaths/minute is encompassed by the limitation "about 30 breaths/minute" . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 U.S.C. 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong One), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then in the second part of Step 2A (Prong Two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination provide "inventive concept" that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 U.S.C. 101. Claims 1-15 are rejected under 35 U.S.C. 101. Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. The claim recite(s) "select between the flow-based respiratory rate and the spectrum-based respiratory rate based on a respiratory rate threshold". This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 satisfies Step 1, namely the claim is directed to one of the four statutory classes, machine. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations "select between the flow-based respiratory rate and the spectrum-based respiratory rate based on a respiratory rate threshold" are abstract ideas as they are directed to a mental process. With the identification of an abstract idea, the next phase is to proceed Step 2A, Prong Two, wherewith additional elements and taken as a whole, evaluation occurs of whether the identified abstract idea is integrated into a practical application. In Step 2A, Prong Two, the claim does not recite any additional elements or evidence that amounts to significantly more than the judicial exception. Besides the abstract idea, the claim recites the additional elements “a depth-sensing camera; a display; and a processor and a memory storing instructions therein that, when executed by the processor, cause the processor to: determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements; calculate a respiratory flow waveform from the respiratory volume over a time period; determine a flow-based respiratory rate from the respiratory flow waveform; calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum” and “show the selected respiratory rate on the display”. However, these components may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The measurement step leads to the final limitation of “show[ing]” such that the end result of use of the system is only the generic display. As this is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the measurement of data relating to depth measurements and generation of respiratory rates from these measurements, such that the claim does not integrate the judicial exception into any practical application. Regarding “a processor”, the limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer, which does not render an abstract idea eligible. The steps performed by the processor are, as claimed, capable of being performed in the human mind similar to the examples given in MPEP 2106.04(a)(2)(III)(A)-(C), wherein it is described that “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” recites a mental process and that claims which merely use a computer as a tool to perform a mental process are not eligible when “there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper” such as “mental processes of parsing and comparing data” when the steps are recited at a high level of generality and a computer is used merely as a tool to perform the processes. Furthermore, while the abstract idea itself may be an improvement over the existing technology, per Genetic Technologies Limited v. Merial LLC (Fed Cir., 2016) the inventive concept of step 2 of the Alice/Mayo analysis cannot be supplied by the abstract idea . The inventive concept necessary at step two of the Mayo/Alice analysis cannot be furnished by the unpatentable abstract idea itself . That is, under the Mayo/Alice framework, a claim directed to a newly discovered abstract idea cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility; instead, the application must provide something inventive, beyond mere “well-understood, routine, conventional activity.” Mayo, 132 S. Ct. at 1294; see also Myriad, 133 S. Ct. at 2117; Ariosa, 788 F.3d at 1379. For this reason, the abstract limitations may not be seen as an improvement which integrates the judicial exception into a practical application. As the additional limitations of the claim are not an improvement over the existing technology (see Hajj-Ali “Towards Depth-based Respiratory Rate Estimation with Arbitrary Camera Placement” which discusses the use of depth sensing cameras to measure respiratory rates using each of a flow-based and a spectrum-based method where these means and methods are known in the art), it may be seen that the claim fails to integrate the judicial exception into a practical application via an improvement to the technology. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea. Under the broadest reasonable interpretation, the claim elements are recited with a high level of generality (as written, each claimed step of the processor may be performed by a person in an undefined manner including making transformations of data received from a sensor mentally, with aid of pen and paper, or using a generic computer) that there are no meaningful limitations to the abstract idea. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea. In Step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “a depth-sensing camera; a display; and a processor and a memory storing instructions therein that, when executed by the processor, cause the processor to: determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements; calculate a respiratory flow waveform from the respiratory volume over a time period; determine a flow-based respiratory rate from the respiratory flow waveform; calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum” and “show the selected respiratory rate on the display” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic camera, processor, and memory, which Hajj-Ali (“Towards Depth-based Respiratory Rate Estimation with Arbitrary Camera Placement”) describes as well-understood, routine, or conventional in its description of existing studies which utilize depth sensing cameras and computer processing to determine respiratory rates and commercially available cameras usable for depth sensing (Page 1-2—I. Introduction; Page 2—II. Data Collection--RGB-D camera (Intel RealSense SR300)…recorded using the custom Patient Monitor Data Import (PMDI) software developed for the project [18]…; Page 4—C. Respiratory Rate Estimation—methods were adapted from the work of A. Bekele [21]). As discussed above with respect to integration of the abstract idea into a practical application, the present elements amount to no more than mere indications to apply the exception. In Summary, claim 1 recites abstract idea without being integrated into a practical application, and does not provide additional elements that would amount to significantly more. As such, taken as a whole, the claim and is ineligible under the 35 U.S.C. 101. Claims 11 and 15 are rejected for similar reasons. Claims 2-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 1, which was rejected under 35 U.S.C. 101 in paragraph 8 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 1, or comprise significantly more than the limitations of claim 1. Besides the abstract idea of claim 1: claims 2-5 and 8 recites further limitations of the abstract idea which are additionally abstract as they are directed toward details of a judgment which can be made in the human mind; claims 7-10 recite additional limitations which may be considered extra-solution activity which does not integrate the abstract idea into a practical application as the limitations are directed toward further details of the mere gathering of data to be provided to the abstract idea, specifically the processing of the received data prior to making the human judgment. The claim element of claim 1 of a system for determining a respiratory rate of a subject is recited with a high level of generality (as written, the actions of the processor may be carried out by a person alone or with a generic computer in any undefined manner). The limitations provide no practical application, nor do they provide meaningful limitations to the abstract idea. Claims 12-14 are rejected for similar reasons. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim(s) 1-3, 5, 7, and 9-15 is/are rej ected under 35 U .S.C. 103 as being unpatentable over Addison (US 20190209046 A 1) in view of Hajj-Ali ("Towards Depth-based Respiratory Rate Estimation with Arbitrary Camera Placement"), further in view of Sun ("An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images"). Regarding claim 1 , Addison teaches a non-contact monitoring system for determining a respiratory rate of a subject (Paragraph 0081-- using a depth sensing camera to monitor a patient and determine movements of their chest and/or other body parts as the patient breathes; video-based patient monitoring system 200), the system comprising: a depth-sensing camera (Paragraph 0081-0084-- camera 214 may be a depth sensing camera…; non-contact detector 210 including camera 214); a display (Display 222); and a processor (Processor 218) and a memory (Memory 226) storing instructions therein that, when executed by the processor (Paragraph 0088), cause the processor to: determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Paragraph 0087-0088-- The captured images/video can be processed or analyzed at the computing device and/or a server to determine tidal volume of the patient 212); calculate a respiratory flow waveform from the respiratory volume over a time period (Paragraph 0095-0096, 0099-0100-- As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI 395 changes over time. The position of individual points within the ROI 395 may be integrated across the area of the ROI 395 to provide a change in volume over time as shown in FIGS. 4 and 5. FIG. 4 is a graph showing a tidal volume calculation over time according to various embodiments described herein…); determine a flow-based respiratory rate from the respiratory flow waveform (Paragraph 0096-0097-- This movement forward and backward can be tracked to determine a respiration rate… The peaks and valleys of the signal in FIG. 4 can be used to identify individual breaths, the size of individual breaths, and a patient's overall respiration rate…) and show the respiratory rate on the display (Paragraph 0002, 0089-- determines vital signs (e.g., pulse rate, respiration rate, arterial oxygen saturation), and displays the vital signs on a display). However, Addison does not explicitly disclose calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum. Hajj-Ali, in the same field of endeavor of depth-based respiratory rate estimation, teaches a system configured to determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Page 2, II. Data Collection and III. Methods—An RGB-D camera (Intel RealSense SR300) was placed above or around the patient’s bed. The cameras were placed such that the viewplanes were at arbitrary angles relative to the plane of the bed. The SR300 uses a combination of an IR projector and IR camera sensor to generate a depth pixel frame…); calculate a respiratory flow waveform from the respiratory volume over a time period (Page 4, C. Respiratory Rate Estimation-- A single signal over each of the investigated time segments was first derived from the depth frames); determine a flow-based respiratory rate from the respiratory flow waveform (Page 4, C. Respiratory Rate Estimation-- RR estimation method in the time domain involves extracting peaks and calculating the period of the signal…); calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum (Page 4, C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution…). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Addison to determine an additional spectrum-based respiratory rate as described by Hajj-Ali in order to predictably improve the accuracy of the system by providing two different means of determining respiratory rate which have different advantages relating to motion artifacts and noise for particular respiratory rates and thus may improve the accuracy of the system. However, the combination of Addison and Hajj-Ali does not explicitly disclose select between the flow-based respiratory rate and the spectrum-based respiratory rate based on a respiratory rate threshold. Sun, in the same field of endeavor of a system for non-contact monitoring to determine a respiratory rate of a subject using a depth-sensing camera, discloses that an estimated respiratory rate can be an indicator for further processing steps (Page 8307, left column—“ the RR calculated by the first step can be exploited as an indicator to reflect the respiratory condition ") wherein comparing the estimated respiratory rate to a threshold can be used to select how the respiratory signal is processed, where the reason for this is that at higher respiratory rates, the higher frequency components may be desirable while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system of Addison and Hajj-Ali to utilize a threshold to select a processing method as disclosed by Sun in order to predictably improve the accuracy of the system by allowing the processing method outcome which is displayed to be changed based on which method is likely to be the most accurate. Regarding claim 2 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. Addison does not explicitly disclose wherein the processor selects the spectrum-based respiratory rate dependent on either or both the spectrum-based respiratory rate and the flow-based respiratory rate is above the respiratory rate threshold. Hajj-Ali additionally notes that low frequency components may be motion artifacts which can affect the accuracy of the spectrum-based respiratory rate (Page 4, C. Respiratory Rate Estimation, right column). Sun discloses that higher frequency components may be desirable for a user having a higher respiratory rate while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system, which utilizes a threshold to select between a flow-based and a spectrum-based respiratory rate, with the teachings of Hajj-Ali and Sun, which effectively disclose that a spectrum-based approach may be more suited to higher respiratory rates and frequencies and a flow-based approach may be more suited to lower respiratory rates and frequencies in order to predictably improve the system by selecting the spectrum-based respiratory rate dependent on either or both the spectrum-based respiratory rate and the flow-based respiratory rate is above the respiratory rate threshold to predictably improve the selection of the more accurate respiratory rate for display to the user. Regarding claim 3 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. Addison does not explicitly disclose wherein the processor selects the flow-based respiratory rate dependent on either or both the spectrum-based respiratory rate and the flow-based respiratory rate is below the respiratory rate threshold. Hajj-Ali additionally notes that low frequency components may be motion artifacts which can affect the accuracy of the spectrum-based respiratory rate (Page 4, C. Respiratory Rate Estimation, right column). Sun discloses that higher frequency components may be desirable for a user having a higher respiratory rate while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system, which utilizes a threshold to select between a flow-based and a spectrum-based respiratory rate, with the teachings of Hajj-Ali and Sun, which effectively disclose that a spectrum-based approach may be more suited to higher respiratory rates and frequencies and a flow-based approach may be more suited to lower respiratory rates and frequencies in order to predictably improve the system by selecting the flow-based respiratory rate dependent on either or both the spectrum-based respiratory rate and the flow-based respiratory rate is below the respiratory rate threshold to predictably improve the selection of the more accurate respiratory rate for display. Regarding claim 5 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. Addison does not explicitly disclose wherein the respiratory rate threshold is about 30 breaths/minute. Sun additionally teaches wherein the respiratory rate threshold is about 30 breaths/minute and wherein the threshold can be determined from routine experimentation (Page 8307—preliminary tests verified that the RR 14 and 17 as the threshold values can achieve promising results… The three different p values in the second step were selected based on empirical experiments; equation 9). Sun additionally teaches wherein the threshold can be determined from routine experimentation such that both methods of processing the signal to determine the respiratory rate may be performed prior to the outputting on screen (Page 8307—preliminary tests verified that the RR 14 and 17 as the threshold values can achieve promising results… The three different p values in the second step were selected based on empirical experiments; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system to utilize a particular threshold respiratory rate according to the teachings of Sun, where a rate which attains greater accuracy for one processing method over another may be determined through routine experimentation and would predictably improve the accuracy of the system by enabling the selection of the more accurate respiratory rate for display. Regarding claim 7 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. Addison additionally teaches wherein the processor manipulates the respiratory flow waveform prior to the processor determining the flow-based respiratory rate (Paragraph 0100-- the volume signal from the non-contact system may need to be calibrated to provide an absolute measure of volume). Hajj-Ali additionally teaches wherein the processor manipulates the respiratory flow waveform prior to the processor determining the flow-based respiratory rate (Page 4—a band-pass filter in the form of a second-order Butterworth filter is then applied to the signal…to eliminate any high or low frequency signal artifacts…). Regarding claim 9 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. Hajj-Ali additionally teaches wherein the processor manipulates the power spectrum prior to the processor determining the spectrum-based respiratory rate (Page 4—C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution. It is assumed that the largest power contribution is attributable to the breathing signal, since the sections of the recordings that were selected had minimal movement and other factors affecting the scene. Future work will include more robust filtering to remove low frequency motion artifacts...; Equation 8). Regarding claim 10 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 9. Hajj-Ali additionally teaches wherein the processor manipulates the power spectrum by applying a linear transformation to the power spectrum, a frequency scaling to the power spectrum, or a second derivative transformation to the power spectrum (Equation 8 demonstrates a linear transformation to the power spectrum). Regarding claim 11 , Addison teaches a non-contact monitoring system for determining a respiratory rate of a subject (Paragraph 0081-- using a depth sensing camera to monitor a patient and determine movements of their chest and/or other body parts as the patient breathes; video-based patient monitoring system 200), the system comprising: a depth-sensing camera (Paragraph 0081-0084-- camera 214 may be a depth sensing camera…; non-contact detector 210 including camera 214); a display (Display 222); and a processor (Processor 218) and a memory (Memory 226) storing instructions therein that, when executed by the processor (Paragraph 0088), cause the processor to: determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Paragraph 0087-0088-- The captured images/video can be processed or analyzed at the computing device and/or a server to determine tidal volume of the patient 212); calculate a respiratory flow waveform from the respiratory volume over a time period (Paragraph 0095-0096, 0099-0100-- As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI 395 changes over time. The position of individual points within the ROI 395 may be integrated across the area of the ROI 395 to provide a change in volume over time as shown in FIGS. 4 and 5. FIG. 4 is a graph showing a tidal volume calculation over time according to various embodiments described herein…); determine a respiratory rate from the respiratory flow waveform (Paragraph 0096-0097-- This movement forward and backward can be tracked to determine a respiration rate… The peaks and valleys of the signal in FIG. 4 can be used to identify individual breaths, the size of individual breaths, and a patient's overall respiration rate…) and show the respiratory rate on the display (Paragraph 0002, 0089-- determines vital signs (e.g., pulse rate, respiration rate, arterial oxygen saturation), and displays the vital signs on a display). However, Addison does not explicitly disclose calculate a power spectrum from the respiratory flow waveform and determine a respiratory rate from the power spectrum. Hajj-Ali, in the same field of endeavor of depth-based respiratory rate estimation, teaches a system configured to determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Page 2, II. Data Collection and III. Methods—An RGB-D camera (Intel RealSense SR300) was placed above or around the patient’s bed. The cameras were placed such that the viewplanes were at arbitrary angles relative to the plane of the bed. The SR300 uses a combination of an IR projector and IR camera sensor to generate a depth pixel frame…); calculate a respiratory flow waveform from the respiratory volume over a time period (Page 4, C. Respiratory Rate Estimation-- A single signal over each of the investigated time segments was first derived from the depth frames); determine a flow-based respiratory rate from the respiratory flow waveform (Page 4, C. Respiratory Rate Estimation-- RR estimation method in the time domain involves extracting peaks and calculating the period of the signal…); calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum (Page 4, C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution…). Hajj-Ali additionally notes that low frequency components may be motion artifacts which can affect the accuracy of the spectrum-based respiratory rate (Page 4, C. Respiratory Rate Estimation, right column). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Addison to determine an additional spectrum-based respiratory rate as described by Hajj-Ali in order to predictably improve the accuracy of the system by providing two different means of determining respiratory rate which have different advantages relating to motion artifacts and noise for particular respiratory rates and thus may improve the accuracy of the system. However, the combination of Addison and Hajj-Ali does not explicitly disclose dependent on the respiratory rate from the respiratory flow waveform being above a threshold , calculate a power spectrum from the respiratory flow waveform and determine a respiratory rate from the power spectrum and dependent on the respiratory rate from the power spectrum being determined, show on the display the respiratory rate from the power spectrum on the display for a user. Sun, in the same field of endeavor of a system for non-contact monitoring to determine a respiratory rate of a subject using a depth-sensing camera, discloses that an estimated respiratory rate can be an indicator for further processing steps (Page 8307, left column—“ the RR calculated by the first step can be exploited as an indicator to reflect the respiratory condition ") wherein comparing the estimated respiratory rate to a threshold can be used to select how the respiratory signal is processed, where the reason for this is that at higher respiratory rates, the higher frequency components may be desirable while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9). Sun additionally teaches wherein the threshold can be determined from routine experimentation such that both methods of processing the signal to determine the respiratory rate may be performed prior to the outputting on screen (Page 8307—preliminary tests verified that the RR 14 and 17 as the threshold values can achieve promising results… The three different p values in the second step were selected based on empirical experiments; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system of Addison and Hajj-Ali to utilize a threshold to select a processing method as disclosed by Sun in order to predictably improve the accuracy of the system and reduce the processing power required by the system by allowing the processing method performed to be changed based on the likely accuracy of the method according to a threshold, such that additional processing need not be performed if the first method is likely to be accurate based on not exceeding a threshold. It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system to utilize a particular threshold respiratory rate according to the teachings of Sun and the teachings of Hajj-Ali and Sun with respect to a spectrum-based approach may be more suited to higher respiratory rates and frequencies and a flow-based approach may be more suited to lower respiratory rates and frequencies, where a rate which attains greater accuracy for one processing method over another may be determined through routine experimentation and would predictably improve the accuracy of the system by enabling the selection of the more accurate respiratory rate. Regarding claim 12 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 11. Hajj-Ali teaches a system configured to determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Page 2, II. Data Collection and III. Methods—An RGB-D camera (Intel RealSense SR300) was placed above or around the patient’s bed. The cameras were placed such that the viewplanes were at arbitrary angles relative to the plane of the bed. The SR300 uses a combination of an IR projector and IR camera sensor to generate a depth pixel frame…); calculate a power spectrum from the respiratory flow waveform and determine a spectrum-based respiratory rate from the power spectrum (Page 4, C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution…). Hajj-Ali additionally notes that low frequency components may be motion artifacts which can affect the accuracy of the spectrum-based respiratory rate (Page 4, C. Respiratory Rate Estimation, right column). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Addison to determine an additional spectrum-based respiratory rate as described by Hajj-Ali in order to predictably improve the accuracy of the system by providing two different means of determining respiratory rate which have different advantages relating to motion artifacts and noise for particular respiratory rates and thus may improve the accuracy of the system. However, the combination of Addison and Hajj-Ali does not explicitly disclose determines the respiratory rate from the power spectrum dependent on the respiratory rate from the respiratory flow waveform being above 90% of the threshold . Sun discloses that an estimated respiratory rate can be an indicator for further processing steps (Page 8307, left column—“ the RR calculated by the first step can be exploited as an indicator to reflect the respiratory condition ") wherein comparing the estimated respiratory rate to a threshold can be used to select how the respiratory signal is processed, where the reason for this is that at higher respiratory rates, the higher frequency components may be desirable while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9), such that the system may determine the respiratory rate from the power spectrum dependent on the respiratory rate from the respiratory flow waveform being above 90% of the threshold. Sun additionally teaches wherein the threshold can be determined from routine experimentation such that both methods of processing the signal to determine the respiratory rate may be performed prior to the outputting on screen (Page 8307—preliminary tests verified that the RR 14 and 17 as the threshold values can achieve promising results… The three different p values in the second step were selected based on empirical experiments; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system of Addison and Hajj-Ali to utilize a threshold to select a processing method as disclosed by Sun in order to predictably improve the accuracy of the system and reduce the processing power required by the system by allowing the processing method performed to be changed based on the likely accuracy of the method according to a threshold, such that additional processing need not be performed if the first method is likely to be accurate based on not exceeding a threshold where the teachings of Hajj-Ali and Sun with respect to the accuracy of each approach may be used to inform a threshold where exceeding 90% of the threshold indicates greater accuracy will be obtained from the spectrum-based respiratory rate, where a rate which attains greater accuracy for one processing method over another may be determined through routine experimentation and would predictably improve the accuracy of the system by enabling the selection of the more accurate respiratory rate. It is additionally noted that the phrasing of “dependent on the respiratory rate from the respiratory flow waveform being above 90% of the threshold” includes a respiratory rate being 100% of the threshold or exceeding the threshold, and that a threshold may be modified such that “90% of the threshold” is treated as the new threshold. Regarding claim 13 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 11. Addison additionally teaches wherein the processor manipulates the respiratory flow waveform prior to the processor determining the respiratory rate from the respiratory flow waveform (Paragraph 0100-- the volume signal from the non-contact system may need to be calibrated to provide an absolute measure of volume). Hajj-Ali additionally teaches wherein the processor manipulates the respiratory flow waveform prior to the processor determining the respiratory rate from the respiratory flow waveform (Page 4—a band-pass filter in the form of a second-order Butterworth filter is then applied to the signal…to eliminate any high or low frequency signal artifacts…). Regarding claim 14 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 11. Hajj-Ali additionally teaches wherein the processor manipulates the power spectrum prior to the processor determining the respiratory rate from the power spectrum (Page 4—C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution. It is assumed that the largest power contribution is attributable to the breathing signal, since the sections of the recordings that were selected had minimal movement and other factors affecting the scene. Future work will include more robust filtering to remove low frequency motion artifacts...; Equation 8). Regarding claim 15 , Addison teaches a method for determining an accurate respiratory rate (Paragraph 0081-- using a depth sensing camera to monitor a patient and determine movements of their chest and/or other body parts as the patient breathes; video-based patient monitoring system 200), the method comprising: determining a respiratory volume of a subject with a non-contact monitoring system using depth measurements (Paragraph 0087-0088-- The captured images/video can be processed or analyzed at the computing device and/or a server to determine tidal volume of the patient 212; Paragraph 0081-0084-- camera 214 may be a depth sensing camera…; non-contact detector 210 including camera 214); calculating a respiratory flow waveform from the respiratory volume over a time period (Paragraph 0095-0096, 0099-0100-- As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI 395 changes over time. The position of individual points within the ROI 395 may be integrated across the area of the ROI 395 to provide a change in volume over time as shown in FIGS. 4 and 5. FIG. 4 is a graph showing a tidal volume calculation over time according to various embodiments described herein…); determining a flow-based respiratory rate from the respiratory flow waveform (Paragraph 0096-0097-- This movement forward and backward can be tracked to determine a respiration rate… The peaks and valleys of the signal in FIG. 4 can be used to identify individual breaths, the size of individual breaths, and a patient's overall respiration rate…) and displaying the respiratory rate to a user (Paragraph 0002, 0089-- determines vital signs (e.g., pulse rate, respiration rate, arterial oxygen saturation), and displays the vital signs on a display). However, Addison does not explicitly disclose calculating a power spectrum from the respiratory flow waveform and determining a spectrum-based respiratory rate from the power spectrum. Hajj-Ali, in the same field of endeavor of depth-based respiratory rate estimation, teaches a method to determine a respiratory volume of the subject with the non-contact monitoring system using depth measurements (Page 2, II. Data Collection and III. Methods—An RGB-D camera (Intel RealSense SR300) was placed above or around the patient’s bed. The cameras were placed such that the viewplanes were at arbitrary angles relative to the plane of the bed. The SR300 uses a combination of an IR projector and IR camera sensor to generate a depth pixel frame…), the method comprising: calculating a respiratory flow waveform from the respiratory volume over a time period (Page 4, C. Respiratory Rate Estimation-- A single signal over each of the investigated time segments was first derived from the depth frames); determining a flow-based respiratory rate from the respiratory flow waveform (Page 4, C. Respiratory Rate Estimation-- RR estimation method in the time domain involves extracting peaks and calculating the period of the signal…); calculating a power spectrum from the respiratory flow waveform and determining a spectrum-based respiratory rate from the power spectrum (Page 4, C. Respiratory Rate Estimation-- The second method estimates the RR in the frequency domain by finding the power spectral density of the signal and selecting the frequency with the largest power contribution…). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Addison to determine an additional spectrum-based respiratory rate as described by Hajj-Ali in order to predictably improve the accuracy of the method by providing two different means of determining respiratory rate which have different advantages relating to motion artifacts and noise for particular respiratory rates and thus may improve the accuracy of the system. However, the combination of Addison and Hajj-Ali does not explicitly disclose selecting between the flow-based respiratory rate and the spectrum-based respiratory rate based on a respiratory rate threshold. Sun, in the same field of endeavor of a system for non-contact monitoring to determine a respiratory rate of a subject using a depth-sensing camera, discloses that an estimated respiratory rate can be an indicator for further processing steps (Page 8307, left column—“ the RR calculated by the first step can be exploited as an indicator to reflect the respiratory condition ") wherein comparing the estimated respiratory rate to a threshold can be used to select how the respiratory signal is processed, where the reason for this is that at higher respiratory rates, the higher frequency components may be desirable while at lower respiratory rates, the same components may be noise (Page 8307, left column—Since different subject’s respiratory rate is quite different and the depth camera can capture components of various frequencies, hence, the main component of the respiratory signal of one subject may be noise to other subjects. For example, for the subject whose RR is 23/min, the signal with relative higher frequency components may be the desired respiratory signal, but a component with the same frequency components may be noise for subject whose RR is 8/min; equation 9). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined method of Addison and Hajj-Ali to utilize a threshold to select a processing method as disclosed by Sun in order to predictably improve the accuracy of the method by allowing the processing method outcome which is displayed to be changed based on which method is likely to be the most accurate . 07-21-aia AIA Claim(s) 4 is/ are rejected under 35 U.S.C. 103 as being unpatentable over Add ison in view of Hajj-Ali, further in view of Sun, further in view of Bekele ("Neonatal Respiratory Rate Monitoring using a Pressure-Sensitive Mat"). Reg arding claim 4 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. However, the combination fails to explicitly disclose wherein the processor select both the flow-based respiratory rate and the spectrum-based respiratory rate and provides a composite respiratory rate as the selected respiratory rate. Bekele, in the same field of endeavor of determining a respiratory rate from a sensor signal, discloses determining each of a flow-based and a spectrum-based respiratory rate (Page 41—4.4.1 Time Domain Method-- extracts peaks from the time domain signal and computes the period of the breathing signal as the average inter-peak interval; Page 42—Frequency Domain Method-- to estimate the RR using the frequency domain method, first the power spectral density (PSD) of the breathing signal is computed using Fast Fourier Transform (FFT). Then, the frequency with the highest power contribution, is selected and multiplied by 60 to estimate the RR in bpm). Bekele additionally teaches wherein the processor select both the flow-based respiratory rate and the spectrum-based respiratory rate and provides a composite respiratory rate as the selected respiratory rate (Page 43—4.4.3 Combined method-- The combined method uses both time and frequency domain approaches to estimate RR. This method improves underestimation of the RR due to undetected peaks by the time domain RR method described in section 4.4.1). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system to include a combined method as described by Bekele in order to predictably improve the accuracy of the respiratory rate determination by improving underestimation of the respiratory rate (See Bekele, page 43) . 07-21-aia AIA Claim (s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Addison in view of Hajj-Ali, further in view of Sun, further in view of Scebba ("Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea") . Regarding claim 6 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 1. However, the combination fails to explicitly disclose wherein the processor calculates the power spectrum by utilizing a Lomb-Scargle method. Scebba, in the same field of endeavor of a system for non-contact monitoring of respiratory rate, discloses that a power spectrum may be calculated using a Lomb-Scargle method (Page 353--signals were then windowed and the Lomb-Scargle power spectral density (PSD) [52], [53] computed. The frequency with the highest power density in the range of 0.015–0.75 Hz was selected and converted to breaths/min. The Lomb-Scargle PSD estimator was chosen because cameras often have unstable frame rates and it did not require an evenly sampled time series). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the combined system to utilize a Lomb-Scargle PSD as a substitute for the FFT PSD to predictably improve the accuracy of the spectrum-based respiratory rate as this method does not require evenly sampled time series and thus would not require the depth sensing camera to have a stable frame rate . 07-21-aia AIA Claim (s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Addison in view of Hajj-Ali, further in view of Sun, further in view of Tarsaud (US 20170290548 A1) . Regarding claim 8 , the combination of Addison, Hajj-Ali, and Sun teaches the system of claim 7. The combination is silent as to wherein the processor manipulates the waveform by: detecting a local maxima in a window of the waveform; analyzing the waveform in relation to the local maxima, and determining whether or not to discard the local maxima. Tarsaud, in the same field of endeavor of respiratory monitoring of a user, teaches wherein the processor manipulates the waveform by: detecting a local maxima in a window of the waveform; analyzing the waveform in relation to the local maxima, and determining whether or not to discard the local maxima (Paragraph 0033-0034, 0143-0145-- In particular general motion or movement of the patient's body is detected and samples collected during positioning, or repositioning, can be discarded and will not be used to update respiration rate measurements… A measurement is taken of the maximum sensor data output, the minimum sensor data output and a determination of the average (e.g. mean) sensor data output for the window is also made… If it is determined that the MD value for any segment is greater than a given threshold, threshold1, and/or it is determined that max(MD)−median(MD)>threshold2 (where max(MD) is the maximum MD value for all the segments, median(MD) is the median value for all the segments, and threshold2 is a second threshold value), then the entire window is discarded). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the particular manipulations of Addison to include the waveform maxima manipulations of Tarsaud in order to predictably improve the accuracy of the system by excluding data which is likely to include motion of the user which would otherwise alter the determined respiratory rate (see Tarsaud, paragraphs 0143-0145). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA ROBERTS whose telephone number is (571)272-7912. The examiner can normally be reached M-F 8:30-4:30 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at (571) 272-4233. 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. /ANNA ROBERTS/Examiner, Art Unit 3791 Application/Control Number: 18/755,304 Page 2 Art Unit: 3791 Application/Control Number: 18/755,304 Page 3 Art Unit: 3791 Application/Control Number: 18/755,304 Page 4 Art Unit: 3791 Application/Control Number: 18/755,304 Page 5 Art Unit: 3791 Application/Control Number: 18/755,304 Page 6 Art Unit: 3791 Application/Control Number: 18/755,304 Page 7 Art Unit: 3791 Application/Control Number: 18/755,304 Page 8 Art Unit: 3791 Application/Control Number: 18/755,304 Page 9 Art Unit: 3791 Application/Control Number: 18/755,304 Page 10 Art Unit: 3791 Application/Control Number: 18/755,304 Page 11 Art Unit: 3791 Application/Control Number: 18/755,304 Page 12 Art Unit: 3791 Application/Control Number: 18/755,304 Page 13 Art Unit: 3791 Application/Control Number: 18/755,304 Page 14 Art Unit: 3791 Application/Control Number: 18/755,304 Page 15 Art Unit: 3791 Application/Control Number: 18/755,304 Page 16 Art Unit: 3791 Application/Control Number: 18/755,304 Page 17 Art Unit: 3791 Application/Control Number: 18/755,304 Page 18 Art Unit: 3791 Application/Control Number: 18/755,304 Page 19 Art Unit: 3791 Application/Control Number: 18/755,304 Page 20 Art Unit: 3791 Application/Control Number: 18/755,304 Page 21 Art Unit: 3791 Application/Control Number: 18/755,304 Page 22 Art Unit: 3791 Application/Control Number: 18/755,304 Page 23 Art Unit: 3791 Application/Control Number: 18/755,304 Page 24 Art Unit: 3791
