DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 21st, 2026 has been entered.
The Examiner acknowledges the amendments to claims 1, 36, 38, 41, 50, and 52. Claims 1 and 35-53 remain pending.
Response to Arguments
Applicant's arguments, filed May 21st, 2026, with respect to the claim objections have been fully considered. The claim objections are withdrawn. However, additional objections are added.
Applicant's arguments, filed May 21st, 2026, with respect to the rejections under 35 U.S.C. 112(b) have been fully considered. The rejections under 35 U.S.C. 112(b) are withdrawn.
Applicant's arguments, filed May 21st, 2026, with respect to the rejections under 35 U.S.C. 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments, filed May 21st, 2026, with respect to the rejections under 35 U.S.C. 101 have been fully considered but are not persuasive.
At pages 12-14, Applicant argues that the claims are not properly characterized as being directed to mental processes because the claims are directed to a specific machine-implemented framework for using captured audio in the context of treatment monitoring and treatment adjustment and that the claims are integrated into a practical application because the claims affirmatively recite the use of the evaluation in an iterative digital-therapeutic monitoring loop. Examiner respectfully disagrees.
Under Step 2a, Prong One, the claims are analyzed to determine whether it is directed to any judicial exception. The steps of extracting, analyzing, and generating are directed to abstract ideas. “It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility.” MPEP 2106 II. In light of Applicant’s specification, the claim encompasses acoustic data with as few as one or two audio samples. See, for example, [0023-0024]. “The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea.” MPEP 2106.04(a)(2) III. The claimed steps can be performed using one or two audio samples in the human mind or by using a pen and paper.
With respect the limitations regarding the digital therapeutics, the modification of the treatment regimen of the one or more digital therapeutics based on the evaluations of pulmonary function is only provided when the evaluations do not indicate improvement in pulmonary function. Because the modifying of the treatment regimen only occurs upon the evaluations not indicating improvement, there is an instance where the evaluations could indicate an improvement and there would be no modification to the treatment regime of the one or more digital therapeutics based on the evaluations of pulmonary function. Furthermore, "The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s) must have more than a nominal or insignificant relationship to the exception(s)." See MPEP 2106.04(d)(2). The digital therapeutic could be any kind of digital therapeutic that the subject is undergoing, see para. [0073-0077] of the published instant application specification (US 20240049981 A1). It seems as if the treatment or prophylaxis limitation has a nominal or insignificant relationship to the exceptions as the claims do not explicitly recite what type of digital therapeutic/treatment regimen the subject is undergoing and further does not explicitly recite/specify the specific modifications to the treatment regimen/digital therapeutic for improving or treating pulmonary function. Furthermore, digital therapeutics were known to doctors, and was well-known, routinely, and conventionally used to treat pulmonary function – as evidenced by – North, M., Bourne, S., Green, B. et al. A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial. npj Digit. Med. 3, 145 (2020). https://doi.org/10.1038/s41746-020-00347-7. Examiner suggests that the claim recites modifying or providing a treatment regimen of the one or more digital therapeutics based on the evaluations of pulmonary function such that the modification to the treatment regimen is always performed.
Furthermore, when considered as a whole, the claims are not improving upon electronic devices, processors, and digital therapeutics used in the technical field of monitoring pulmonary function and do not provide an improvement to the technological field of monitoring pulmonary function. The electronic device, memory, processor, and digital therapeutics perform the same with or without the claimed abstract idea. Therefore, it is unclear how the abstract idea can improve the standard functions of the additional elements. Therefore, the step of “modifying a treatment regimen of the one or more digital therapeutics when the one or more subsequent evaluations do not indicate improvement in pulmonary function relative to the baseline evaluation” is merely an instruction to “apply” the digital therapeutics using well-understood, routine, or conventional techniques in the field.
At page 14, Applicant argues that the claims recite an ordered combination of features that provides a specific technological and therapeutic framework and do more than simply invoke a generic processor to apply an abstract idea because the claim uses a structured feedback loop that informs treatment modification. Examiner respectfully disagrees.
The claims do not apply the abstract idea to a particular machine. "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 computer for extracting, analyzing, and generating. The claims do not apply the information to a particular machine. Rather, the data is merely output in a post-solution step. Furthermore, the claims are analyzed to determine whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the improvement is in system functionality) or, instead, on a process that qualifies as an “abstract idea” for which computers are invoked merely as a tool. The claims are not directed to an improvement of computer functionality but rather are focused on a process of providing information regarding breathing patterns that qualifies as an “abstract idea” for which computers are invoked merely as a tool. The processor, sensors, and non-transitory processor-readable medium perform the same with or without the claimed abstract idea. Therefore, it is unclear how the abstract idea can improve the standard functions of the additional elements. The improvement cannot be found in the abstract idea itself. “[I]t is important to keep in mind that an improvement in the abstract idea itself ... is not an improvement in technology.” MPEP 2106.05(a) Il. The claims recite steps for an processing of data. The claims do not integrate the processing into a practical application. Rather, the alleged improvement lies solely within the processing steps performed by the processor. “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology." Id. The steps directed to modification of the treatment regimen of the one or more digital therapeutics based on the evaluations of pulmonary function is only provided when the evaluations do not indicate improvement in pulmonary function. Because the modifying of the treatment regimen only occurs upon the evaluations not indicating improvement, there is an instance where the evaluations could indicate an improvement and there would be no modification to the treatment regime of the one or more digital therapeutics based on the evaluations of pulmonary function and the system would merely generate a predicted forced vital capacity for a subject. Furthermore, additional elements are well known – as evidenced by – as evidenced by the non-patent literature of record (Nemati et al., Private Audio-Based Cough Sensing for In-Home Pulmonary Assessment Using Mobile Devices. 2020, In: Sugimoto, C., Farhadi, H., Hämäläinen, M. (eds) 13th EAI International Conference on Body Area Networks. pp 221-232, .1007/978-3-030-29897-5_18; Chung H, Jeong C, Luhach AK, Nam Y, Lee J. Remote Pulmonary Function Test Monitoring in Cloud Platform via Smartphone Built-in Microphone. Evolutionary Bioinformatics. 2019;15. bi:10.1177/1176934319888904; Larson et al., SpiroSmart: using a microphone to measure lung function on a mobile phone, 2012, ISBN: 9781450312240, Association for Computing Machinery, New York, NY, USA, doi:10.1145/2370216.2370261; North, M., Bourne, S., Green, B. et al. A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial. npj Digit. Med. 3, 145 (2020). https://doi.org/10.1038/s41746-020-00347-7; Nazir et al., 2020. Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '20). Association for Computing Machinery, New York, NY, USA, Article 10, 1-11).
Claim Objections
Claim 38 is objected to because of the following informalities:
Claim 38 line 7 “the training data” should recite “the training data set”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
Claims 1 and 35-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claims 1, 50, and 52 follows.
STEP 1
Regarding claims 1, 50, and 52, the claims recite a series of structural elements and a series of steps or acts, including an electronic device. Thus, the claims are directed to a machine and/or a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claims are then analyzed to determine whether it is directed to any judicial exception. The steps of:
(b) extracting one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data;
"(c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning regression model to generate an evaluation of pulmonary function for the subject including a predicted forced vital capacity (FVC),the predictive algorithm comprising a mixed-effects model trained using leave-one-participant-out cross-validation and configured to account for repeated measurements per participant by disaggregating the MPT feature into a participant-specific mean and an observation-specific deviation, the mixed-effects model using age and height with the MPT to generate the predicted FVC, wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value;
repeating the receiving, extracting, and analyzing operations at a plurality of time points to monitor pulmonary function of the subject over time;
generating a baseline evaluation and one or more subsequent evaluations of pulmonary function
set forth a judicial exception. These steps describe concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (the extracting step) and mathematical relationships (the analyzing and generating steps using the predictive algorithm is a mathematical relationship between maximum phonation time and forced vital capacity). Thus, the claims are drawn to Mental Processes and Mathematical Concepts, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claims 1, 50, and 52 recites receiving audio data of a subject through an electronic device comprising a microphone, prompting the subject to provide the audio data as input, and prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The generated evaluation of pulmonary function including a predicted forced vital capacity does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the generated evaluation of pulmonary function including a predicted forced vital capacity, nor does the method use a particular machine to perform the Abstract Idea.
Regarding the step of “wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject, and wherein the instructions, when executed by the processor, further cause: modifying a treatment regimen of the one or more digital therapeutics when the one or more subsequent evaluations do not indicate improvement in pulmonary function relative to the baseline evaluation”, the modification of the treatment regimen of the one or more digital therapeutics based on the evaluations of pulmonary function is only provided when the evaluations do not indicate improvement in pulmonary function. Because the modifying of the treatment regimen only occurs upon the evaluations not indicating improvement, there is an instance where the evaluations could indicate an improvement and there would be no modification to the treatment regime of the one or more digital therapeutics based on the evaluations of pulmonary function. Furthermore, "The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s) must have more than a nominal or insignificant relationship to the exception(s)." See MPEP 2106.04(d)(2). The digital therapeutic could be any kind of digital therapeutic that the subject is undergoing, see para. [0073-0077] of the published instant application specification (US 20240049981 A1). Furthermore, digital therapeutics were known to doctors, and was well-known, routinely, and conventionally used to treat pulmonary function – as evidenced by – North, M., Bourne, S., Green, B. et al. A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial. npj Digit. Med. 3, 145 (2020). https://doi.org/10.1038/s41746-020-00347-7.
Regarding claims 1, 50, and 52, the memory, processor, and electronic device comprising a microphone recited in the claims is a generic system/device (as evidenced by the non-patent literature of record) comprising generic components configured to perform the abstract idea. The recited electronic device and microphone are generic sensors configured to perform pre-solutional data gathering activity, and the processor/memory is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of:
A memory to store instructions;
A processor configured to execute the instructions …;
Receiving audio data of a subject through an electronic device comprising a microphone;
a predictive algorithm comprising a trained machine learning regression model … the predictive algorithm comprising a mixed-effects model trained using leave-one-participant-out cross-validation …, wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value;
wherein the subject is undergoing a clinical trial … participates in the clinical trial via prompting …;
wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject;
wherein the instructions, when executed by the processor, further cause: modifying a treatment regimen of the one or more digital therapeutics when the one or more subsequent evaluations do not indicate improvement in pulmonary function relative to the baseline evaluation.
The receiving and prompting steps are well-understood, routine and conventional activities for those in the field of medical diagnostics. Further, the receiving and prompting steps are each recited at a high level of generality such that it amounts to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the memory, processor, electronic device, and analyzing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
"The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s) must have more than a nominal or insignificant relationship to the exception(s)." See MPEP 2106.04(d)(2). The digital therapeutic could be any kind of digital therapeutic that the subject is undergoing, see para. [0073-0077] of the published instant application specification (US 20240049981 A1). Furthermore, digital therapeutics were known to doctors, and was well-known, routinely, and conventionally used to treat pulmonary function – as evidenced by – North, M., Bourne, S., Green, B. et al. A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial. npj Digit. Med. 3, 145 (2020). https://doi.org/10.1038/s41746-020-00347-7. Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claims 1, 50, and 52, the memory, processor, and electronic device comprising a microphone recited in the claims is a generic system/device (as evidenced by the non-patent literature of record – previously cited) comprising generic components configured to perform the abstract idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims. Claims 35-49, 51, and 53 are directed to more abstract ideas, which does not add anything significantly more. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 50-51 are rejected under 35 U.S.C. 103 as being unpatentable over Nazir (Nazir et al., 2020. Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones. In 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '20). Association for Computing Machinery, New York, NY, USA, Article 10, 1–11. https://doi.org/10.1145/3379503.3403543) in view of Miri (US 20210298711 A1 – previously cited), further in view of Gilifanov (RU2598051C2 English Translation), further in view of Stamatopoulos (US 20190088367 A1), and further in view of Au (US 20210219925 A1).
Regarding claim 50, Nazir discloses a computer-implemented method (fig. 4), wherein the method comprises: (a) receiving audio data of a subject (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”); (b) extracting one or more acoustic features from the audio data (fig. 4 & pages 6-7, 4.4 Lung Function Estimation, “extracted a set of 310 features for estimating lung function … speech features: shimmer and jitter to assess lung function”); (c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning regression model to generate an evaluation of pulmonary function for the subject (figs. 4 & 7 & pages 7-8, 4.4.3 Regression Model, “evaluated six regression models to estimate the FEV1/FVC ratio” & 5.3 Performance of Estimating Lung Function, “Multi-Layer Regression (MLR) model”), wherein the trained machine learning regression model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value (pages 4-5, 3.1 “collected the ground-truth lung function parameters, such as FEV1, FVC, and FEV1/FVC ratio” & ”4.2 System Overview, “train a regression model … lab study data … and in-clinic data” & Table 2), and wherein the subject is undergoing a clinical trial that comprises the evaluation for pulmonary function to assess status of a disease area associated with pulmonary function (Abstract, pages 4-5, 2.2 Mobile Audio Based Lung Assessment & 3.2 Study-II: Clinical Study); and an electronic device comprising a microphone (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”).
Nazir does not disclose the computer-implemented method performed by a system having at least a processor and a memory therein to execute instructions for evaluating pulmonary function and wherein the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone.
However, Miri directed to virtual lung function assessment and auscultation (VLFAA) discloses computer-implemented method performed by a system (“system”, para. [0032], fig. 1) having at least a processor (processor 1040, para. [0110]) and a memory (1050, para. [0110]) (sever 1025, para. [0110]) therein to execute instructions (“program instructions”, para. [0110]) for evaluating pulmonary function (“virtual lung function assessment test”; “analyze patients speech, breathing and lung sounds”, para. [0032-0033, 0110]) and wherein the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone (“coughing into a microphone 10 … mobile device 22 or 24 … requests … types of sound and record audio”; “virtual lung function tests … readily incorporated in … clinical trials”; “during each test, user is prompted”, para. [0032, 0102, 0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir such that the computer-implemented method performed by a system having at least a processor and a memory therein to execute instructions for evaluating pulmonary function and the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone, in view of the teachings of Miri, as this would aid in obtaining and analyzing audio recordings for virtual lung function assessment and auscultation (VLFAA).
Nazir, as modified by Miri hereinabove, does not disclose the extracted one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data, wherein the MPT feature is extracted from the sustained phonation portion fur use in generating the evaluation of pulmonary function.
However, Gilifanov directed to the field of diagnostics and determining changes in human voice function as a criterion for the effectiveness of treatment of diseases of the upper and lower respiratory tract in patients with chronic obstructive pulmonary disease (COPD) (page 1, 1st para. after Description), discloses extracting one or more acoustic features from the audio data including a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data (page 2, 7th-10th para., “maximum phonation time” & page 4, 4th para., “objective determination of changes in a person's voice function (voice) under pathological conditions such as COPD and laryngitis”), wherein the MPT feature is extracted from the sustained phonation portion fur use in generating the evaluation of pulmonary function (“reliable and fast objective detection the individual's vocal function variations accompanying COPD, the dynamic monitoring with determining the extent of the necessary diagnostic and rehabilitation measures”, Abstract & page 2, 9th para.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri hereinabove, such that the extracted one or more acoustic features from the audio data includes a maximum phonation time (MPT) feature from a sustained phonation portion of the audio data, wherein the MPT feature is extracted from the sustained phonation portion fur use in generating the evaluation of pulmonary function, in view of the teachings of Gilifanov, as this would aid in providing reliable and fast objective detection the individual's vocal function variations accompanying COPD and determining the effectiveness of treatment of diseases of the upper and lower respiratory tract in patients with chronic obstructive pulmonary disease (COPD) using changes in human voice function as a criterion.
Nazir, as modified by Miri and Gilifanov hereinabove, does not expressly disclose wherein the trained machine learning regression model is configured to analyze the MPT feature together with one or more demographic features to generate the evaluation of pulmonary function.
However, Stamatopoulos directed to computer-implemented method for determining lung pathology from an audio respiratory signal discloses a trained machine learning model (“training set ... artificial neural network (ANN)”, Abstract, para. [0451], figs. 34-35) is configured to analyze acoustic features (extracting descriptors at blocks 3515-3518, fig. 35) together with one or more demographic features to generate the evaluation of pulmonary function (“input data regarding the user ... gender, age, height, weight ... ventilatory thresholds”; “extracting descriptors at blocks 3515-3518 ... descriptors and all the metadata information from blocks 3511, 3512, 3513 and 3514 are fed into the ANN module 3570 ... determines the pathology, disease and severity”, Abstract, para. [0246, 0253, 0444-0451], figs. 35 & 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri and Gilifanov hereinabove, such that the trained machine learning regression model is configured to analyze the MPT feature together with one or more demographic features to generate the evaluation of pulmonary function, in view of the teachings of Stamatopoulos, as this would aid in determining a variety of metrics including ventilatory thresholds and determining the lung pathology, disease and severity.
Nazir, as modified by Miri, Gilifanov, and Stamatopoulos hereinabove, does not disclose wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject, and wherein the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics, repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics, and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time.
However, Au directed to an apparatus and method for acquiring sound and motion data to detect physiological events discloses wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject (“dynamic feedback for physical therapy and pulmonary rehabilitation”; “physical therapy ... software platform ... user interface that provides real-time feedback and instructions on prescribed rehab activities based on sensor data”; “improving symptoms”, para. [0063, 0125-0126, 0166), and wherein the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics (“during the exercise ... a user interface that provides real-time feedback and instructions on prescribed rehab activities based on sensor data”, para. [0063, 0125-0126, 0166]), repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics (“real-time feedback and decision support is provided ... data gathered by the wearable device 100 is used to provide information regarding the patient during physical therapy”, para. [0063, 0125-0126, 0166]), and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time (“real-time feedback and instructions on prescribed rehab activities based on sensor data”; “identifying a risk level associated with a cough ... number of coughs identified in the previous 24 hours may be compared with those received in the prior 72 hours ... indication of improving symptoms”, para. [0063, 0125-0126, 0166]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri, Gilifanov, and Stamatopoulos hereinabove, such that the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject, and the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics, repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics, and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time, in view of the teachings of Au, as this would aid in providing dynamic/real-time feedback and decision support for physical therapy and pulmonary rehabilitation (para. [0063, 0125-0126]).
Regarding claim 51, Nazir, as modified by Miri, Gilifanov, Stamatopoulos, and Au hereinabove, discloses the computer-implemented method of claim 50: wherein the disease area is a respiratory condition or disease affecting bloody oxygenation or homeostasis or a neurodegenerative disease affecting pulmonary function (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); wherein the disease area comprises one of: ALS, COPD, asthma, cystic fibrosis,COVID-19 (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); and wherein the evaluation of pulmonary function comprises one or more of: a predicted force vital capacity, forced expiratory volume, peak expiratory flow, mid- expiratory flow rate, forced inspiratory vital capacity, forced expiratory time, respiration rate, respiration rhythm, respiration quality, pause rate, cough events, maximum phonation time, vocal quality, hypernasality, or any combination thereof (page 4, 2.2 Mobile Audio Based Lung Assessment, “estimate the FEV1/FVC ratio” & fig. 7 & Gilifanov page 2, 7th-10th para., “maximum phonation time”).
Claims 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over Nazir in view of Miri, further in view of Stamatopoulos (US 20190088367 A1), and further in view of Au (US 20210219925 A1).
Regarding claim 52, Nazir discloses a method for evaluating pulmonary function (fig. 4), by performing the following operations: (a) receiving audio data of a subject (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”); (b) extracting one or more acoustic features from the audio data generated in response to one or more elicitation tasks prompted to the subject (page 3, 3 Data Collection Studies, “asked our participants to perform various tasks” & fig. 4 & pages 6-7, 4.4 Lung Function Estimation, “extracted a set of 310 features for estimating lung function … speech features: shimmer and jitter to assess lung function”); (c) analyzing the one or more acoustic features using a predictive algorithm comprising a trained machine learning model to generate an evaluation of pulmonary function for the subject (figs. 4 & 7 & pages 7-8, 4.4.3 Regression Model, “evaluated six regression models to estimate the FEV1/FVC ratio” & 5.3 Performance of Estimating Lung Function, “Multi-Layer Regression (MLR) model”), wherein the trained machine learning model is trained using a training data set comprising acoustic features extracted from audio samples and corresponding ground-truth spirometry measurements of a pulmonary function value (pages 4-5, 3.1 “collected the ground-truth lung function parameters, such as FEV1, FVC, and FEV1/FVC ratio” & ”4.2 System Overview, “train a regression model … lab study data … and in-clinic data” & Table 2), and wherein the subject is undergoing a clinical trial that comprises the evaluation for pulmonary function to assess status of a disease area associated with pulmonary function (Abstract, pages 4-5, 2.2 Mobile Audio Based Lung Assessment & 3.2 Study-II: Clinical Study); and an electronic device comprising a microphone (fig. 1 & page 4, 2.2 Mobile Audio Based Lung Assessment, “blowing exhalation sound captured in a smartphone microphone”).
Nazir does not disclose a non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, the instructions cause the processor to execute instructions for evaluating pulmonary function and wherein the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone.
However, Miri discloses a non-transitory computer readable storage media having instructions (“program instructions”, para. [0110]) stored thereupon that, when executed by a system (fig. 1, “system”, para. [0032]) having at least a processor (processor 1040, para. [0110]) and a memory therein (1050, para. [0110]) (sever 1025, para. [0110]), the instructions cause the processor to execute instructions for evaluating pulmonary function (“virtual lung function assessment test”; “analyze patients speech, breathing and lung sounds”, para. [0032-0033, 0110]) and wherein the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone (“coughing into a microphone 10 … mobile device 22 or 24 … requests … types of sound and record audio”; “virtual lung function tests … readily incorporated in … clinical trials”; “during each test, user is prompted”, para. [0032, 0102, 0106]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir to comprise a non-transitory computer readable storage media having instructions stored thereupon that, when executed by a system having at least a processor and a memory therein, the instructions cause the processor to execute instructions for evaluating pulmonary function and the subject participates in the clinical trial at via prompting to request the subject to provide the audio data as input remotely through an electronic device comprising a microphone, in view of the teachings of Miri, as this would aid in obtaining and analyzing audio recordings for virtual lung function assessment and auscultation (VLFAA).
Nazir, as modified by Miri hereinabove, does not expressly disclose wherein the trained machine learning model is configured to generate the evaluation of pulmonary function using the one or more acoustic features and one or more demographic features.
However, Stamatopoulos directed to computer-implemented method for determining lung pathology from an audio respiratory signal discloses a trained machine learning model (“training set ... artificial neural network (ANN)”, Abstract, para. [0451], figs. 34-35) is configured to generate the evaluation of pulmonary function using the one or more acoustic features (extracting descriptors at blocks 3515-3518, fig. 35) and one or more demographic features (“input data regarding the user ... gender, age, height, weight ... ventilatory thresholds”; “extracting descriptors at blocks 3515-3518 ... descriptors and all the metadata information from blocks 3511, 3512, 3513 and 3514 are fed into the ANN module 3570 ... determines the pathology, disease and severity”, Abstract, para. [0246, 0253, 0444-0451], figs. 35 & 38).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri hereinabove, such that the trained machine learning model is configured to generate the evaluation of pulmonary function using the one or more acoustic features and one or more demographic features, in view of the teachings of Stamatopoulos, as this would aid in determining a variety of metrics including ventilatory thresholds and determining the lung pathology, disease and severity.
Nazir, as modified by Miri and Stamatopoulos hereinabove, does not disclose wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject, and wherein the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics, repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics, and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time.
However, Au directed to an apparatus and method for acquiring sound and motion data to detect physiological events discloses wherein the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject (“dynamic feedback for physical therapy and pulmonary rehabilitation”; “physical therapy ... software platform ... user interface that provides real-time feedback and instructions on prescribed rehab activities based on sensor data”; “improving symptoms”, para. [0063, 0125-0126, 0166), and wherein the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics (“during the exercise ... a user interface that provides real-time feedback and instructions on prescribed rehab activities based on sensor data”, para. [0063, 0125-0126, 0166]), repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics (“real-time feedback and decision support is provided ... data gathered by the wearable device 100 is used to provide information regarding the patient during physical therapy”, para. [0063, 0125-0126, 0166]), and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time (“real-time feedback and instructions on prescribed rehab activities based on sensor data”; “identifying a risk level associated with a cough ... number of coughs identified in the previous 24 hours may be compared with those received in the prior 72 hours ... indication of improving symptoms”, para. [0063, 0125-0126, 0166]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nazir, as modified by Miri and Stamatopoulos hereinabove, such that the subject is undergoing one or more digital therapeutics for improving or treating pulmonary function or mitigating or slowing progression of decline of pulmonary function of the subject, and the instructions, when executed by the processor, further cause: prompting one or more elicitation tasks to the subject during administration of the one or more digital therapeutics, repeating the receiving, extracting, and analyzing operations one or more times over a period of time while the subject undergoes the one or more digital therapeutics, and generating a plurality of evaluations of pulmonary function for monitoring pulmonary function of the subject over time, in view of the teachings of Au, as this would aid in providing dynamic/real-time feedback and decision support for physical therapy and pulmonary rehabilitation (para. [0063, 0125-0126]).
Regarding claim 53, Nazir, as modified by Miri, Stamatopoulos, and Au hereinabove, discloses the non-transitory computer readable storage media of claim 52: wherein the disease area is a respiratory condition or disease affecting bloody oxygenation or homeostasis or a neurodegenerative disease affecting pulmonary function (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); wherein the disease area comprises one of: ALS, COPD, asthma, cystic fibrosis,COVID-19 (page 8, 5.3 Performance of Estimating Lung Function, “asthma COPD”); and wherein the evaluation of pulmonary function comprises one or more of: a predicted forced vital capacity, forced expiratory volume, peak expiratory flow, mid- expiratory flow rate, forced inspiratory vital capacity, forced expiratory time, respiration rate, respiration rhythm, respiration quality, pause rate, cough events, maximum phonation time, vocal quality, hypernasality, or any combination thereof (page 4, 2.2 Mobile Audio Based Lung Assessment, “estimate the FEV1/FVC ratio” & fig. 7).
Conclusion
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/A.E.H./Examiner, Art Unit 3791
/AURELIE H TU/Primary Examiner, Art Unit 3791
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