DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
This Office Action is responsive to the amendment filed 08/19/2026 (“Amendment”). Claims 17, 22, 24-32, and 35-37 are currently under consideration. The Office acknowledges the amendments to claims 17, 24, 28, 29, 32, and 37, as well as the cancellation of claims 18-21, 23, 33, and 34.
The objection(s) to the drawings, specification, and/or claims, the interpretation(s) under 35 USC 112(f), and/or the rejection(s) under 35 USC 101 and/or 35 USC 112 not reproduced below has/have been withdrawn in view of the corresponding amendments.
Information Disclosure Statement
Applicant is reminded of the continuing obligation under 37 CFR 1.56, to timely apprise the Office of any information which is material to patentability of the claims under consideration in this application.
Claim Objections
Claims 28 and 37 are objected to because of the following informalities: the recitations of “a support vector machine and logistic regression model” should instead read --a support vector machine and a logistic regression model--. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 32 and 37 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 32 and 37, there is no support for the alert being any of a visual alert and/or an audible alert.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17, 22, 24-32, and 35-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 17, 22, 24-32, and 35-37 are directed to a “method,” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 17 and 37 recite (“set forth” or “describe”) the abstract idea of a mathematical concept, substantially as follows:
extracting, from the first sound data, a first frequency, a first time-frequency feature, and a first Mel-frequency cepstral coefficient representing breathing patterns; performing feature selection to identify select ones of the first extracted features; classifying the first sound data using a classifier trained on the select features to distinguish between different respiratory conditions, including COVID-19 and hypercapnia; using the first sound data to detect each inhalation of breath made by the user as the user reads aloud the passage of text; measuring a first plurality of intervals between the detected inhalations while the user reads the passage of text; determining a first average of the first plurality of intervals; at a second time, extracting, from the second sound data, a second frequency, a second time-frequency feature, and a second Mel-frequency cepstral coefficient representing breathing patterns; performing feature selection to identify select ones of the second extracted features; classifying the second sound data using a classifier trained on the select features to distinguish between different respiratory conditions, including COVID-19 and hypercapnia; using the second sound data to detect each inhalation of breath made by the user as the user reads aloud the same passage of text; measuring a second plurality of intervals between the detected inhalations; determining a second average of the second plurality of intervals; and, determining that the second average is less than the first average.
With respect to claim 37, the abstract idea further comprises wherein: the first time is when the user is in a known, healthy state, detecting each inhalation comprises identifying a transient acoustic signature corresponding to a rapid inspiratory airflow, the first average and the second average correspond to respiration rate during speech, the Mel-frequency cepstral coefficients comprise a plurality of coefficients representing perceptual frequency bands of human hearing, the feature selection comprises dimensionality reduction using principal component analysis, the extracting further comprises extracting at least one acoustic, prosodic, or durational speech feature, the first average corresponds to a baseline respiratory metric for the user, the baseline respiratory metric is established while the user is in a known healthy state.
These steps also involve the mathematical concepts of feature extraction, selection, classification, analysis of signal morphology, averaging, comparison, principal component analysis, etc. These steps correspond to “[w]ords used in a claim operating on data to solve a problem [that] can serve the same purpose as a formula.” See MPEP 2106.04(a)(2)(I).
Prong Two: Claims 17 and 37 do not include additional elements that integrate the mathematical concepts into a practical application. Therefore, the claims are “directed to” the mathematical concepts. The additional elements merely:
recite the words “apply it” (or an equivalent) with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. a software application via a processor and memory of a smartphone, classification via a support vector machine or logistic regression model as claimed), and
add insignificant extra-solution activity (the pre-solution activity of: receiving and storing data, displaying text according to a reading rate, further details of the passage of text (claim 37), training, using generic data-gathering components (a microphone, such as a smartphone microphone); the post-solution activity of: issuing an alert, and further details of the alert (claim 37), using generic data-outputting components (e.g. a display), etc.).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea, while generically implementing it on a computer/processor. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The issued alert need not be seen, heard, or acted on. No improvement to the technology is evident, and nothing is done with the determined current state. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application. Also, it should be noted that in these method claims, the alert is not actually limiting because the condition “upon determining that the second average is less than the first average and the classification of the second sound data indicates one of the different respiratory conditions” need not actually occur.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 17 and 37 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Dependent Claims
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
further describe the abstract idea ((as indicated with respect to claim 37 above), determining a duration (claim 24), etc.), and
further describe the extra-solution activity (or the structure used for such activity) (e.g. as indicated with respect to claim 37 above), etc.).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way (as above, the alert is not limiting, and if it were, it need not be seen, heard, or acted on). They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves another technology or technical field, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 17, 22, 24-32, and 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over various teachings of US Patent Application Publication 2021/0045656 (“Rahman”) in view of US Patent Application Publication 2012/0033948 (“Rodriguez”), US Patent Application Publication 2020/0337594 (“Reddy”), and US Patent Application Publication 2020/0094007 (“Koizumi”).
Regarding claim 17, Rahman teaches [a] method for monitoring human respiratory performance of a user, comprising: providing a smartphone having a processor, a memory associated with the processor, a microphone configured to feed sound data to the processor, and a display (¶ 0026, a smartphone having a microphone, also inherently having a processor, memory, and display (and see Fig. 5 and ¶¶s 0072 and 0073)); providing a software application stored within the memory and configured to run on the processor (¶ 0026, necessary to analyze the sound; although there is no explicit teaching that the smartphone is what analyses the sound data, ¶ 0072 describes a computer system as performing the steps of the method via software running on the computer system, and ¶ 0073 describes a mobile telephone as able to be the computer system. Thus, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use software on the smartphone that obtains the sound data for processing the data, since it is a known computer system (Fig. 5 and ¶ 0073), and for the purpose of easy use via a stand-alone device); at a first time, while the user is in a known, healthy state (¶ 0047 describes comparing a current value/condition to a user’s baseline condition, which suggests the establishment of a healthy baseline. ¶ 0049 describes comparing to previous periods to determine a trend of e.g. deterioration, which also suggests that the previous period is a healthy state. ¶¶s 0062 and 0068 describe use of a “healthy” classification. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to establish and use a known, healthy baseline, for the purpose of being able to catch deterioration and other abnormal conditions more quickly and accurately (¶¶s 0047, 0049)), the software application displaying a passage of text to the user on the display (¶ 0052 and Fig. 3, step 304, selecting an assessment task such as reading; and ¶ 0053 and Fig. 3, step 306, requesting the user to provide data for the task), … ; the software application receiving a first sound data from the microphone as the user reads aloud the passage of text (¶ 0053 and Fig. 3, step 308, receiving the user data); the software application recording the first sound data (¶ 0076, storing one or more results, or storing in general to enable subsequent processing as contemplated by Fig. 4); the software application extracting, from the first sound data, a first frequency, a first time-frequency feature, and a first Mel-frequency cepstral coefficient representing breathing patterns (¶¶s 0030, 0034-0037, 0041, etc., acoustic features including cough frequency, pause frequency, jitter, shimmer, spectrogram (i.e., time-frequency) features, and mel-frequency cepstral coefficients); the software application performing feature selection to identify select ones of the first extracted features (Fig. 4, step 408, ¶ 0067, selecting the top features); the software application classifying the first sound data using a classifier trained on the select features to distinguish between different respiratory conditions (Fig. 4, step 410, ¶¶s 0033 and 0068, claim 13, etc., distinguishing between severities of pulmonary obstruction), …; the software application using the first sound data to detect each inhalation of breath made by the user as the user reads aloud the passage of text (¶¶s 0031, 0034, 0035, etc. describe monitoring inhalations/pause time, etc.); the software application measuring a first plurality of intervals between the detected inhalations while the user reads the passage of text (respiration rate (¶¶s 0033 and 0034) being measured during reading (Fig. 3 and ¶¶s 0052 and 0053), based on inhalations because they are part of the sound data (¶¶s 0054, 0055, etc. - also see ¶ 0035, describing inhalations as affecting breathing rate because they affect pause time). Other measures such as inhale-exhale ratio, inhalation sound pattern, breathing pattern, etc. (¶ 0034) are also based on these intervals); the software application determining a first average of the first plurality of intervals and storing the first average in the memory (¶ 0035, pause time and frequency may be the average for a set of segments. Storing is necessary for measuring a change (¶¶s 0030, 0033, 0044, 0045, etc.)); at a second time, the software application repeating displaying the same passage of text … (¶ 0049, monitoring over time to detect e.g. deterioration over the course of a week or month – also see ¶¶s 0046, 0047, 0052, etc., reading a passage of text and repeating the exercise at a later time to monitor changes, comparing with respect to a baseline, etc. And, using the same passage would have been obvious for the purpose of being able to make an accurate comparison), the software application receiving a second sound data from the microphone as the user reads aloud the same passage of text (as above, repeating the process to assess the condition over time); the software application receiving a second sound data from the microphone as the user reads aloud the passage of text (as above, repeating the process to assess the condition over time); the software application recording the second sound data (as above, repeating the process to assess the condition over time); the software application extracting, from the second sound data, a second frequency, a second time-frequency feature, and a second Mel-frequency cepstral coefficient representing breathing patterns (as above, repeating the process to assess the condition over time); the software application performing feature selection to identify select ones of the second extracted features (as above, repeating the process to assess the condition over time); the software application classifying the second sound data using the classifier trained on the select features to distinguish between different respiratory conditions … (as above, repeating the process to assess the condition over time); the software application using the second sound data to detect each inhalation of breath made by the user as the user reads aloud the same passage of text (as above, repeating the process to assess the condition over time); the software application measuring a second plurality of intervals between the detected inhalations (as above, repeating the process to assess the condition over time); the software application determining a second average of the second plurality of intervals and storing the second average in the memory (as above, repeating the process to assess the condition over time); and the software application, upon determining that the second average is less than the first average (¶ 0049, detecting deterioration over time, such as the difficulty in breathing described in ¶ 0035, which may be based on an increased respiration rate due to reduced intervals between inhalations (increased pause frequency)) and the classification of the second sound data indicates one of the different respiratory conditions (Fig. 4, step 410, ¶¶s 0033 and 0068, claim 13, etc., distinguishing between severities of pulmonary obstruction), issuing an alert (¶ 0071, issuing an alert based on the detection of e.g. lung deterioration).
Rahman does not appear to explicitly teach the passage being scrolled on the display in order to regulate a reading rate at which the user reads the passage of text aloud, and then repeating the process of displaying the passage of text by scrolling at the second time.
Rodriguez teaches scrolling text from bottom to top or top to bottom (¶ 0182) at a controlled and pre-defined reading rate (¶¶s 0010, 0205, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to present the passage of Rahman via scrolling at a defined rate at both times, to make the assessment task of Rahman be based on a user-defined reading rate, as in Rodriguez, as the simple substitution of one known text presentation method for another, with predictable results (controlling the reading rate), and for the purpose of controlling the speech/reading rate (Rodriguez: ¶¶s 0010, 0205, etc.).
Rahman-Rodriguez does not appear to explicitly teach distinguishing between different respiratory conditions including COVID-19 and hypercapnia.
Reddy teaches classifying COVID-19 based on respiratory samples (¶¶s 0077, 0123, 0201, 0211, etc.).
Koizumi teaches classifying hypercapnic patients based on respiration rate (Fig. 1, ¶¶s 0007, 0025, 0030-0032).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the classifier of the combination to also classify/distinguish between COVID-19 and hypercapnia, for the purpose of classifying more conditions related to respiration (Reddy: Abstract, ¶¶s 0077, 0123, 0201, 0211, etc.; Koizumi: Fig. 1, ¶¶s 0007, 0025, 0030-0032). Further, use of the classifier of the combination to classify COVID-19 and hypercapnia would simply have been the application of a known technique to improve the device in a predictable way, since COVID-19 was a disease of particular interest for study, and its relation to respiratory distress was known (Applicant’s specification at ¶¶s 0005 and 0006).
Regarding claim 22, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein detecting each inhalation comprises identifying a transient acoustic signature corresponding to a rapid inspiratory airflow (Rahman: ¶ 0035, sharp inhalation).
Regarding claim 24, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches determining at least one of inhalation duration or exhalation duration (Rahman: ¶ 0034, inhalation-to-exhalation ratio is based on inhalation and exhalation durations).
Regarding claim 25, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the Mel-frequency cepstral coefficients comprise a plurality of coefficients representing perceptual frequency bands of human hearing (Rahman: ¶¶s 0030 and 0058, the Mel scale generally).
Regarding claim 26, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the feature selection comprises dimensionality reduction using principal component analysis (Rahman: ¶ 0058, Fig. 4, step 408).
Regarding claim 27, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the extracting further comprises extracting at least one acoustic, prosodic, or durational speech feature (Rahman: ¶¶s 0027, 0031, 0034, 0036, etc.).
Regarding claim 28, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the classifier comprises at least one of a support vector machine and logistic regression model (Rahman: Fig. 4, step 410, ¶ 0068).
Regarding claim 29, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the classifier is trained using labeled speech and breathing samples obtained from healthy users and users exhibiting one of COVID-19, hypercapnia, or hypoxia (Rahman: ¶¶s 0062, 0068, etc., trained on healthy subjects and pulmonary patients, SVM, etc. – also Reddy and Koizumi, describing COVID-19 and hypercapnia).
Regarding claims 30 and 31, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the first average corresponds to a baseline respiratory metric for the user, wherein the baseline respiratory metric is established while the user is in a known healthy state (Rahman: ¶¶s 0046 and 0047 describe using models to assess a pulmonary condition, the models being based on a user’s baseline condition - also see ¶¶s 0062 and 0068, describing use of a “healthy” classification. It would have been obvious to establish a healthy/baseline respiration state, e.g. based on the first average (¶ 0035, pause time and frequency may be the average for a set of segments), for the purpose of facilitating such classification, as well as for better tracking a deterioration trend (Rahman: ¶¶s 0017, 0049, 0050, etc.).
Regarding claim 32, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the alert comprises at least one of a visual alert or an audible alert (Rahman: ¶ 0071).
Regarding claim 35, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the microphone is an onboard smartphone microphone (Rahman: ¶ 0026, a smartphone having a microphone).
Regarding claim 36, Rahman-Rodriguez-Reddy-Koizumi teaches all the features with respect to claim 17, as outlined above. Rahman-Rodriguez-Reddy-Koizumi further teaches wherein the method is performed without external respiratory sensors (Rahman: ¶¶s 0023, 0026, 0058, etc., a microphone for sound data).
Regarding claim 37, Rahman teaches [a] method for monitoring human respiratory performance of a user, comprising: providing a smartphone having a processor, a memory associated with the processor, a microphone configured to feed sound data to the processor, and a display (¶ 0026, a smartphone having a microphone, also inherently having a processor, memory, and display (and see Fig. 5 and ¶¶s 0072 and 0073)); providing a software application stored within the memory and configured to run on the processor (¶ 0026, necessary to analyze the sound; although there is no explicit teaching that the smartphone is what analyses the sound data, ¶ 0072 describes a computer system as performing the steps of the method via software running on the computer system, and ¶ 0073 describes a mobile telephone as able to be the computer system. Thus, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use software on the smartphone that obtains the sound data for processing the data, since it is a known computer system (Fig. 5 and ¶ 0073), and for the purpose of easy use via a stand-alone device); at a first time, the software application displaying a passage of text to the user on the display (¶ 0052 and Fig. 3, step 304, selecting an assessment task such as reading; and ¶ 0053 and Fig. 3, step 306, requesting the user to provide data for the task), … ; the software application receiving a first sound data from the microphone as the user reads aloud the passage of text (¶ 0053 and Fig. 3, step 308, receiving the user data); the software application recording the first sound data (¶ 0076, storing one or more results, or storing in general to enable subsequent processing as contemplated by Fig. 4); the software application extracting, from the first sound data, a first frequency, a first time-frequency feature, and a first Mel-frequency cepstral coefficient representing breathing patterns (¶¶s 0030, 0034-0037, 0041, etc., acoustic features including cough frequency, pause frequency, jitter, shimmer, spectrogram (i.e., time-frequency) features, and mel-frequency cepstral coefficients); the software application performing feature selection to identify select ones of the first extracted features (Fig. 4, step 408, ¶ 0067, selecting the top features); the software application classifying the first sound data using a classifier trained on the select features to distinguish between different respiratory conditions (Fig. 4, step 410, ¶¶s 0033 and 0068, claim 13, etc., distinguishing between severities of pulmonary obstruction), …; the software application using the first sound data to detect each inhalation of breath made by the user as the user reads aloud the passage of text (¶¶s 0031, 0034, 0035, etc. describe monitoring inhalations/pause time, etc.); the software application measuring a first plurality of intervals between the detected inhalations while the user reads the passage of text (respiration rate (¶¶s 0033 and 0034) being measured during reading (Fig. 3 and ¶¶s 0052 and 0053), based on inhalations because they are part of the sound data (¶¶s 0054, 0055, etc. - also see ¶ 0035, describing inhalations as affecting breathing rate because they affect pause time). Other measures such as inhale-exhale ratio, inhalation sound pattern, breathing pattern, etc. (¶ 0034) are also based on these intervals); the software application determining a first average of the first plurality of intervals and storing the first average in the memory (¶ 0035, pause time and frequency may be the average for a set of segments. Storing is necessary for measuring a change (¶¶s 0030, 0033, 0044, 0045, etc.)); at a second time, the software application repeating displaying the passage of the text … (¶ 0049, monitoring over time to detect e.g. deterioration over the course of a week or month); the software application receiving a second sound data from the microphone as the user reads aloud the passage of text (as above, repeating the process to assess the condition over time); the software application receiving a second sound data from the microphone as the user reads aloud the passage of text (as above, repeating the process to assess the condition over time); the software application recording the second sound data (as above, repeating the process to assess the condition over time); the software application extracting, from the second sound data, a second frequency, a second time-frequency feature, and a second Mel-frequency cepstral coefficient representing breathing patterns (as above, repeating the process to assess the condition over time); the software application performing feature selection to identify select ones of the second extracted features (as above, repeating the process to assess the condition over time); the software application classifying the second sound data using a classifier trained on the select features to distinguish between different respiratory conditions … (as above, repeating the process to assess the condition over time); the software application using the second sound data to detect each inhalation of breath made by the user as the user reads aloud the passage of text (as above, repeating the process to assess the condition over time); the software application measuring a second plurality of intervals between the detected inhalations (as above, repeating the process to assess the condition over time); the software application determining a second average of the second plurality of intervals and storing the second average in the memory (as above, repeating the process to assess the condition over time); and the software application, upon determining that the second average is less than the first average (¶ 0049, detecting deterioration over time, such as the difficulty in breathing described in ¶ 0035, which may be based on an increased respiration rate due to reduced intervals between inhalations (increased pause frequency)) and the classification of the second sound data indicates one of the different respiratory conditions (Fig. 4, step 410, ¶¶s 0033 and 0068, claim 13, etc., distinguishing between severities of pulmonary obstruction), issuing an alert (¶ 0071, issuing an alert based on the detection of e.g. lung deterioration), wherein: the first time is when the user is in a known, healthy state (¶¶s 0046 and 0047 describe using models to assess a pulmonary condition, the models being based on a user’s baseline condition - also see ¶¶s 0062 and 0068, describing use of a “healthy” classification. It would have been obvious to establish a healthy/baseline respiration state, for the purpose of facilitating such classification, as well as for better tracking a deterioration trend (Rahman: ¶¶s 0017, 0049, 0050, etc.)), …, the passage of text is identical at the first time and the second time (¶¶s 0049 and 0052, reading a passage of text and repeating the exercise at a later time to monitor changes – also see ¶¶s 0046, 0047, etc., comparison with respect to a baseline. Using the same passage would have been obvious for the purpose of being able to make an accurate comparison), detecting each inhalation comprises identifying a transient acoustic signature corresponding to a rapid inspiratory airflow (¶ 0035, sharp inhalation), the Mel-frequency cepstral coefficients comprise a plurality of coefficients representing perceptual frequency bands of human hearing (Rahman: ¶¶s 0030 and 0058, the Mel scale generally), the feature selection comprises dimensionality reduction using principal component analysis (¶ 0058, Fig. 4, step 408), the extracting further comprises extracting at least one acoustic, prosodic, or durational speech feature (¶¶s 0027, 0031, 0034, 0036, etc.), the classifier comprises at least one of a support vector machine and logistic regression model (Fig. 4, step 410, ¶ 0068), the classifier is trained using labeled speech and breathing samples obtained from healthy users and users exhibiting [respiratory impairment] (¶¶s 0062, 0068, etc., trained on healthy subjects and pulmonary patients, SVM, etc.), the first average corresponds to a baseline respiratory metric for the user, the baseline respiratory metric is established while the user is in a known healthy state (¶¶s 0046 and 0047 describe using models to assess a pulmonary condition, the models being based on a user’s baseline condition - also see ¶¶s 0062 and 0068, describing use of a “healthy” classification. It would have been obvious to establish a healthy/baseline respiration state, e.g. based on the first average (¶ 0035, pause time and frequency may be the average for a set of segments), for the purpose of facilitating such classification, as well as for better tracking a deterioration trend (¶¶s 0017, 0049, 0050, etc.), the alert comprises at least one of a visual alert and an audible alert (¶ 0071), the alert indicates shortness of breath (¶¶s 0049, 0058, 0061, 0071, etc., lung deterioration based on e.g. FEV1%, which is indicative of respiratory effort or shortness of breath), the microphone is an onboard smartphone microphone (Rahman: ¶ 0026, a smartphone having a microphone, and the method is performed without external respiratory sensors (Rahman: ¶¶s 0023, 0026, 0058, etc., a microphone for sound data).
Rahman does not appear to explicitly teach the passage being scrolled on the display in order to regulate a reading rate at which the user reads the passage of text aloud, and then repeating the process of displaying the passage of text by scrolling at the second time. Rahman does not appear to explicitly teach wherein the passage of text is scrolled at a rate selected to regulate the reading rate at which the user reads the passage of text aloud.
Rodriguez teaches scrolling text from bottom to top or top to bottom (¶ 0182) at a controlled and pre-defined reading rate, including one that matches a speaker’s natural speech rate (¶¶s 0010, 0046, 0205, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to present the passage of Rahman via scrolling at a defined rate at both times, to make the assessment task of Rahman be based on a user-defined reading rate, as in Rodriguez, as the simple substitution of one known text presentation method for another, with predictable results (controlling the reading rate), and for the purpose of controlling the speech/reading rate (Rodriguez: ¶¶s 0010, 0205, etc.).
Rahman-Rodriguez does not appear to explicitly teach distinguishing between different respiratory conditions including COVID-19 and hypercapnia, or the classifier trained on users exhibiting one of COVID-19, hypercapnia, or hypoxia.
Reddy teaches classifying COVID-19 based on respiratory samples (¶¶s 0077, 0123, 0201, 0211, etc.).
Koizumi teaches classifying hypercapnic patients based on respiration rate (Fig. 1, ¶¶s 0007, 0025, 0030-0032).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the classifier of the combination to also train/classify/distinguish between COVID-19 and hypercapnia, for the purpose of classifying more conditions related to respiration (Reddy: Abstract, ¶¶s 0077, 0123, 0201, 0211, etc.; Koizumi: Fig. 1, ¶¶s 0007, 0025, 0030-0032). Further, use of the classifier of the combination to classify COVID-19 and hypercapnia would simply have been the application of a known technique to improve the device in a predictable way, since COVID-19 was a disease of particular interest for study, and its relation to respiratory distress was known (Applicant’s specification at ¶¶s 0005 and 0006).
Response to Arguments
Applicant’s amendments and arguments filed 08/19/2026 have been fully considered. In response to the arguments and amendments regarding the rejections under 35 USC 101, they are not persuasive. In addition to the arguments already made of record, the Office notes that (1) Applicant has still not pointed to where in the specification the alleged improvement is discussed, and (2) at least because the condition “upon determining…” need not actually occur, the claimed classification of different respiratory conditions is not tied in any way to the comparing of average intervals to issue an alert. I.e., the alert continues to be based on features previously discussed, and not any “select” extracted features. Thus, it is unclear what the similarity with CardioNet is.
In response to the amendments and arguments regarding the rejections under 35 USC 103, they are not persuasive. Applicant has not addressed any of the Office’s particular citations, e.g. ¶ 0049 of Rahman, which describes monitoring over time to detect e.g. deterioration over the course of a week or month, ¶ 0035 which describes pause time and frequency as the average for a set of segments, discussions of inhalation sound patterns, respiration rate (¶¶s 0033 and 0034) being measured during reading (Fig. 3 and ¶¶s 0052 and 0053), based on inhalations because they are part of the sound data (¶¶s 0054, 0055, etc. - also see ¶ 0035, describing inhalations as affecting breathing rate because they affect pause time), etc. Applicant’s arguments about the “same passage” limitations ignore Rahman’s desire to track trends/changes over time in the user’s condition, including with respect to a baseline state. It should be noted that Rahman is useful for what it suggests, not simply for what it explicitly teaches.
Regarding the other art, Applicant has alleged that the rejection fails to identify an apparent reason for combination, but has not addressed the Office’s cited motivations for combination. Therefore, all claims remain rejected in light of the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791
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