DETAILED ACTION
Applicant’s arguments, filed on 10/14/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 10/14/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-20 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claims 1, 3, 7-8, 12-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stamatopoulos (US 20190192047) in further view of O’Keeffe (WO 2021188786), Stamatopoulos ‘367 (US 20190088367), and Oster (AU 2004285457).
Regarding independent claim 1, Stamatopoulos teaches a computer-implemented method of determining lung pathology severity from a subject under test ([0017]: “A computer-implemented method for determining lung pathology from an audio respiratory signal is disclosed”), the method comprising:
receiving a training set comprising a plurality of breath flow signals and a plurality of audio signals for a convolutional neural network, wherein the training set is extracted from subjects with known pathologies of known degrees of severity ([0017]: “The method comprises inputting a plurality of audio files comprising a training set into an artificial neural network, wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity”; [0426]-[0427]: “Other physiological measurements and diagnostics, including pulmonary function testing (spirometry) … These recordings together with the annotated metadata comprise the “training set.”; [0288]: ”the spirometer device simultaneously records airflow volumes and lung sounds.”; [0284]: “The spirometer may comprise a microphone 2601, a flow sensor 2602”);
analyzing the plurality of audio signals and the plurality of breath flow signals to extract a plurality of descriptors therefrom ([0110]: “the present invention detects and analyzes audio-extracted breath sounds from a full breath cycle, recognizing the different breath phases (inhale, transition, exhale, rest), detecting characteristics about the breath phases and the breath cycle such as inhale, pause, exhale, rest duration, the wheeze source and type (source of the constriction causing the wheeze can be either nasal or tracheal and the type of the constrictions can be either tension or wheezing) and cough type and source, choppiness and smoothness, attack and decay, etc. These breath cycle characteristics are obtained from the extraction of different audio descriptors from a respiratory audio signal and the performance of audio signal analysis on the descriptors.”), wherein the plurality of descriptors comprises flow-based descriptors and sound-based descriptors ([0288]: “the spirometer device simultaneously records airflow volumes and lung sounds. Standardized measurements of spirometry are combined with the dynamic classification of lung sounds, such as wheeze and crackles (from the DRCT framework), to improve the detection of the presence, progression and severity of lung pathology and disease”; [0293]: “Embodiments of the present invention advantageously extract sound-based wheeze descriptors, spectrograms, spectral profiles, sound-based airflow descriptors and sound based crackle descriptors, all of which can detect and track both the audible and inaudible characteristics of wheezing and crackles that occur in breathing.”; Fig. 25B).
Stamatopoulos discloses creating a plurality of graphs using information from the plurality of descriptors ([0017]: “The method further comprises annotating the plurality of audio files in the training set with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files.”. Spectrograms are graphs used to represent a signal’s frequency over time, which is created from the data and used for analysis.), however Stamatopoulos does not include the flow-based descriptors in the spectrograms.
O’Keeffe discloses a system and method using a sound-producing breathing device to determine pulmonary function. Specifically, O’Keeffe teaches creating a graph using information from the flow-based descriptors and sound-based descriptors ([00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”). Stamatopoulos and O’Keeffe are analogous arts as they are both related to monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the graphs using both the flow-based descriptors and sound-based descriptors from O’Keeffe into the method from Stamatopoulos as it provides more information into the method used for analysis which can provide a more complete and comprehensive analysis, as well as show the relation between the flow descriptors and sound descriptors.
However, the Stamatopoulos/O’Keeffe combination does not teach causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs.
Stamatopoulos ‘367 discloses a method and apparatus for training and evaluating neural networks used to determine lung pathology. Specifically, Stamatopoulos ‘367 teaches causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs (Fig. 36; [0429]: “FIG. 36 illustrates exemplary original spectrogram PDFs aggregated over pathology and severity in accordance with an embodiment of the present invention. As will be discussed further below, the PDFs are used in the evaluation module (discussed in connection with FIG. 35) to decide if a new respiratory recording inputted into the ANN belongs to a healthy category or to a category indicating disease by employing a Binary Hypothesis Likelihood Ratio Test”; [0354]: “residual airflow sounds 3117 may also be visible on the magnified spectrogram”). Stamatopoulos, O’Keeffe, and Stamatopoulos ‘367 are analogous arts as they are all related to methods that monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the synthesized pattern image from Stamatopoulos ‘367 into the Stamatopoulos/O’Keeffe combination as it allows the method to analyze the plurality of graphs in an easier form by combining them into one image, which can allow for easier analysis and processing.
The Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination teaches training the convolutional neural network (Stamatopoulos, [0279]: “the feature extraction and classification is performed using artificial intelligence (AI) algorithms such as Deep Fully Convolutional Neural Network (CNN) architectures or other artificial neural networks (ANNs)”) using the plurality of graphs (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors”) and the synthesized pattern image (Stamatopoulos ‘367, [0438]: “This binary decision can be carried out after the PDFs in the training set are averaged and the resulting PDFs are correlated with a pathology pattern (mild to severe as shown in FIG. 36)”; Fig. 34, reference character 3403);
creating at least one test graph using a breath flow signal and an audio signal from the subject under test (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network”; [0451]: “a new recording from a new patient is inputted into the deep learning process. At step 3812, using the deep learning process a pathology is determined with an associated severity for the new patient”; [0434]: “The evaluation or decision-making module 3500 shown in FIG. 35 receives as an input a new recording at block 3501. The evaluation module then applies time frequency analysis and extracts a spectrogram (and associated PDF) at block 3502. This is similar to the way in which spectrograms and PDFs are extracted at blocks 3402 and 3403 in the training process shown in FIG. 34.”; O’Keeffe, [00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”), wherein the breath flow signal and the audio signal are annotated with metadata associated with the subject under test (Stamatopoulos, [0425]: “The metadata used to annotate the respiratory recordings at block 3401 may comprise respiratory measurements and diagnostics 3411 (spirometry, plethysmography, inflammatory markers, ventilation, CT scans, auscultation, etc.), medication 3412, patient symptoms 3413, and doctor's diagnoses 3414.”);
inputting the at least one test graph associated with the subject under test into the convolutional neural network (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network. Finally, the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determining an existing pathology and associated severity for the subject under test using the convolutional neural network (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determining a prediction for a future condition of the subject under test using the at least one test graph and the metadata associated with the subject under test (Stamatopoulos, [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”); and
determining the lung pathology severity (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”).
However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method for determining the lung pathology severity.
Oster teaches a technique for analyzing auscultatory sounds to aid a medical professional in diagnosing physiological conditions of a patient. Specifically, Oster teaches determining the lung pathology severity by computing a distance between the future condition of the subject under test and the existing pathology and associated severity ([0035]: “Diagnostic device may display a severity indicator based on a calculated distance from which the mapped auscultatory sounds of patient is from the normal region”). Oster, Stamatopoulos, and O’Keeffe are analogous arts as they are both methods directed to using sounds from the body to monitor and diagnose patients.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the method for determining the lung pathology severity from Oster in the method from the Stamatopoulos/O’Keeffe combination, as the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method used to determine the severity, and Oster provides a method that is suitable for using sounds to monitor and diagnose a patient.
Regarding claim 3, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, further comprising: updating the training set with the at least one test graph associated with the subject under test; and repeating the training of the convolutional neural network with the training set as updated by the updating (Stamatopoulos, [0452]: “the training set of audio files is updated with the recording of the new patient and the training process is repeated with the additional new recording.”).
Regarding claim 7, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein determining the lung pathology severity further comprises: using statistical analysis to analyze a progression of the existing pathology and severity towards the future condition (Stamatopoulos, [0441]: “Each time a new respiratory recording related to the patient is fed into the system, the test is repeated taking into account the stored data in order to detect a possible statistical change that could mean that early stages of pathology or lung disease are present”).
Regarding claim 8, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, further comprising:
updating the training set with the at least one test graph associated with the subject under test (Stamatopoulos, [0452]: “the training set of audio files is updated with the recording of the new patient and the training process is repeated with the additional new recording.”);
repeating the training of the convolutional neural network with the training set as updated by the updating (Stamatopoulos, [0452]: “the training set of audio files is updated with the recording of the new patient and the training process is repeated with the additional new recording.”);
performing a second computation of an existing pathology and severity for the subject under test using the convolutional neural network (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”); and
calculating a trajectory towards the future condition of the subject under test using the existing pathology and severity and the second computation of the existing pathology and severity (Stamatopoulos, [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”).
Regarding claim 12, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein the creating the plurality of graphs comprises annotating the plurality of graphs with metadata, wherein the metadata is selected from a group consisting of: metadata associated with subjects with a similar risk profile as the subject under test; metadata health status; pathology; results from diagnostic tests; severity of pathology; respiratory measurements and diagnostics; inflammatory markers; CT scans; auscultation; pulmonary function testing; blood oxygen levels; respiratory gas analysis; body temperature; blood and sputum inflammatory and genetic markers; medication usage; air quality; and exercise and diet habits (Stamatopoulos, [0425]: “The metadata used to annotate the respiratory recordings at block 3401 may comprise respiratory measurements and diagnostics (spirometry, plethysmography, inflammatory markers, ventilation, CT scans, auscultation, etc.), medication, patient symptoms, and doctor's diagnoses.”).
Regarding claim 13, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein the training set is captured by a spirometer comprising a flow sensor and a microphone (Stamatopoulos, [0284]: “The spirometer may comprise a microphone, a flow sensor …”).
Regarding independent claim 14, Stamatopoulos teaches a non-transitory computer-readable storage medium having stored thereon, computer executable instructions that, if executed by a computer system cause the computer system to perform a method of determining lung pathology severity from a subject under test ([0018]: “a non-transitory computer-readable storage medium having stored thereon, computer executable instructions that, if executed by a computer system cause the computer system to perform a method for determining lung pathology”), the method comprising:
receiving a training set comprising a plurality of breath flow signals and a plurality of audio signals for a convolutional neural network, wherein the training set is extracted from subjects with known pathologies of known degrees of severity ([0017]: “The method comprises inputting a plurality of audio files comprising a training set into an artificial neural network, wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity”; [0426]-[0427]: “Other physiological measurements and diagnostics, including pulmonary function testing (spirometry) … These recordings together with the annotated metadata comprise the “training set.”; [0288]: ”the spirometer device simultaneously records airflow volumes and lung sounds.”);
analyzing the plurality of audio signals and the plurality of breath flow signals to extract a plurality of descriptors ([0110]: “the present invention detects and analyzes audio-extracted breath sounds from a full breath cycle, recognizing the different breath phases (inhale, transition, exhale, rest), detecting characteristics about the breath phases and the breath cycle such as inhale, pause, exhale, rest duration, the wheeze source and type (source of the constriction causing the wheeze can be either nasal or tracheal and the type of the constrictions can be either tension or wheezing) and cough type and source, choppiness and smoothness, attack and decay, etc. These breath cycle characteristics are obtained from the extraction of different audio descriptors from a respiratory audio signal and the performance of audio signal analysis on the descriptors.”), wherein the plurality of descriptors comprises flow-based descriptors and sound-based descriptors ([0288]: “the spirometer device simultaneously records airflow volumes and lung sounds. Standardized measurements of spirometry are combined with the dynamic classification of lung sounds, such as wheeze and crackles (from the DRCT framework), to improve the detection of the presence, progression and severity of lung pathology and disease”; [0293]: “Embodiments of the present invention advantageously extract sound-based wheeze descriptors, spectrograms, spectral profiles, sound-based airflow descriptors and sound based crackle descriptors, all of which can detect and track both the audible and inaudible characteristics of wheezing and crackles that occur in breathing.”; Fig. 25B).
Stamatopoulos discloses creating a plurality of graphs using information from the plurality of descriptors ([0017]: “The method further comprises annotating the plurality of audio files in the training set with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files.”. Spectrograms are graphs used to represent a signal’s frequency over time, which is created from the data and used for analysis.), however Stamatopoulos does not include the flow-based descriptors in the spectrograms.
O’Keeffe discloses a system and method using a sound-producing breathing device to determine pulmonary function. Specifically, O’Keeffe teaches creating a graph using information from the flow-based descriptors and sound-based descriptors ([00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”). Stamatopoulos and O’Keeffe are analogous arts as they are both related to monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the graphs using both the flow-based descriptors and sound-based descriptors from O’Keeffe into the method from Stamatopoulos as it provides more information into the method used for analysis which can provide a more complete and comprehensive analysis, as well as show the relation between the flow descriptors and sound descriptors.
However, the Stamatopoulos/O’Keeffe combination does not teach causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs.
Stamatopoulos ‘367 discloses a method and apparatus for training and evaluating neural networks used to determine lung pathology. Specifically, Stamatopoulos ‘367 teaches causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs (Fig. 36; [0429]: “FIG. 36 illustrates exemplary original spectrogram PDFs aggregated over pathology and severity in accordance with an embodiment of the present invention. As will be discussed further below, the PDFs are used in the evaluation module (discussed in connection with FIG. 35) to decide if a new respiratory recording inputted into the ANN belongs to a healthy category or to a category indicating disease by employing a Binary Hypothesis Likelihood Ratio Test”; [0354]: “residual airflow sounds 3117 may also be visible on the magnified spectrogram”). Stamatopoulos, O’Keeffe, and Stamatopoulos ‘367 are analogous arts as they are all related to methods that monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the synthesized pattern image from Stamatopoulos ‘367 into the Stamatopoulos/O’Keeffe combination as it allows the device to analyze the plurality of graphs in an easier form by combining them into one image, which can allow for easier analysis and processing.
The Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination teaches training the convolutional neural network (Stamatopoulos, [0279]: “the feature extraction and classification is performed using artificial intelligence (AI) algorithms such as Deep Fully Convolutional Neural Network (CNN) architectures or other artificial neural networks (ANNs)”) using the plurality of graphs (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors”) and the synthesized pattern image (Stamatopoulos ‘367, [0438]: “This binary decision can be carried out after the PDFs in the training set are averaged and the resulting PDFs are correlated with a pathology pattern (mild to severe as shown in FIG. 36)”; Fig. 34, reference character 3403);
creating at least one test graph using a breath flow signal and an audio signal from the subject under test (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network”; [0451]: “a new recording from a new patient is inputted into the deep learning process. At step 3812, using the deep learning process a pathology is determined with an associated severity for the new patient”; [0434]: “The evaluation or decision-making module 3500 shown in FIG. 35 receives as an input a new recording at block 3501. The evaluation module then applies time frequency analysis and extracts a spectrogram (and associated PDF) at block 3502. This is similar to the way in which spectrograms and PDFs are extracted at blocks 3402 and 3403 in the training process shown in FIG. 34.”; O’Keeffe, [00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”), wherein the breath flow signal and the audio signal are annotated with metadata associated with the subject under test (Stamatopoulos, [0425]: “The metadata used to annotate the respiratory recordings at block 3401 may comprise respiratory measurements and diagnostics 3411 (spirometry, plethysmography, inflammatory markers, ventilation, CT scans, auscultation, etc.), medication 3412, patient symptoms 3413, and doctor's diagnoses 3414.”);
inputting the at least one test graph associated with the subject under test into the convolutional neural network (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network. Finally, the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determining an existing pathology for the subject under test using the convolutional neural network (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determining a prediction for a future potential condition of the subject under test using the at least one test graph and the metadata associated with the subject under test (Stamatopoulos, [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”); and
determining the lung pathology severity (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”).
However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method for determining the lung pathology severity.
Oster teaches a technique for analyzing auscultatory sounds to aid a medical professional in diagnosing physiological conditions of a patient. Specifically, Oster teaches determining the lung pathology severity for the subject under test by computing a distance between the future condition of the subject under test and the existing pathology and associated severity ([0035]: “Diagnostic device may display a severity indicator based on a calculated distance from which the mapped auscultatory sounds of patient is from the normal region”). Oster, Stamatopoulos, and O’Keeffe are analogous arts as they are both methods directed to using sounds from the body to monitor and diagnose patients.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the method for determining the lung pathology severity from Oster in the method from the Stamatopoulos/O’Keeffe combination, as the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method used to determine the severity, and Oster provides a method that is suitable for using sounds to monitor and diagnose a patient.
Regarding claim 19, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the non-transitory computer-readable storage medium of Claim 14, wherein determining the lung pathology severity further comprises: using statistical analysis to analyze a progression of the existing pathology and severity towards the future condition (Stamatopoulos, [0441]: “Each time a new respiratory recording related to the patient is fed into the system, the test is repeated taking into account the stored data in order to detect a possible statistical change that could mean that early stages of pathology or lung disease are present”).
Regarding independent claim 20, Stamatopoulos teaches a system for determining lung pathology severity from breath flow and audio respiratory signals ([0019]: “a system for determining lung pathology from an audio respiratory signal is presented”), the system comprising:
a memory for storing a plurality of audio signals ([0019]: “The system comprises a memory for storing a plurality of audio files”), a plurality of breath flow signals ([0426]-[0427]: “Other physiological measurements and diagnostics, including pulmonary function testing (spirometry) … These recordings together with the annotated metadata comprise the “training set.”; [0288]: ”the spirometer device simultaneously records airflow volumes and lung sounds.”), instructions associated with a convolutional neural network ([0019]: “instructions associated with an artificial neural network”) and instructions associated with a process for determining lung pathology severity from the plurality of audio signals and the plurality of breath flow signals ([0019]: “instructions associated with an artificial neural network and a process for determining lung pathology from an audio respiratory signal”); and
a processor coupled to the memory ([0019]: “a processor coupled to the memory”), the processor configured to operate in accordance with the instructions to:
receive a training set comprising the plurality of breath flow signals and the plurality of audio signals for the convolutional neural network, wherein the training set is extracted from subjects with known pathologies of known degrees of severity ([0017]: “The method comprises inputting a plurality of audio files comprising a training set into an artificial neural network, wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity”; [0426]-[0427]: “Other physiological measurements and diagnostics, including pulmonary function testing (spirometry) … These recordings together with the annotated metadata comprise the “training set.”; [0288]: ”the spirometer device simultaneously records airflow volumes and lung sounds.”);
analyze the plurality of audio signals and the plurality of breath flow signals to extract a plurality of descriptors ([0110]: “the present invention detects and analyzes audio-extracted breath sounds from a full breath cycle, recognizing the different breath phases (inhale, transition, exhale, rest), detecting characteristics about the breath phases and the breath cycle such as inhale, pause, exhale, rest duration, the wheeze source and type (source of the constriction causing the wheeze can be either nasal or tracheal and the type of the constrictions can be either tension or wheezing) and cough type and source, choppiness and smoothness, attack and decay, etc. These breath cycle characteristics are obtained from the extraction of different audio descriptors from a respiratory audio signal and the performance of audio signal analysis on the descriptors.”), wherein the plurality of descriptors comprises flow-based descriptors and sound-based descriptors ([0288]: “the spirometer device simultaneously records airflow volumes and lung sounds. Standardized measurements of spirometry are combined with the dynamic classification of lung sounds, such as wheeze and crackles (from the DRCT framework), to improve the detection of the presence, progression and severity of lung pathology and disease”; [0293]: “Embodiments of the present invention advantageously extract sound-based wheeze descriptors, spectrograms, spectral profiles, sound-based airflow descriptors and sound based crackle descriptors, all of which can detect and track both the audible and inaudible characteristics of wheezing and crackles that occur in breathing.”; Fig. 25B).
Stamatopoulos discloses creating a plurality of graphs using information from the plurality of descriptors ([0017]: “The method further comprises annotating the plurality of audio files in the training set with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files.”. Spectrograms are graphs used to represent a signal’s frequency over time, which is created from the data and used for analysis.), however Stamatopoulos does not include the flow-based descriptors in the spectrograms.
O’Keeffe discloses a system and method using a sound-producing breathing device to determine pulmonary function. Specifically, O’Keeffe teaches create a graph using information from the flow-based descriptors and sound-based descriptors ([00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”). Stamatopoulos and O’Keeffe are analogous arts as they are both related to monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the graphs using both the flow-based descriptors and sound-based descriptors from O’Keeffe into the method from Stamatopoulos as it provides more information into the method used for analysis which can provide a more complete and comprehensive analysis, as well as show the relation between the flow descriptors and sound descriptors.
However, the Stamatopoulos/O’Keeffe combination does not teach causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs.
Stamatopoulos ‘367 discloses a method and apparatus for training and evaluating neural networks used to determine lung pathology. Specifically, Stamatopoulos ‘367 teaches causing a pattern creation module to generate a synthesized pattern image, wherein the synthesized pattern image comprises the plurality of graphs (Fig. 36; [0429]: “FIG. 36 illustrates exemplary original spectrogram PDFs aggregated over pathology and severity in accordance with an embodiment of the present invention. As will be discussed further below, the PDFs are used in the evaluation module (discussed in connection with FIG. 35) to decide if a new respiratory recording inputted into the ANN belongs to a healthy category or to a category indicating disease by employing a Binary Hypothesis Likelihood Ratio Test”; [0354]: “residual airflow sounds 3117 may also be visible on the magnified spectrogram”). Stamatopoulos, O’Keeffe, and Stamatopoulos ‘367 are analogous arts as they are all related to methods that monitoring breath sounds and breath flow of a user to determine health conditions.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the synthesized pattern image from Stamatopoulos ‘367 into the Stamatopoulos/O’Keeffe combination as it allows the system to analyze the plurality of graphs in an easier form by combining them into one image, which can allow for easier analysis and processing.
The Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination teaches training the convolutional neural network (Stamatopoulos, [0279]: “the feature extraction and classification is performed using artificial intelligence (AI) algorithms such as Deep Fully Convolutional Neural Network (CNN) architectures or other artificial neural networks (ANNs)”) using the plurality of graphs (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors”) and the synthesized pattern image (Stamatopoulos ‘367, [0438]: “This binary decision can be carried out after the PDFs in the training set are averaged and the resulting PDFs are correlated with a pathology pattern (mild to severe as shown in FIG. 36)”; Fig. 34, reference character 3403);
create at least one test graph using a breath flow signal and an audio signal from a subject under test (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network”; [0451]: “a new recording from a new patient is inputted into the deep learning process. At step 3812, using the deep learning process a pathology is determined with an associated severity for the new patient”; [0434]: “The evaluation or decision-making module 3500 shown in FIG. 35 receives as an input a new recording at block 3501. The evaluation module then applies time frequency analysis and extracts a spectrogram (and associated PDF) at block 3502. This is similar to the way in which spectrograms and PDFs are extracted at blocks 3402 and 3403 in the training process shown in FIG. 34.”; O’Keeffe, [00029]: “FIG. 7B depicts a graph plotting sound intensity as a function of air flow rate, consistent with some embodiments of the present invention”), wherein the breath flow signal and the audio signal are annotated with metadata associated with the subject under test (Stamatopoulos, [0425]: “The metadata used to annotate the respiratory recordings at block 3401 may comprise respiratory measurements and diagnostics 3411 (spirometry, plethysmography, inflammatory markers, ventilation, CT scans, auscultation, etc.), medication 3412, patient symptoms 3413, and doctor's diagnoses 3414.”);
input the at least one test graph associated with the subject under test into the convolutional neural network (Stamatopoulos, [0017]: “the method comprises training the artificial neural network using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors and inputting a recording of a new patient into the artificial neural network. Finally, the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determine an existing pathology for the subject under test using the convolutional neural network (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”);
determine a prediction for a future possible condition of the subject under test using the at least one test graph and the metadata associated with the subject under test (Stamatopoulos, [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”); and
determine the lung pathology severity determine the lung pathology severity (Stamatopoulos, [0017]: “the method comprises determining a pathology and associated severity for the new patient using the artificial neural network.”).
However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method for determining the lung pathology severity.
Oster teaches a technique for analyzing auscultatory sounds to aid a medical professional in diagnosing physiological conditions of a patient. Specifically, Oster teaches determine the lung pathology severity for the subject under test by computing a distance between the future condition of the subject under test and the existing pathology and associated severity ([0035]: “Diagnostic device may display a severity indicator based on a calculated distance from which the mapped auscultatory sounds of patient is from the normal region”). Oster, Stamatopoulos, and O’Keeffe are analogous arts as they are both methods directed to using sounds from the body to monitor and diagnose patients.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the method for determining the lung pathology severity from Oster in the method from the Stamatopoulos/O’Keeffe combination, as the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367 combination is silent on the method used to determine the severity, and Oster provides a method that is suitable for using sounds to monitor and diagnose a patient.
Claims 2, 4, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination as applied to claims 1 and 14 above, with further evidence in view of McKeever (EP 4275255).
Regarding claim 2, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein the determining the prediction for the future condition comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.).
Regarding claim 4, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein the determining the prediction for the future condition of the subject under test comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.) using the at least one test graph, the metadata associated with the subject under test (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”) and metadata associated with subjects with a risk profile similar to that of the subject under test (Stamatopoulos [0445]: “the extracted features may also be stored to the user profile database in order to compare the new user data to the previous user data for tracking purposes. If a new recording shows characteristics of pathology or disease progression, its characteristics can be compared to the data that has been extracted from older recordings in order to estimate the rate of pathology or disease progression.”).
Regarding claim 15, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the non-transitory computer-readable storage medium of Claim 14, wherein the determining the prediction for the future potential condition comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.).
Regarding claim 16, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the non-transitory computer-readable storage medium of Claim 14, wherein the determining the prediction for the future potential condition of the subject under test comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.) using the at least one test graph, the metadata associated with the subject under test (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”) and metadata associated with subjects with a risk profile similar to that of the subject under test (Stamatopoulos [0445]: “the extracted features may also be stored to the user profile database in order to compare the new user data to the previous user data for tracking purposes. If a new recording shows characteristics of pathology or disease progression, its characteristics can be compared to the data that has been extracted from older recordings in order to estimate the rate of pathology or disease progression.”).
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination as applied to claims 1 and 14 above, with further evidence in view of McKeever and further in view of Forbes (US 20110092840).
Regarding claim 5, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 1, wherein the determining the prediction for the future condition comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.). However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination does not teach wherein the stochastic computation analyzes a decay associated with a flow-volume loop exhalation curve associated with the subject under test.
Forbes teaches a sensor that measures airflow and breath sounds to monitor patients for respiratory issues. Specifically, Forbes teaches wherein the stochastic computation analyzes a decay associated with a flow-volume loop exhalation curve associated with the subject under test ([0004]: “Spirometry is a physiological test that measures how an individual inhales or exhales volumes of air as a function of time. The primary signal measured in spirometry may represent volume or flow. The spirometry is typically performed using a spirometer. The spirometer may provide graphs, called spirograms, as a result of the measurements. The spirograms may illustrate a volume-time curve and/or a flow-volume loop.”). Stamatopoulos, Oster, O’Keeffe, and Forbes are analogous arts as they are all directed to using sounds from the body to monitor and diagnose patients.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the flow-volume loop curve from Forbes in the analysis performed by the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination, as the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination teaches using spirometry measurements in the analysis, and Forbes describes a measurement from a spirometry test which provides more information for analysis. This type of graph can be used to show different measurements that may not have been visible otherwise, therefore expanding the knowledge on the patient, leading to a better, more informed diagnosis and prediction.
Regarding claim 17, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the non-transitory computer-readable storage medium of Claim 14, wherein the determining the prediction for the future potential condition comprises performing a stochastic computation (Stamatopoulos [0294]: “the machine learning system, according to embodiments of the present invention, compares respiratory recordings from the same individual to classify the onset, stability or progression of a lung pathology or disease over time.”. Machine learning algorithms are a type of stochastic computation, as stated in McKeever ([0031]: “the algorithm that is implemented as a stochastic algorithm such as a machine learning algorithm”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the evidence of McKeever that the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches stochastic computation.). However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination does not teach wherein the stochastic computation analyzes a decay associated with a flow-volume loop exhalation curve associated with the subject under test.
Forbes teaches wherein the stochastic computation analyzes a decay associated with a flow-volume loop exhalation curve associated with the subject under test ([0004]: “Spirometry is a physiological test that measures how an individual inhales or exhales volumes of air as a function of time. The primary signal measured in spirometry may represent volume or flow. The spirometry is typically performed using a spirometer. The spirometer may provide graphs, called spirograms, as a result of the measurements. The spirograms may illustrate a volume-time curve and/or a flow-volume loop.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the flow-volume loop curve from Forbes in the analysis performed by the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination, as the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/McKeever combination teaches using spirometry measurements in the analysis, and Forbes describes a measurement from a spirometry test which provides more information for analysis. This type of graph can be used to show different measurements that may not have been visible otherwise, therefore expanding the knowledge on the patient, leading to a better, more informed diagnosis and prediction.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination as applied to claims 1 and 14 above, in further view of Forbes.
Regarding claim 6, the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination teaches the method of Claim 1. However, the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination does not teach wherein a subset of the plurality of descriptors is associated with the plurality of breath flow signals and is further selected from a group consisting of flow over time descriptors, flow over volume descriptors and flow volume loop descriptors.
Forbes teaches wherein the flow-based descriptors are further selected from a group consisting of flow over time descriptors, flow over volume descriptors and flow volume loop descriptors ([0004]: “Spirometry is a physiological test that measures how an individual inhales or exhales volumes of air as a function of time. The primary signal measured in spirometry may represent volume or flow. The spirometry is typically performed using a spirometer. The spirometer may provide graphs, called spirograms, as a result of the measurements. The spirograms may illustrate a volume-time curve and/or a flow-volume loop.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the volume-time curve or the flow-volume loop curve from Forbes as descriptors for the analysis performed by the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination, as the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination teaches using spirometry measurements in the analysis, and Forbes describes measurements from a spirometry test which provides more information for analysis. This type of graph can be used to show different measurements that may not have been visible otherwise, therefore expanding the knowledge on the patient, leading to a better, more informed diagnosis and prediction.
Regarding claim 18, the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination teaches the non-transitory computer-readable storage medium of Claim 14. However, the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination does not teach wherein a subset of the plurality of descriptors is associated with the plurality of breath flow signals and is further selected from a group consisting of flow over time descriptors, flow over volume descriptors and flow volume loop descriptors.
Forbes teaches wherein the flow-based descriptors are selected from a group consisting of flow over time descriptors, flow over volume descriptors and flow volume loop descriptors ([0004]: “Spirometry is a physiological test that measures how an individual inhales or exhales volumes of air as a function of time. The primary signal measured in spirometry may represent volume or flow. The spirometry is typically performed using a spirometer. The spirometer may provide graphs, called spirograms, as a result of the measurements. The spirograms may illustrate a volume-time curve and/or a flow-volume loop.”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the volume-time curve or the flow-volume loop curve from Forbes as descriptors for the analysis performed by the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination, as the Stamatopoulos/O’Keeffe//Stamatopoulos ‘367/Oster combination teaches using spirometry measurements in the analysis, and Forbes describes measurements from a spirometry test which provides more information for analysis. This type of graph can be used to show different measurements that may not have been visible otherwise, therefore expanding the knowledge on the patient, leading to a better, more informed diagnosis and prediction.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination as applied to claims 1 and 14 above, in further view of Poltorak (WO 2021034784).
Regarding claim 9, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination teaches the method of Claim 8. However, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination does not teach wherein the calculating the trajectory comprises computing a velocity and acceleration towards the future condition.
Poltorak teaches a method for analyzing sampled physiological parameters and predict the future state of the individual based on these parameters. Specifically, Poltorak teaches wherein the calculating the trajectory comprises computing a velocity and acceleration towards the future condition ([0175]: “the state estimate and covariances are coded into matrices to handle the multiple dimensions involved in a single set of calculations. This allows for a representation of linear relationships between different state variables (such as position, velocity, and acceleration) in any of the transition models or covariances”; [0180]: “the sensor data from the individual are principally analyzed as state variables within the model, to predict errors in predictiveness of the model, as well as predicted state of the individual”; [0181]: “Whatever physiological parameter is being considered, discovering its acceleration, i.e., the second derivative over time, reveals the dynamic of the system – the “force” that is acting on the system, causing the system to “accelerate” the evolution of the given parameter over time”). Stamatopoulos, O’Keeffe, Oster, and Poltorak are analogous arts as they are all directed to using measured parameters from the body to monitor and diagnose patients.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the step of determining the velocity acceleration of the chosen parameters and using it to analyze the progression of the condition and severity in the method from the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster combination as it allows for the progression of the condition to be looked at, which gives the patient insight into their condition and how it will progress.
Regarding claim 10, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/Poltorak combination teaches the method of Claim 9, wherein the calculating the trajectory comprises computing the velocity and acceleration towards the future condition, and analyzing the velocity and acceleration statistical analysis (Poltorak [0195]: “Depending on the urgency of the detection of a change in status, a statistical temporal analysis of the signal, signal difference, and higher-order difference over-time, may be analyzed to filter unattributed variance, e.g., noise. In cases where an immediate response to a change in the patient’s state is required, a statistical temporal analysis may only be backward-looking, and the system must remain sensitive to immediate changes”; Stamatopoulos, [0441]: “Each time a new respiratory recording related to the patient is fed into the system, the test is repeated taking into account the stored data in order to detect a possible statistical change that could mean that early stages of pathology or lung disease are present”).
Regarding claim 11, the Stamatopoulos/O’Keeffe/Stamatopoulos ‘367/Oster/Poltorak combination teaches the method of Claim 9, further comprising: flagging an alert responsive to a determination that the velocity and the acceleration have exceeded a prescribed threshold (Poltorak, [0205]: “the alert may be sent when the future value of said at least one physiological parameter, computed using a value of the physiological parameter, its first derivative, and the second derivative, is predicted to will exceed a predetermined or adaptively determined threshold value indicating an abnormal condition or a medical condition, for example at any future time, within a predetermined time interval, or an adaptively determined interval”).
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been 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.
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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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791