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 .
Response to Amendment and Arguments.
Applicant’s arguments, see page 5, filed 1/29/2026, with respect to claims 1-3,5,7-13 rejection have been fully considered and are not persuasive. Applicant argues that “A human physician cannot mentally capture, process, and correlate high-fidelity internal auscultation audio with precisely matched far-field external audio from the same events, apply computational filtering/tempo normalization/voice-interim extraction, and computationally correlate them across many specimens to derive a model transferable to consumer devices. This is a technical data-generation and transfer-learning paradigm, not mental observation or judgment.” however audio data is pre-solution activity where the human would receive data. From this data the human would have the data necessary to analyze the internal and external sounds. Hence being a mental process. Applicant further argues that “It provides a specific improvement to the field of non-invasive, remote disease detection (e.g., respiratory infections like COVID-19) in telemedicine.”. Examiner disagrees having an improvement to an abstract idea is still an abstract idea where the doctor can treat the patient in an office. Applicant furthermore argues “The ordered combination of elements is not well-understood, routine, conventional: Paired internal / external collection of raw audio data coupled with specific processing (bandpass, tempo metric, voice-interims); and used for correlation data sets to train for external-only (remote, over the phone) classification is inventive. Generic "ML model" is applied to this unconventional data pipeline, providing more than mere computer implementation.”. However, the examiner disagrees since the collection of this data (internal and external) is data gathering for the generic model. Therefore, the 101 rejection of claims 1- 20 are maintained.
Applicant’s arguments with respect to claim(s) 1-3,5,7-13 have been considered but are not persuasive. Applicant states for claim one that “Stamatopoulos does not disclose audio recordings of internal sounds”. Examiner disagrees, paragraph 266 shows that there is a capture of internal sound by the stethoscope. Also see paragraphs 262 (for wheezing when breathing), paragraph 264 (crackles when breathing), paragraph 287 (for obstruction inflammation or fluid present) and last but not least paragraph 288 capturing lung sounds such as wheeze and crackles by an audio device. Furthermore, the paragraphs above teach internal sounds due to the human anatomy (specifically the subject raspatory system that its capturing). The external sound would be the breathing of the subject without the noise of the raspatory congestion (being the nose and the mouth). Applicant further argues “It is most telling that Examiner uses the same quotations for anticipation of the limitations of an internal dataset as for anticipation of the limitations of an external dataset.”. However, the examiner disagrees since the same paragraphs may disclose more than one teaching. In regards for the internal aspect paragraphs 266 and 425 were relied upon. These paragraphs describe an apparatus for evaluating lung pathology and raspatory recordings being annotated. Therefore, teaching the aspect of internal sounds. In regards to external sounds paragraphs 266, 267, and 425 were used, examiner relies on 266 and 267 because these paragraphs describe a microphone capturing externally when the user is breathing. Therefor the same paragraphs are not relied upon for the same teachings. Further, the two data sets don’t have to be captured by two different devices. Applicant further argues “To the extent the Examiner relies on inherency, such reliance is misplaced. Inherency requires that a feature be necessarily present in the prior art reference, not merely possible or inferable. Here, Stamatopoulos does not necessarily produce audio recordings of internal sounds. Its system records external acoustic signals and treats environmental and bodily sounds as mixed signals to be filtered and processed. Nothing in the reference requires that the resulting recordings constitute internal sound recordings as claimed, nor that such recordings are obtained as a distinct data type.”. However, examiner strongly disagrees since Stamatopoulos does teach internal sound being captured via the paragraphs mentioned above. Further inherency is not being relied upon due to the citations and explanation above. Furthermore, applicant argues for claim two that “Stamatopoulos does not disclose, teach, or suggest, any feature, process, or even recognition of two distinct types of audio recordings, namely "audio recording of internal sounds and said audio recording of external sounds". Stamatopoulos is oblivious to this distinction. Anushiravani suggests synchronizing sensors using an activation signal. However, Stamatopoulos does not show, teach, or suggest different sensors for recording different types of sounds. Therefore, even if one was to modify Stamatopoulos with the synchronization technique of Anushiravani, one would still not achieve the claimed invention of claim 2.”. Examiner disagrees since Stamatopoulos teaches the internal and external sound captures (internal being the wheezing crackles and etc.) while Anushiravani teaches combining the sounds together being the internal signal path and the external signal path (see paragraph 57 ). Applicant further argues for claim 11 and 12 that “Olivero merely uses the same, single microphone to record baseline recordings. There is no distinction made between recording internal sounds and recording external sounds. All the recordings are of external sounds.” However, examiner disagrees Olivero and Moulios are teaching the tempo and adjusting the tempo. This is done to see abnormal breathing by the user (Olivero paragraph 36). Finally, the applicant argues that since claim 13 depends from claim 1 and therefore should be allowable, the examiner respectfully, disagrees due to the explanations above. Therefore, applicants’ argument for claim 1-3,5,7-13 is not persuasive and rejection is till maintained.
Claim Objections
Applicant is advised that should claim 1 be found allowable, claim 7 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3,5,7-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 recites A method for detecting infection from a voice sample, the method comprising:
generating machine learning (ML) training data, including:(i) collecting raw data from a plurality of specimens, for each specimen:
capturing an audio recording of internal sounds of said specimen inhaling and exhaling using a specialized recording device approximating auscultation of a thorax, capturing an audio recording of external sounds of said specimen inhaling and exhaling using a commercial recording device held away from a face of the specimen, and receiving medical data, such that said training data includes: (A) an internal dataset of a plurality of said audio recordings of internal sounds of a plurality of specimens inhaling and exhaling,
(B) an external dataset of a plurality of said audio recordings of external sounds of said plurality of specimens inhaling and exhaling, and
(C) a medical dataset of medical information related to each of said specimens;
(ii) processing said internal and external datasets to generate processed data and metrics for each of said internal and external datasets;
(iii) correlating between said internal dataset, said external dataset and said medical dataset;
(b) training a ML model based on said training data;
(c) classifying a newly received audio recording of external sounds of a user, using said ML model; and
(d) outputting a metric determining a health status of said user.
The limitation of “generating …”, “processing …”, “correlating …”, “training …”, “classifying …”, and “outputting …” , as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person going to the doctor. Where the doctor has a resident with him training him. Where the doctor hears his cough and the way the patient is talking. As well as using a stethoscope to capture the condition of his lungs by hearing what’s going on in his body. Further, corelating between what he heard with the stethoscope and the users cough/speaking to determine the patient’s condition. Additionally, the sounds would be an aspect of training the resident or even the doctor to be on the lookout for a certain sound.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements that are computer components “model” (page 3 lines 18-27) recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “collecting data” which is pre-solution activity by collecting data from the user. Additionally, the claim recites “commercial recording device” which is pre-solution activity by collecting data. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the computer components amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2 additionally recite the method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are synchronized. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the doctor using the stethoscope and hearing the inside and outside when the patients’ breaths at the same time. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 3 additionally recites the method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are unsynchronized. However, this limitation does not prevent a human from performing the steps mentally as described above. Further, the doctor using the stethoscope and hearing the inside of the body. Then stepping back and asks the patient to breath/ cough to hear the cough. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claims 5 additionally recites the method of claim 1, wherein each said audio recording of internal sounds is captured by pressing an audio recorder against a thorax of said specimen. However, these limitations encompass a doctor using a stethoscope on a patient’s chest. Thus, the claim is directed towards a mental process. In particular, the claim only recites additional elements that is “audio recorder” which is pre-solution activity by collecting data. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claims 7 additionally recites 7. The method of claim 1, wherein each said audio recording of external sounds is captured by a recording device held away from a face of said specimen. However, these limitations encompass a doctor talking to the patient and he’s a few steps away from the patient. Thus, the claim is directed towards a mental process. In particular, the claim only recites additional elements that is “recording device” which is pre-solution activity by collecting data. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claims 8 additionally recites the method of claim 1, wherein said specimen inhaling and exhaling is achieved by said specimen performing at least one action selected from the group including: coughing, counting, reciting a given sequence of words. However, these limitations encompass a doctor asking his patient to cough, say a phrase, or count. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 9 additionally recite the method of claim 1, wherein said processing includes: bandpass filtering of raw data of said internal dataset and said external dataset to produce a bandpass filtered data set. However, these limitations encompass a person having a frequency representation of a single and only accepting a certain frequency range. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 10 additionally recites the method of claim 1, wherein said processing includes: detecting a rhythm in each of said plurality of audio recordings of external sounds or said plurality of audio recordings of said internal sounds. However, these limitations encompass a doctor hearing the patients’ lungs with a stethoscope and hearing for a rhythm his lungs are producing. Thus, these claims are directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claims are not patent eligible.
Claim 11 additionally recites the method of claim 10, wherein said rhythm is compared to a reference rhythm having an associated reference tempo, and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo. However, these encompass a doctor analyzing the patient’s rhythm and a reference rhythm of someone sick and comparing the two to generate a data set. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 12 additionally recites the method of claim 11, further comprising: adjusting said data set tempo to match said reference tempo, thereby producing a prepared data set and a corresponding tempo adjustment metric. However, these encompass a doctor analyzing the patient’s rhythm and a reference rhythm of someone sick and comparing the two to generate a data set. Further adjusting the data to more align the reference set. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 13 additionally recites the method of claim 12, further comprising: detecting and removing spoken portions of said prepared data set to produce a voice-interims data set. However, these limitations encompass , a person detecting and removing spoken words from a data set. Thus, the claim is directed towards a mental process. Similar to above, no additional limitations are provided that provide a practical application, or amount to significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3,5,7-10 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US Patent US 20190192047 A1, (Stamatopoulos; Charalampos-Christos.).
Claim 1
Regarding Claim 1, Stamatopoulos teach
1. A method for detecting infection from a voice sample, the method comprising:
(a) generating machine learning (ML) training data, including:
(paragraph 421 "In one embodiment of the present invention, an artificial neural network (ANN) can be trained and evaluated to determine lung pathology, disease type and severity. The ANN system for determining lung pathology comprises a training module (shown in FIG. 34) and an evaluation module (shown in FIG. 35)."
(i) collecting raw data from a plurality of specimens, for each specimen: capturing an audio recording of internal sounds of said specimen inhaling and exhaling,
(paragraph 423 " At block 3401 multiple audio files are inputted into the ANN training software—the audio files may comprise sessions with patients exhibiting symptoms of varying degrees of severity (mild, moderate, severe). Further, the symptoms may relate to a pathology of interest, e.g., asthma."
Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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.")
capturing an audio recording of internal sounds of said specimen inhaling and exhaling using a specialized recording device approximating auscultation of a thorax,
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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.")
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch."
Paragraph 267 "The apparatus may be similar to the apparatus illustrated in FIG. 4, which shows an exemplary breathing microphone set-up used in the methods and apparatus of the present invention. As discussed in connection with FIG. 4, a conventional microphone 420, available commercially, can be used to record the breathing patterns of the user. By using the microphone 420 that comes with many electronic devices (such as an iPad® or iPhone®) and the software as described here within (e.g. in connection with FIGS. 5, 19, 20, and 21), the present invention can detect wheeze and crackle related events. Moreover, the test can be self-administered without requiring special testing equipment or trained personnel."
Also see paragraphs 262 (for wheezing when breathing), paragraph 264 (crackles when breathing), paragraph 287 (for obstruction inflammation or fluid present) and last but not least paragraph 288 capturing lung sounds such as wheeze and crackles by an audio device.)
capturing an audio recording of external sounds of said specimen inhaling and exhaling using a commercial recording device held away from a face of the specimen,
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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.")
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch."
Paragraph 267 "The apparatus may be similar to the apparatus illustrated in FIG. 4, which shows an exemplary breathing microphone set-up used in the methods and apparatus of the present invention. As discussed in connection with FIG. 4, a conventional microphone 420, available commercially, can be used to record the breathing patterns of the user. By using the microphone 420 that comes with many electronic devices (such as an iPad® or iPhone®) and the software as described here within (e.g. in connection with FIGS. 5, 19, 20, and 21), the present invention can detect wheeze and crackle related events. Moreover, the test can be self-administered without requiring special testing equipment or trained personnel.")
and receiving medical data, such that said training data includes:
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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.")
(A) an internal dataset of a plurality of said audio recordings of internal sounds of a plurality of specimens inhaling and exhaling,
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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."
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch.")
(B) an external dataset of a plurality of said audio recordings of external sounds of said plurality of specimens inhaling and exhaling, and
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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."
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch."
Paragraph 267 "The apparatus may be similar to the apparatus illustrated in FIG. 4, which shows an exemplary breathing microphone set-up used in the methods and apparatus of the present invention. As discussed in connection with FIG. 4, a conventional microphone 420, available commercially, can be used to record the breathing patterns of the user. By using the microphone 420 that comes with many electronic devices (such as an iPad® or iPhone®) and the software as described here within (e.g. in connection with FIGS. 5, 19, 20, and 21), the present invention can detect wheeze and crackle related events. Moreover, the test can be self-administered without requiring special testing equipment or trained personnel."))
(C) a medical dataset of medical information related to each of said specimens;
(Paragraph 425 " Additionally, the set of respiratory recordings at block 3401 that the training system uses may be annotated by specialists regarding health status, disease, pathology and severity and can include references from other diagnostic tests such auscultation, spirometry, CT scans, blood and sputum inflammatory and genetic markers, etc. 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.")
(ii) processing said internal and external datasets to generate processed data and metrics for each of said internal and external datasets;
(paragraph 424 "The audio frames are analyzed both using time frequency analysis (used for analyzing wheezes as discussed above) at block 3488 and using non-overlapping frame based analysis (used for analyzing crackles) at block 3408."
Paragraph "428 "Each recording in the training set is analyzed using overlapping frames (as discussed in connection with wheeze module 2700 above) at block 3488. These frames are 4096 samples long and the overlap by 93% of their duration (every 256 samples). For example, if the used sample rate is 44.100 Hz, each frame lasts 92 msecs and the frames overlap every 5 msecs. The exemplary values were chosen to provide temporal and frequency accuracy. It should be noted that both the frame lengths and the overlap duration can vary."
Paragraph 431 "Using the non-overlapping frame based analysis at block 3408, the descriptors pertaining to crackle are also extracted at block 3407 (e.g., the descriptors from block 2756).")
(iii) correlating between said internal dataset, said external dataset and said medical dataset;
(Figure 34 shows the audio files (element 3401) as well as the medical dataset (element 3414) where they all go to the extracted features (element 3409)
Paragraph 432"The next step is to store all the extracted spectrograms and descriptor, wherein the values for each of the respiratory recordings are stored separately in the extracted features database at block 3409. The descriptors are also aggregated over pathology and severity to tune the neural network layers and coefficients at block 3410.")
(b) training a ML model based on said training data;
(Figure 34 shows the audio files (element 3401) as well as the medical dataset (element 3414) where they all go to the extracted features (element 3409) where further they go to AI/Neural network/fuzzy logic system training (element 3410)
Paragraph 432"The next step is to store all the extracted spectrograms and descriptor, wherein the values for each of the respiratory recordings are stored separately in the extracted features database at block 3409. The descriptors are also aggregated over pathology and severity to tune the neural network layers and coefficients at block 3410."
Paragraph 450 "At step 3808, the deep learning process is trained using the plurality of audio files, the spectrograms, the descriptors, and the metadata (e.g. as shown at block 3410)."))
(c) classifying a newly received audio recording of external sounds of a user, using said ML model; and
(Paragraph 433 " FIG. 35 illustrates a block diagram providing an overview of the manner in which an artificial neural network can be used to evaluate a respiratory recording associated with a patient to determine lung pathologies and severity in accordance with an embodiment of the present invention."
Paragraph 434 "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. Further, at block 3502, a histogram of the extracted spectrogram (either original spectrogram or a magnified spectrogram) is calculated. This histogram can be used to obtain the session's PDF.")
(d) outputting a metric determining a health status of said user.
(paragraph 443 "When the respiratory recording is characterized as a pathology at block 3585, the descriptor extraction modules (sound based wheeze descriptors at block 3515, sound based airflow descriptors at block 3516, wheeze source descriptors at block 3517, crackling descriptors at 3503) are employed to extract the pathology and disease related features. The descriptor extraction modules are similar to the blocks 3402, 3403, 3404, 3405, 3406 and 3407 discussed in connection with FIG. 34. The descriptors and all the metadata information from blocks 3511, 3512, 3513 and 3514 are fed into the ANN module 3570. The ANN module 3570 then determines the pathology, disease and severity at block 3566 using the information learned from the processing of the training sets.")
Claim 3
Regarding Claim 3, Stamatopoulos teach 3. The method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are unsynchronized.
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch."
Paragraph 267 "The apparatus may be similar to the apparatus illustrated in FIG. 4, which shows an exemplary breathing microphone set-up used in the methods and apparatus of the present invention. As discussed in connection with FIG. 4, a conventional microphone 420, available commercially, can be used to record the breathing patterns of the user. By using the microphone 420 that comes with many electronic devices (such as an iPad® or iPhone®) and the software as described here within (e.g. in connection with FIGS. 5, 19, 20, and 21), the present invention can detect wheeze and crackle related events. Moreover, the test can be self-administered without requiring special testing equipment or trained personnel."
Having two different single paths would indicate that its unsynchronized )
Claim 5
Regarding Claim 5, Stamatopoulos teach 5. The method of claim 1, wherein each said audio recording of internal sounds is captured by pressing an audio recorder against a thorax of said specimen.
(Paragraph 262 "The occurrence of wheeze is a diagnostic marker for lung disease and is most commonly detected by listening to the lungs with a stethoscope. Some wheeze sounds may also be heard by the person generating the wheeze or a person nearby, and thus the occurrence of wheeze can also be a patient-reported symptom."
Paragraph 266 " Embodiments of the present invention provide an apparatus for evaluating lung pathology that may comprise a microphone or a device with a microphone such as mobile phone that includes a headset and a speaker. The apparatus may comprise one or more of the following devices for lung testing, monitoring and therapy: a mobile phone, a headset, a speaker, a Continuous Positive Airway Pressure (CPAP), a spirometer, a stethoscope, a ventilator, cardiopulmonary equipment, an inhaler, an oxygen delivery device and a biometric patch.")
Claim 7
Regarding Claim 7, Stamatopoulos teach the method of claim 1, wherein each said audio recording of external sounds is captured by a recording device held away from a face of said specimen.
(Paragraph 267 "The apparatus may be similar to the apparatus illustrated in FIG. 4, which shows an exemplary breathing microphone set-up used in the methods and apparatus of the present invention. As discussed in connection with FIG. 4, a conventional microphone 420, available commercially, can be used to record the breathing patterns of the user. By using the microphone 420 that comes with many electronic devices (such as an iPad® or iPhone®) and the software as described here within (e.g. in connection with FIGS. 5, 19, 20, and 21), the present invention can detect wheeze and crackle related events. Moreover, the test can be self-administered without requiring special testing equipment or trained personnel.")
Claim 8
Regarding Claim 8, Stamatopoulos teach the method of claim 1, wherein said specimen inhaling and exhaling is achieved by said specimen performing at least one action selected from the group including: coughing, counting, reciting a given sequence of words.
(paragraph 261 " Wheezing is a continuous harmonic sound made while breathing and may occur while breathing out (exhalation or cough) or breathing in (inhalation). Wheeze or wheezing sounds occur during breathing when there is obstruction, constriction or restriction in the lung airways and is often indicative of lung disease or heart disease that affects the lungs. Wheeze can be categorized as a whistling sound, a stridor (a high pitched harsh wheeze sound) or rhonchi, (a low pitched wheeze sound). Asthma and chronic obstructive pulmonary disease (COPD) are the most common cause of wheeze. Other causes of wheeze can include allergy, pneumonia, cystic fibrosis, lung cancer, congestive heart failure and anaphylaxis.")
Claim 9
Regarding Claim 9, Stamatopoulos teach
9. The method of claim 1, wherein said processing includes:
bandpass filtering of raw data of said internal dataset and said external dataset to produce a bandpass filtered data set.
(Figure 34 shows the audio files (element 3401) where it goes to the nonoverlapping frame based analysis (3408) which is used to analyze crackles
Paragraph 414 "FIG. 32 illustrates the manner in which the filtered impulse response is created by filtering a delta function to create an artificial crackle in accordance with an embodiment of the present invention. The artificial crackle sound is formed by filtering a delta function with a narrow IIR band-pass filter. The filtered frame is the artificial crackle."
Paragraph 424 " The audio frames are analyzed both using time frequency analysis (used for analyzing wheezes as discussed above) at block 3488 and using non-overlapping frame based analysis (used for analyzing crackles) at block 3408.)
Claim 10
Regarding Claim 10, Stamatopoulos teach
10. The method of claim 1, wherein said processing includes:
detecting a rhythm in each of said plurality of audio recordings of external sounds or said plurality of audio recordings of said internal sounds.
(paragraph 251 "In addition, input data regarding the user, client or patient including but not limited to gender, age, height, weight, fitness, level, nutrition, substance use (e.g. drugs, alcohol, smoking etc.), location, health info, lifestyle info, etc. can be used to determine a variety of metrics including ventilatory thresholds. The output data metrics can include, but are not limited to heart rate, power output, rated perceived exertion (RPE), speed of activity, cadence, breath cadence, calories, brain wave patterns, heart rate variability, heart training zones, respiratory training zones, resting metabolic rates, resting heart rate, resting respiratory rate, etc."
Paragraph 252 "Cadence refers to the rhythm, speed, and/or rate of an activity and is frequently referred to in cycling and other sports. Breath cadence is the rhythm of breathing and can be compared to other rhythms including, but not limited to, rpm, strokes, steps, heart beat, etc.")
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 2 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20190192047 A1, (Stamatopoulos; Charalampos-Christos.) in view of US Patent US 20200151516 A1, (Anushiravani; Ramin).
Claim 2
Regarding Claim 2, Stamatopoulos do not explicitly teach all of 2. The method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are synchronized.
However, Anushiravani. teach the method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are synchronized.
(Paragraph 120 " In an embodiment, sensors can be synchronized using an activation signal. For example, a microphone samples pressure in 44.1 kHz rate and a gyroscope sensor output data rate could be 2000 Hz. The sensors can be synchronized by adding redundant data for the sensor with less resolution or down sample the sensor data to the lowest data rate of the sensors. Because different sensors capture different types of data, an activation signal, such as sending a shockwave to the device and synchronize all sensors by undoing the time delay from the received signal."
Paragraph 57 "Fusing different sensors is not a trivial task. Some sensors are used more frequently than others and each sensor could represent one or more numbers that could be on a completely different scale. The details of a fusing mechanism used in the disclosed AI system are discussed in reference to FIG. 4. Based on the type of sensors used and their availability at a current time-stamp, the sensors are synced in a predetermined time-resolution. A feature vector is created using the sensor data. The feature vector can be sparse at a certain time-stamp depending on data availability. The feature vector is fed to an algorithm for inference.")
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 have modified Stamatopoulos to incorporate the teachings of Anushiravani to provide a “The method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are synchronized.” Doing so would Have a larger sample size, as well as to capture symptoms from different audible sources, as recognized by Anushiravani. (paragraph 120, &58).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20190192047 A1, (Stamatopoulos; Charalampos-Christos.) in view of US Patent US 20180353085 A1, (Olivero; Anthony) in further view of US Patent US 8655466 B2, (Moulios; Chris).
Claim 11
Regarding Claim 11, Stamatopoulos do not explicitly teach all of the method of claim 10, wherein said rhythm is compared to a reference rhythm having an associated reference tempo, and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo.
However, Olivero teaches the method of claim 10, wherein said rhythm is compared to a reference rhythm having an associated reference tempo,
((paragraph 36 "In various aspects of the device, the portable biometric monitor 10 may include some functionality to provide and/or assist a healthcare provider in providing analysis of the biometric data. By way of example, and not limitation, the microphone can record a set of baseline recordings of breathing and cardiac sounds under various personal conditions. These baseline sounds may be compared with current sounds to assess whether any atypical or abnormal breathing sounds or cardiac sounds or rhythms are present in the breathing/cardiac patterns of the individual 12. These sounds may be assessed along with information regarding current breathing rate, heart rate and other current data and/or baseline data to assess and/or analyze whether a recommendation is appropriate."))
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 have modified Stamatopoulos to incorporate the teachings of Olivero to provide a “the method of claim 10, wherein said rhythm is compared to a reference rhythm having an associated reference tempo,” Doing so would See if there's any abnormal breathing sounds , as recognized by Olivero . (Paragraph 36).
However, Stamatopoulos in view of Olivero do not explicitly teach and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo.
However, Moulios teaches and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo.
(col 7 lines 42-45 " In one embodiment, correlating the data of the first audio signal to conform to the changes in the second audio signal includes adjusting a tempo of the first audio signal to the tempo of the second audio signal.")
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 have modified Stamatopoulos in view of Olivero to incorporate the teachings of Moulios to provide a “and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo.” Doing so would Have an impact of the listening experience, as recognized by Moulios. (col 1 lines 55-62).
Claim 12
Regarding Claim 12, Moulios further teaches12. The method of claim 11, further comprising:
adjusting said data set tempo to match said reference tempo, thereby producing a prepared data set and a corresponding tempo adjustment metric.
(col 7 lines 42-45 " In one embodiment, correlating the data of the first audio signal to conform to the changes in the second audio signal includes adjusting a tempo of the first audio signal to the tempo of the second audio signal.")
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 have modified Stamatopoulos in view of Olivero to incorporate the teachings of Moulios to provide a “and a data set tempo generated for said external dataset or said internal dataset, said data set tempo being in reference to said associated reference tempo.” Doing so would Have an impact of the listening experience, as recognized by Moulios. (col 1 lines 55-62).
Claims 13 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20190192047 A1, (Stamatopoulos; Charalampos-Christos.) in view of US Patent US 20180353085 A1, (Olivero; Anthony) in further view of US Patent US 8655466 B2, (Moulios; Chris) in further view of US Patent US 20200151516 A1, (Anushiravani; Ramin)
Claim 13
Regarding Claim 13, Stamatopoulos in view of Olivero in further view of Moulios do not explicitly teach all of 13. The method of claim 12, further comprising: detecting and removing spoken portions of said prepared data set to produce a voice-interims data set.
However, Anushiravani teach 13. The method of claim 12, further comprising:
detecting and removing spoken portions of said prepared data set to produce a voice-interims data set.
(Paragraph 52 "In a first step, audio data 201 from a data object 108 is collected. If the data is collected by a microphone (denoted as “m”), the data is augmented using an equalization technique. The equalization technique randomly manipulates the frequency response of the audio data using one or more of a low pass, band pass, high pass or stop band filter to simulate different microphone frequency responses, device placement, and different acoustical environments. In another embodiment, if the data is collected by a digital stethoscope (denoted as “s”) then a different set of equalization filters is used to augment the audio data (e.g., with a focus on capturing device placement variability). Each audio data may also be modified using one or more of the following audio processes: time stretching, time compressing, shifting in time, pitch shifting, adding background noise at different ratios, adding or removing reverberation, etc. The augmentation described above creates many different variations for each data object 108, wherein each variation includes audio data that has been augmented differently than the original recorded audio data and the other audio objects."
Paragraph 112 “Now, consider the same patient speaking in a noisy and reverberant environment which describes the desired semantic in a noisy and reverberation effects. The goal is to create an alternative feature from the cough sound feature that represents the patient coughing in a noisy and reverberant environment using the patient's speech sound in the noisy and reverberant environment.”)
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 have modified Stamatopoulos in view of Olivero in further view of Moulios to incorporate the teachings of Anushiravani to provide a “The method of claim 1, wherein said audio recording of internal sounds and said audio recording of external sounds are synchronized.” Doing so would Have a larger sample size, as well as to capture symptoms from different audible sources, as recognized by Anushiravani. (paragraph 120, &58).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALI M HASSAN whose telephone number is (571)272-5331. The examiner can normally be reached Monday - Friday 8:00am - 4:00pm.
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/ALI M HASSAN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653 05/28/2026
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