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
This Office Action is responsive to the amendment filed on 24 Jun 2026. As directed by the amendment: claims 31-32 have been amended, claims 1-20 have been canceled, claims 33-34 have been withdrawn, and no claims have been added. Thus, claims 21-32 are presently pending examination.
Claim Objections
Applicant is advised that should claim 30 be found allowable, claim 31 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 21-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Determination as to whether a claim satisfies the criteria for subject matter eligibility is a stepwise process (MPEP 2016).
Step 1: Does the claim fall within a statutory category of invention?
Claims 21-32 recite a machine (system), which is within the four statutory categories. Therefore, claims 21-32 are directed to a statutory category of invention.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 21-32 are directed to an abstract idea.
Claim 21 is directed to a computing system, comprising: one or more processors; and
one or more tangible, non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: inputting audio data into a first portion of a machine-learned health model, wherein the audio data was generated based on an audio recording obtained using a microphone; receiving, as an output of the first portion of the machine-learned health model generated based on the audio data, embedded health information data that is indicative of a health state of a user; inputting the embedded health information data into a second portion of the machine-learned health model; and
receiving as an output of the second portion of the machine-learned health model generated based on the embedded health information data, predicted health information indicative of the health state of the user.
The limitations of inputting data and receiving outputs from a machine-learned health model, as drafted, under their broadest reasonable interpretations, are merely mental processes, because these steps are akin to having a doctor or other human actor performing these operations with pen and paper. For example, “receiving as an output of the second portion of the machine-learned health model generated based on the embedded health information data, predicted health information indicative of the health state of the user” encompasses nothing more than a human actor mentally evaluating the embedded health information data and forming a prediction based on the data and predetermined logic rules.
Therefore, claim 21 recites an abstract idea.
Claims 22-32 depend on claim 21. These dependent claims only recite additional features of the analysis described in claim 21, which may also be performed by a human actor mentally and using a pen and paper. For example, claim 23 recites “wherein the second portion of the machine-learned health model comprises a classifier, and wherein the predicted health information comprises a classification output from the classifier generated based on the embedded health information data”, which encompasses nothing more than a human actor mentally evaluating the embedded health information data and drawing a conclusion about how to classify the data.
Therefore, claims 21-32 recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
This judicial exception is not integrated into a practical application.
Claim 21 recites the additional limitations “one or more processors” and “one or more tangible, non-transitory computer-readable media”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0048], [0054], [0058], [0062] describe the processor and memory components at a high level of generality. These generic processor and memory limitations are no more than mere instructions to apply the exception using a generic computer component. 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. Therefore, claim 21 does not integrate the judicial exception into a practical application.
Claim 21 recites the additional limitation “a microphone”, and claim 32 recites the limitations “a wearable device” and “a mobile computing device”, which amount to no more than mere pre-solution activity of data gathering. Therefore, the claimed generic microphone, wearable device, and mobile computing device do not integrate the judicial exception into a practical application.
Thus, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. As described above, dependent claims 22-31 only recite other limitations of the mental process steps recited in claim 21, which may be done mentally by a human actor and/or with a pen and paper.
Step 2B: Does the claim include additional elements that are sufficient to amount to significantly more than the judicial exception?
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above with respect to the integration of the judicial exception into a practical application (Step 2A, Prong 2), the additional elements of using computer components to perform the process steps amounts to no more than mere instructions to apply the judicial exception using generic computer elements. The structural elements recited in claim 21 are “one or more processors” and “one or more tangible, non-transitory computer-readable media”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0048], [0054], [0058], [0062] describe the processor and non-transitory computer-readable medium at a high level of generality, and only provide conventional, well-known computing functions that do not add meaningful limits to practicing the abstract idea.
Claim 21 recites the additional limitation “a microphone”, and claim 32 recites the limitations “a wearable device” and “a mobile computing device”. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A, Prong 2), the additional elements of a micro to collect data amounts to no more than mere pre-solution activity of data gathering. This pre-solution activity of data gathering using wearable microphones is well-understood, routine, and conventional in the field of smart wearable technology. For example, see Seneviratne et al. (A Survey of Wearable Devices and Challenges, 2017), which describes known methods of using wearable electronics to collect sense and classify non-speech body sounds (page 2584, BodyBeat). Therefore, the claimed generic microphone, device, and computer processing elements are all well-understood, routine, and conventional in the field of smart wearable technology.
Therefore, claims 21-32 are not patent-eligible under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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 (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 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.
Claims 21-25, 27, and 32 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kakkar et al. (US 20160089089 A1), hereinafter Kakkar.
Regarding claim 21, Kakkar discloses a computing system (Fig. 1A), comprising:
one or more processors (Fig. 1C, paragraph [0042], central processing unit/microprocessor 121); and
one or more tangible, non-transitory computer-readable media that store instructions (paragraph [0042], "The central processing unit 121 is any logic circuitry that responds to and processes instructions fetched from the main memory unit 122") that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
inputting audio data into a first portion of a machine-learned health model (paragraph [0068], "DSP engine 212 can process the data signals generated by the pulse sensor 213 and breath sensor 214"; paragraph [0072], "The EP application 120 may retrieve or be provided data from the databases stored within the storage device 128. ... Data from...the patient behavioral database 304...can be provided to the EP application 120"; paragraph [0077], "The patient behavioral database 304 may receive and store data from the sensors 308 and the external sensor 204"), wherein the audio data was generated based on an audio recording obtained using a microphone (paragraph [0071], "The breath sensor 214 can include multiple microphones 219"; paragraph [0082], "a microphone is used to record audio sequences of the user's breathing or speech");
receiving, as an output of the first portion of the machine-learned health model generated based on the audio data, embedded health information data that is indicative of a health state of a user (paragraph [0068], "The DSP engine 212 can analyze the data signals generated by the breath and pulse sensors to identify one or more features of those data signals. For example, the DSP engine 212 can analyze breath measurements to determine an inspiration and expiration ratio; analyze breath measurements to determine a breath rate; and analyze breath measurements to detect at least one of a cough, a wheeze, an apnea condition, and a use of an inhaler");
inputting the embedded health information data into a second portion of the machine-learned health model (paragraph [0075], "predictive engine 316 may use the digital biomarkers as inputs into a machine learning algorithm, such as clustering algorithm, neural network, or a support vector machine"); and
receiving as an output of the second portion of the machine-learned health model generated based on the embedded health information data, predicted health information indicative of the health state of the user (paragraph [0075], "The predictive engine 316 of the application 120 may be designed, constructed and/or configured to make predictions about the exacerbation of a disease based on one or more digital biomarkers").
Regarding claim 22, Kakkar discloses the computing system of claim 21, as explained above. Kakkar further discloses that the predicted health information indicates a health condition or a medical symptom (paragraph [0076], "The digital biomarker engine 320 may be designed, constructed and/or configured to provide one or more digital biomarkers to the predictive engine 316 for a corresponding disease or condition of the user 202"; paragraph [0064], "Using the user 202 provided data, data from the external sensor 204, and data from the sensor 308, the EP application 120 can predict the exacerbation of a disease of the user 202").
Regarding claim 23, Kakkar discloses the computing system of claim 21, as explained above. Kakkar further discloses that the second portion of the machine-learned health model comprises a classifier, and wherein the predicted health information comprises a classification output from the classifier generated based on the embedded health information data (paragraph [0100], "predictive engine 316 uses machine learning to classify the biomarker within a state space").
Regarding claim 24, Kakkar discloses the computing system of claim 22, as explained above. Kakkar further discloses that the predicted health information indicates a trend in the health state of the user (paragraph [0089], "The EP application 120 can also include a reporting module 318. ... The reporting module 318 may also generate reports that provide an overview of the user's health and disease state. For example, the report may include the user's health trends over the past several weeks or months").
Regarding claim 25, Kakkar discloses the computing system of claim 24, as explained above. Kakkar further discloses that the operations further comprise processing the predicted health information using a neural network trained to recognize changes in the predicted health information that are indicative of progression of a health disorder (paragraph [0089], "the trends may show that certain food consumption may worsen the user's health state"; paragraph [0075], "predictive engine 316 may use the digital biomarkers as inputs into a machine learning algorithm, such as clustering algorithm, neural network, or a support vector machine").
Regarding claim 27, Kakkar discloses the computing system of claim 21, as explained above. Kakkar further discloses that the machine-learned health model comprises a portion trained to provide attention to portions of the embedded health information data that are most indicative of the health state of the user (paragraph [0076], "The digital biomarker engine 320 may determine what data from the above sources is clinically relevant to the user's diseases or conditions, or determine what data improves the predictive outcome of the predictive engine, and provide the selected data to the predictive engine 316. ... The disease guideline database 310 can be a lookup table or other database that indicates what data is clinically relevant for a particular disease or condition. For example, the digital biomarker engine 320 may perform a lookup in the disease guideline database to determine that heart rate is a good biomarker for heart disease but not asthma").
Regarding claim 32, Kakkar discloses the computing system of claim 21, as explained above. Kakkar further discloses:
a wearable device comprising the microphone (paragraph [0065], "The sensor 204 can also be referred to as a wearable sensor or a wearable device because, in some implementations, the sensor 204 is coupled to the user 202"; paragraph [0071], "The sensor 204 can also include a breath sensor 214. The breath sensor 214 can include multiple microphones 219"); and
a mobile computing device that comprises the one or more processors and the one or more tangible, non-transitory computer-readable media (paragraph [0041], "FIGS. 1C and 1D depict block diagrams of a computing device 100 useful for practicing an embodiment of the client 102 or a server 106. As shown in FIGS. 1C and 1D, each computing device 100 includes a central processing unit 121, and a main memory unit 122"), wherein the mobile computing device interoperates with the wearable device to obtain the audio data (paragraph [0067], "The sensor 204 can also include a wireless module 211. The wireless module 211 is configured to wirelessly communicate with the client device 102").
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 26 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Kakkar et al. (US 20160089089 A1), hereinafter Kakkar, in view of Patel et al. (US 20140336537 A1), hereinafter Patel.
Regarding claim 26, Kakkar discloses the computing system of claim 21, as explained above. Although Kakkar further discloses that the machine-learned health model classifies non-speech vocal sounds (paragraph [0081], "The EP application 120 may classify the recorded breath sound"), Kakkar does not explicitly disclose that the machine-learned health model was trained using a set of training recordings comprising recordings of non-speech vocal sounds.
However, Patel teaches an audio-based cough detection system (paragraph [0029]) wherein the machine-learned health model was trained using a set of training recordings comprising recordings of non-speech vocal sounds (paragraph [0061], "The audio recordings, made at a 32 kHz sampling rate, were manually annotated as cough, speech, laughter, breathing, throat clearing, sneezing, sniffing, other people's cough, and environmental noise").
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 modify Kakkar with the teachings of Patel so that the machine-learned health model was trained using a set of training recordings comprising recordings of non-speech vocal sounds, because doing so effectively and inexpensively allows for the monitoring and automatic detection of coughs (e.g., without the need for self-reporting) while offering privacy preserving advantages (Patel, paragraph [0029]).
Regarding claim 29, the computing system of claim 26 is obvious over Kakkar and Patel, as explained above. Patel further teaches that the non-speech vocal sounds comprise breathing sounds (paragraph [0061], "The audio recordings, made at a 32 kHz sampling rate, were manually annotated as cough, speech, laughter, breathing, throat clearing, sneezing, sniffing, other people's cough, and environmental noise"), and wherein the audio data comprises a spectrogram of the breathing sounds (paragraph [0046], "An audio signal (e.g., audio stream) may be received as shown in box 210. The audio signal may be represented as an audio spectrogram").
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Kakkar et al. (US 20160089089 A1), hereinafter Kakkar, in view of Sanchez et al. (US 10542930 B1), hereinafter Sanchez.
Regarding claim 28, Kakkar discloses the computing system of claim 27, as explained above. Kakkar does not explicitly disclose that at least a portion of the machine-learned health model was pre-trained using unsupervised learning over an unlabeled collection of sound recordings.
However, Sanchez teaches computer system for assessing sound to analyze a user's sleep (Abstract) wherein at least a portion of the machine-learned health model was pre-trained using unsupervised learning over an unlabeled collection of sound recordings (column 21, lines 33-37, "The sample characteristics may be identified by the system (e.g., by training and/or implementing one or more machine learning algorithms)"; column 22, lines 22-23, "A processor or a processing element may be trained using supervised or unsupervised machine learning"; column 22, lines 33-39, "the machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as sample audio files of known sounds. The machine learning programs may utilize deep learning algorithms that may be primarily focused on audio pattern recognition, and may be trained after processing multiple examples"; column 22, lines 52-55, "In unsupervised machine learning, the processing element may be required to find its own structure in unlabeled example inputs").
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 modify Kakkar with the teachings of Sanchez so that at least a portion of the machine-learned health model was pre-trained using unsupervised learning over an unlabeled collection of sound recordings, because doing so reduces or eliminates the need for in-lab sleep analysis (Sanchez, column 9, line 38).
Claims 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Kakkar et al. (US 20160089089 A1), hereinafter Kakkar, in view of Au et al. (US 20180177432 A1), hereinafter Au.
Regarding claim 30, Kakkar discloses the computing system of claim 21, as explained above. Although Kakkar further discloses performing waveform and frequency analysis of the audio recording (paragraph [0082]), Kakkar does not explicitly disclose that the audio data comprises a spectrogram that describes the audio recording.
However, Au teaches a method and apparatus for acquiring sounds related to breathing and for identifying breathing abnormalities based on the acquired sounds (paragraph [0002]), wherein the audio data comprises a spectrogram that describes the audio recording (paragraph [0040], "The first type of data is the “raw” data, i.e. a recording of sounds that have been sampled by microphone 305"; paragraph [0054], "The “raw” data that may be stored, for example, in memory 172 provides multiple functions. For example, it provides an extended period of time for respiratory sound classification. The data may be processed into a spectrogram ... As a further example, the raw data may be used to improve the algorithm. For example, should an abnormal lung sound be recognized, it can serve as a control, and the raw data is used as a dataset to further refine (or “train”) the pre-specified algorithm").
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 modify Kakkar with the teachings of Au so that the audio data comprises a spectrogram that describes the audio recording, because doing so allows differentiation of each sound type based on its unique feature set (Au, paragraph [0048]).
Regarding claim 31, Kakkar discloses the computing system of claim 21, as explained above. Although Kakkar further discloses performing waveform and frequency analysis of the audio recording (paragraph [0082]), Kakkar does not explicitly disclose that the audio data comprises a spectrogram that describes the audio recording.
However, Au teaches a method and apparatus for acquiring sounds related to breathing and for identifying breathing abnormalities based on the acquired sounds (paragraph [0002]), wherein the audio data comprises a spectrogram that describes the audio recording (paragraph [0040], "The first type of data is the “raw” data, i.e. a recording of sounds that have been sampled by microphone 305"; paragraph [0054], "The “raw” data that may be stored, for example, in memory 172 provides multiple functions. For example, it provides an extended period of time for respiratory sound classification. The data may be processed into a spectrogram ... As a further example, the raw data may be used to improve the algorithm. For example, should an abnormal lung sound be recognized, it can serve as a control, and the raw data is used as a dataset to further refine (or “train”) the pre-specified algorithm").
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 modify Kakkar with the teachings of Au so that the audio data comprises a spectrogram that describes the audio recording, because doing so allows differentiation of each sound type based on its unique feature set (Au, paragraph [0048]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Karakasoglu (US 6290654 B1) discloses an obstructive sleep apnea detection apparatus comprising a neural network pattern recognizer
Abeyratne et al. (US 20150073306 A1) discloses a method of operating a computational device to process patient sounds, the method comprises the steps of: extracting features from segments of said patient sounds; and classifying the segments as cough or non-cough sounds based upon the extracted features and predetermined criteria; and presenting a diagnosis of a disease related state on a display under control of the computational device based on segments of the patient sounds classified as cough sounds.
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/CHRISTINE SISON/Examiner, Art Unit 3796
/REX R HOLMES/Primary Examiner, Art Unit 3796
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