DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 18 July, 2025.
Claims 1 – 20 are currently pending and have been examined.
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 § 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 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
Claim 1 is representative. Claim 1 recites:
A method of providing potential health condition information regarding a potential health condition of a subject, the method comprising:
collecting a heart sound of the subject;
comparing the heart sound to a plurality of example heart sounds to detect an abnormality;
prompting, in response to the heart sound having an abnormality, a providing of symptom information regarding symptoms noticeable by the subject;
determining, by a first machine learning model and depending upon the heart sound and the symptoms of the subject, the potential health condition; and
providing the potential health condition information to the subject depending upon the potential health condition.
Claim 18 recites a system that executes the steps of the method recited in Claim 1.
STEP 1
The claims are directed to a system and a method, which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea including:
collecting a heart sound of the subject;
comparing the heart sound to a plurality of example heart sounds to detect an abnormality;
prompting, in response to the heart sound having an abnormality, a providing of symptom information regarding symptoms noticeable by the subject;
determining, depending upon the heart sound and the symptoms of the subject, the potential health condition; and
providing the potential health condition information to the subject depending upon the potential health condition.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. The claims recite collecting heart sounds and comparing the heart sounds to example heart sounds to detect an abnormality; obtaining symptoms from the patient; and determining a health condition based on the heart sounds and symptoms. The specification discloses that heart sounds can be collected using a variety of devices including a conventional microphone on a mobile phone. (@ 0016, 0042). Symptoms are solicited from the subject, and provided via a user interface. Comparing heart sounds is disclosed as a comparison of the audio of the heart sounds to the audio recordings of one or more example heart sounds (@ 0046). Collecting and analyzing heart sounds to detect an abnormality, and symptoms representative of a health condition, is a process that, except for generic computer implementation steps, can be performed in the human mind. Cardiologists routinely listen to heart sounds to detect abnormal heart rhythms and solicit symptoms (“Are you having any chest pains?, shortness of breath?, feel any palpitations?) to determine a health condition of the subject.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite determining a health condition of a subject by collecting and comparing heart sounds to example heart sounds . This process is typical in medicine, where a doctor listed to heart sounds using a stethoscope, and the heart sounds are analyzed to determine if any diagnosis is indicated, and is process that merely organizes this human activity. (See MPEP 2016.04 (a)(2) II C finding that “a mental process that a neurologist should follow when testing a patient for nervous system malfunctions” is a method of organizing human activity, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982).
As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
a first machine learning model.
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The machine learning model is recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using a generic computer component. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment.
In particular, the claims replace the knowledge and experience of a cardiologist by applying established methods of machine learning to an abstract diagnostic process in a new data environment – i.e. applying a trained model to the heart sounds and symptom information. The specification teaches that the model may be trained using historical data with known labels (e.g., normal and abnormal heart sounds); using any suitable model (@ 0027). Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices does not provide a practical application of the abstract diagnostic process. The Courts have found that the way machine learning works is the inputs are defined, the model is trained; and then the algorithm is actually updated and improved over time based on the input; “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” Nothing in the claims recites an improvement to the machine learning model itself; rather, the model is merely limited to a particular field of use. (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract diagnostic process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract diagnostic process. The machine learning model is a conventional computer function. For example, the specification discloses machine learning techniques at a high level of generality indicating that they are well-known in the art and purely conventional.
Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract diagnostic process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the health information to a particular type (Claim 2);
further limiting the type of symptoms (Claim 7);
further limiting the type of potential health condition (Claim 10);
those that recite additional abstract ideas such as:
adjusting the ML model depending on symptoms (Claim 4 - 6);
comparing heat sounds to examples (Claim 12, 19);
selecting a model (Claim 13, 20);
extracting heart sound features and comparing (Claim 15 - 16);
ML models for different functional analysis (Claim 17);
those that recite well-understood, routine and conventional activity or computer functions such as:
a user interface to provide information (Claim 3);
a device with a microphone to collect heart sounds (Claim 8 - 9);
a ML model to detect abnormality (Claim 11);
those that recite insignificant extra-solution activities such as:
alerting the subject (Claim 14);
or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 18 – 20 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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, 10 – 12 and 14 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over Motley: (US PGPUB 2020/0373011 A1) in view of Sakai et al.: (US PGPUB 2010/0185101 A1)
CLAIMS 1 and 18
Motley discloses a system and method for early detection of cardiac morbidity that includes the following limitations:
A method of providing potential health condition information regarding a potential health condition of a subject; (Motley Abstract), the method comprising:
collecting a heart sound of the subject; comparing the heart sound to a plurality of example heart sounds to detect an abnormality; determining, by a first machine learning model and depending upon the heart sound of the subject, the potential health condition; and providing the potential health condition information to the subject depending upon the potential health condition; (Motley Abstract, 0003, 0025, 0026, 0049, 0051, 0066, 0067, 0090, 0091).
Motley discloses a system and method for detecting abnormalities in heart sounds, and determining an early diagnosis of a health condition using machine learning techniques (i.e. a first machine learning model). Motley collects heart sounds and compares them to a library of known normal, abnormal and diseased signatures (i.e. a plurality of example heart sounds), to correctly associate them with cardiac abnormalities and diseases (i.e. determining the potential health condition).
With respect to the following limitations:
prompting, in response to the heart having an abnormality, a providing of symptom information regarding symptoms noticeable by the subject; (Sakai 0051, 0087, 0212, 0218);
determining, depending upon the [heart abnormality] and the symptoms of the subject, the potential health condition; and providing the potential health condition information to the subject depending upon the potential health condition; (Sakai 0018, 0076, 0085, 0212, 0218).
Motley discloses detecting heart abnormalities using heart sounds, and determining a health condition of the subject, but does not disclose prompting the subject for symptoms, and determining a health condition of the subject based on the symptoms and the heart abnormality. Sakai discloses a system and method for evaluating biological conditions of a subject that includes determining an abnormality of the heart, and in response, outputting a question to ask subjective symptoms to the subject. The apparatus finalized the determination of the subject’s health condition based on the heart abnormality and the subjective symptom. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley so as to have included soliciting and evaluating symptoms, as well as heart abnormalities, using the machine learning model, in accordance with the teaching of Sakai, in order to allow for finalizing an initial determination.
With respect to Claim 18, the combination of Motley/Sakai discloses the following system components:
a health monitoring and analysis system; (Motley Abstract);
an abnormality detection module that includes a computer processor; (Motley 0026, 0051, 0052);
a machine learning model; (Motley 0025, 0064); and
a notification module; (Motley 0052).
a symptom solicitation module; (Sakai 0051, 0087, 0212, 0218).
CLAIMS 2, 3 and 14
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. Additionally, Motley discloses the following limitations:
wherein the potential health condition information includes at least one of the following: information regarding healthcare providers, a digital map showing a location of at least one healthcare provider, details about the potential health condition, information regarding a clinical trial relevant to the potential health condition, and a recommendation that the subject contacts a healthcare provider; wherein the step of providing the potential health condition information to the subject is performed via a user interface on an electronic mobile device; alerting the subject of a seriousness of the potential health condition; (Motley 0025, 0026, 0053, 0086).
Motley teaches providing information about the results of the analysis including a diagnosis and heart state rating to the user’s mobile device.
CLAIMS 10 – 12 and 19
The combination of Motley/Sakai discloses the limitations above relative to Claims 1 and 18. Additionally, Motley discloses the following limitations:
wherein the potential health condition includes at least one of the following: atrial fibrillation, heart murmurs, structural heart valve disease, precursors to cardiac arrest, a bicuspid aortic valve, and aortic valve stenosis; (Motley 0003, 0050, 0066, 0086 (in particular the table of heart conditions detected));
wherein the step of comparing the heart sound to the plurality of example heart sounds to detect the abnormality is performed by the first machine learning model; (Motley 0003, 0025, 0026);
wherein the step of comparing the heart sound to the plurality of example heart sounds to detect the abnormality further comprises: providing the sound of the heart to an abnormality detection module; accessing the plurality of example heart sounds by the abnormality detection module; and determining the abnormality depending upon the heart sound and the plurality of example heart sounds; (Motley Abstract, 0003, 0025, 0026, 0066, 0067, 0090).
Motley discloses detecting a variety of cardiac health conditions using machine learning techniques to compare a patient’s heart sounds to reference heart sounds representing various abnormalities. Reference heart sounds are obtained from a library for comparison.
CLAIMS 15 – 16
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. Additionally, Motley discloses the following limitations:
wherein comparing the heart sound to example heart sounds further comprises: extracting at least one feature from the heart sound; and comparing the at least one feature to example features associated with example heart sounds; wherein the at least one feature extracted from the heart sound includes at least one of the following: a heart sound interval, a heart sound amplitude, a heart sound frequency feature; (Motley Abstract, 0004, 0068).
Motley discloses extracting features, including time-frequency representations, for comparison to features of reference heart sounds representing various abnormalities
CLAIM 7
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. Additionally, Sakai discloses the following limitations:
wherein the symptoms include at least one of the following: shortness of breath, chest pain, chest tightness, feeling faint, feeling dizzy, heart palpitations, difficulty moving, swelling lower extremities, difficulty sleeping, and decline in activity level; (Sakai 0218).
Motley does not disclose soliciting symptoms; however, Sakai does; including questions about feeling any abnormality of the heart. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley so as to have included soliciting and evaluating symptoms, as well as heart abnormalities, using the machine learning model, in accordance with the teaching of Sakai, in order to allow for finalizing an initial determination.
CLAIM 17
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. With respect to the following limitations:
wherein the step of determining the potential health condition includes the machine learning model determining whether the potential health condition includes the presence of a heart murmur in the subject, determining whether the heart murmur is normal or abnormal; (Motley 0003, 0050, 0066, 0086 including Table 3); and
determining a severity of the potential health condition; (Motley Abstract, 0051, 0092, 0105).
The combination of Motley/Sakai teaches using machine learning to determine heart murmurs, whether they are normal or abnormal, and their severity; however Motley/Sakai does not disclose separate models for each determination – i.e. the first machine learning model, a second machine learning model, a third machine learning model). Nonetheless, performing the determinations using three separate models, instead on one model, is an obvious modification by making models separable (MPEP 2144.04 V. C.)
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Motley: (US PGPUB 2020/0373011 A1) in view of Sakai et al.: (US PGPUB 2010/0185101 A1) and in view of Chou et al.: (US PGPUB 2023/0309949 A1).
CLAIMS 8 and 9
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. With respect to the following limitations:
wherein the step of collecting the heart sound is performed by at least one of the following: an electronic mobile device that includes a microphone and an electronic wearable device that includes a microphone; wherein the electronic wearable device is at least one of the following: a smartwatch and a chest-worn device; (Chou 0008 – 0010, 0016, 0018, 0019, 0053 – 0056).
Motley discloses an acoustic sensor for collecting heart sounds, but not a device with a microphone or a wearable device. Chou discloses a system and method for detecting heart sounds of a subject that includes a wearable device, including a chest worn device. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley so as to have included detecting heart sounds using a wearable device, in accordance with the teaching of Chou, in order to allow for monitoring ambulatory subjects.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Motley: (US PGPUB 2020/0373011 A1) in view of Sakai et al.: (US PGPUB 2010/0185101 A1) and in view of Baker et al.: (US PGPUB 2017/0303844 A1).
CLAIM 20
The combination of Motley/Sakai discloses the limitations above relative to Claim 18. With respect to the following limitations:
a first machine learning model that is configured to have higher specificity and lower sensitivity and a second machine learning model that is configured to have higher sensitivity and lower specificity; (Baker 0023).
The combination of Motley/Sakai discloses machine learning models, but does not disclose models with different sensitivity/specificity. Baker discloses a system and method for monitoring a subject’s health that includes a first model with high sensitivity and low specificity to detect true positives, and a second model with higher specificity and lower sensitivity. The selectivity/specificity of a machine learning model are design choices. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley/Sakai so as to have included models with high sensitivity and low specificity; or with high specificity and low sensitivity, in accordance with the teaching of Baker, in order to use a model that matches computational resources.
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over Motley: (US PGPUB 2020/0373011 A1) in view of Sakai et al.: (US PGPUB 2010/0185101 A1) and in view of Dickie et al.: (US PGPUB 2022/0277175 A1).
CLAIM 4
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. With respect to the following limitations:
adjusting a tolerance of the first machine learning model depending upon the symptom information; (Dickie 0073).
The combination of Motley/Sakai teaches using machine learning to determine health conditions based on heart sounds and symptoms, but does not disclose different models based on symptoms. Dickie teaches a system and method for training and deploying AI models that includes a separate AI model for each symptom. AI models trained based on symptoms inherently include adjusting a tolerance of the model based on the symptom. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley so as to have included adjusting a model depending on symptoms, in accordance with the teaching of Dickie, in order to allow for greater accuracy.
Claims 5, 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Motley: (US PGPUB 2020/0373011 A1) in view of Sakai et al.: (US PGPUB 2010/0185101 A1) and in view of Dickie et al.: (US PGPUB 2022/0277175 A1) in view of Baker et al.: (US PGPUB 2017/0303844 A1).
CLAIM 13
The combination of Motley/Sakai discloses the limitations above relative to Claim 1. With respect to the following limitations:
selecting one of the first machine learning model and a second machine learning model depending upon the symptom information; (Dickie 0073).
The combination of Motley/Sakai teaches using machine learning to determine health conditions based on heart sounds and symptoms, but does not disclose different models based on symptoms. Dickie teaches a system and method for training and deploying AI models that includes a separate AI model for each symptom. It would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley so as to have included selecting a model depending on symptoms, in accordance with the teaching of Dickie, in order to allow for greater accuracy.
With respect to the following limitations:
wherein the first machine learning model is configured to have higher specificity and lower sensitivity and the second machine learning model is configured to have higher sensitivity and lower specificity; (Baker 0023).
The combination of Motley/Sakai/Dickie discloses machine learning models, but does not disclose models with different sensitivity/specificity. Baker discloses a system and method for monitoring a subject’s health that includes a first model with high sensitivity and low specificity to detect true positives, and a second model with higher specificity and lower sensitivity. The selectivity/specificity of a machine learning model are design choices. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley/Sakai/Dickie so as to have included models with high sensitivity and low specificity; or with high specificity and low sensitivity, in accordance with the teaching of Baker, in order to use a model that matches computational resources.
CLAIMS 5 and 6
The combination of Motley/Sakai/Dickie discloses the limitations above relative to Claim 4. With respect to the following limitations:
wherein, in response to the symptom information including that the subject is experiencing no noticeable symptoms, the tolerance of the first machine learning model is focused on specificity; wherein, in response to the symptom information including at least one symptom of the subject, the tolerance of the first machine learning model is focused on sensitivity; (Baker 0023).
The claims require configuring the model to reduce false positives for no symptoms, (i.e. focused on specificity), and to increase true positives for positive symptoms, (i.e. focused on sensitivity). Dickie teaches The combination of Motley/Sakai/Dickie discloses different machine learning models for different symptoms, but does not disclose models with different sensitivity/specificity. Baker discloses a system and method for monitoring a subject’s health that includes a first model with high sensitivity and low specificity to detect true positives, and a second model with higher specificity and lower sensitivity. The selectivity/specificity of a machine learning model are design choices. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the diagnostic system of Motley/Sakai/Dickie so as to have included configuring models with high sensitivity and low specificity to insure true positives are identified when there are no reported symptoms; or with high specificity and low sensitivity to insure true negatives are identified when symptoms are reported, in accordance with the teaching of Baker, in order to use a model that matches computational resources.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2021/0169442 to Agarwal et al discloses a system and method for detecting abnormal heart sounds using a normal heart sound model and an abnormal heart sound model.
US PGPUB 2023/0073613 A1 to Lettman et al. discloses a method and system for classifying heart sounds using neural networks.
US PGPUB 2016/0143573 A1 to Brickman et al. discloses a system and method for configuring machine learning models for specificity or sensitivity.
US PGPUB 2016/0028749 A1 to Murynets et al. discloses a system and method for selecting a machine learning algorithm based on symptoms.
US PGPUB 2021/0128920 A1 to Grill et al. discloses a system and method for selecting a machine learning algorithm based on symptoms.
US PGPUB 2023/0238143 A1 to Edmonds et al. discloses a system and method for selecting a machine learning algorithm based on symptoms.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 8 July, 2026
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