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 an abstract idea without significantly more. Claim 1 recites (additional limitations crossed out):
receiving a plurality of murmur-grade values, wherein each murmur-grade value is associated with a different respective auscultation position;
outputting the value indicative of health of the heart valve
The above limitations, as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior, and mental processes. That is, other than reciting the steps as being performed by a “non-transitory computer-readable storage medium storing computer software executed on a processing system” and a “first trained software model”, nothing in the claim precludes the steps as being described as managing personal behavior, or mental processes. For example, but for the “non-transitory computer-readable storage medium storing computer software executed on a processing system” and “first trained software model” language, the limitations describe the obtaining of data (i.e., murmur-grade values) related to a patient, using said data to generate a value indicative of health of a heart valve, and outputting said value, which describes both managing personal behavior, and actions that may be performed mentally and/or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a ““non-transitory computer-readable storage medium storing computer software executed on a processing system” and a “first trained software model” to perform the steps. These additional elements are recited at a high level of generality (see at least Page 10) such that they amount to no more than mere instructions to apply the exception using generic computing components. Moreover, the functionality intended to be performed by the “first trained software model” appears to be based on very rudimentary constraints (e.g., murmur-grade values). Without some prohibition in the claims regarding scalability, computation load, etc., this “first trained software model” could reasonably be considered as merely being applied to the abstract idea (i.e., “apply it”).
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 a “non-transitory computer-readable storage medium storing computer software executed on a processing system” and a “first trained software model” to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component, or generally linking the judicial exception to a particular environment cannot provide an inventive concept. Therefore, the claim is not found to be patent eligible.
Claims 2-17 are dependent on claim 1, and include all the limitations of claim 1. Claim 20 is dependent on claim 19, and includes all the limitations of claim 19. Therefore, they are also directed to the same abstract idea. Claims 5 and 7 merely describe the “first trained software model” that is applied to the judicial exception. Claim 8-12 feature a “second trained software model” that is applied to the judicial exception. The remaining dependent claims do not feature any additional elements, and have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 16, the limitation “The non-transitory computer-readable storage medium of claim 14, comprising instructions for operating a trained software model, implemented by the computer software, to generate the plurality of quality indicator values, wherein the first trained software model is configured to receive a sound recording of the heart as input and to output a murmur-grade value and a quality indicator value for the sound recording” is indefinite. It is unclear if the “trained software model” is the same as the “first trained software model”, the “second trained software model” initially disclosed in claim 8, or a different trained software model, altogether.
Claim Rejections - 35 USC § 102
(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.
Claim(s) 1-4, 7-13, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Beyond Heart Murmur Detection: Automatic Murmur Grading from Phonocardiogram”, by Andoni Elola, available September 27, 2022, hereinafter referred to as Elola1.
Regarding claim 1, Elola discloses A non-transitory computer-readable storage medium storing computer software for assessing health of a heart valve of a heart of a subject, the computer software comprising instructions, which, when executed on a processing system, cause the processing system to carry out a method comprising:
receiving a plurality of murmur-grade values, wherein each murmur-grade value is associated with a different respective auscultation position;”)
inputting the plurality of murmur-grade values to a first trained software model, implemented by the computer software;
operating the first trained software model to generate a value indicative of health of the heart valve, wherein the first trained software model uses [using] murmur-grade values associated with two or more different auscultation positions when generating the value indicative of health of the heart valve; and
outputting the value indicative of health of the heart valve from the first trained software model
(See at least page 3 – “For most patients in the dataset, the PCGs were recorded from four prominent auscultation locations: aortic valve, pulmonary valve, tricuspid valve, and mitral valve.”, and “As an intermediate step, we also evaluate the murmur severity (grade) for each PCG recording (murmur locations were also annotated). In order to classify each recording, 2-D deep convolutional neural networks (CNN)s were used. This section includes a description of the method, a simple data visualization, data preprocessing steps, an elaboration of the classification model, a final decision rule for murmur grading, and the evaluation metrics.”, pages 5-6 – “ The analysis of the murmur’s severity and its corresponding grade are achieved by a joint analysis of several recordings from different auscultation locations. As a result, in our signal processing pipeline, it is necessary to merge the class continuous output (calculated using Equation 1) obtained for each 3 s window to make a decision about the recording first and then provide an overall decision about the patient.”, and Algorithm 1.)
Regarding claim 2, Elola discloses The non-transitory computer- readable storage medium of claim 1, wherein the value indicative of health of the heart valve correlates with a pressure gradient across the heart valve.(See pages 5-6 – “ The analysis of the murmur’s severity and its corresponding grade are achieved by a joint analysis of several recordings from different auscultation locations. As a result, in our signal processing pipeline, it is necessary to merge the class continuous output (calculated using Equation 1) obtained for each 3 s window to make a decision about the recording first and then provide an overall decision about the patient.”, and Algorithm 1.” The Examiner asserts that murmur grade correlates to pressure gradient.)
Regarding claim 3, Elola discloses The non-transitory computer-readable storage medium of claim 1, comprising instructions for receiving at least one of the murmur-grade values as a non-integer value. (See at least page 6 – “To assign a class to a patient, the most severe grade level detected among all recording locations of the same patient was assigned to the patient, with an exception: in the case of patients for whom only some of the auscultation locations were available, even when the most severe grade was loud, we automatically assigned the soft label in order to be consistent with the annotation criteria described in Section III.”
Regarding claim 4, Elola discloses The non-transitory computer-readable storage medium of claim 1,comprising instructions for receiving two, three or four murmur-grade value each murmur-grade value being associated with a different respective one of an aortic, pulmonary, tricuspid or mitral auscultation point. (See at least page 3 – “For most patients in the dataset, the PCGs were recorded from four prominent auscultation locations: aortic valve, pulmonary valve, tricuspid valve, and mitral valve.”
Regarding claim 7, Elola discloses The non-transitory computer-readable storage medium of claim 1,wherein the first trained software model has been trained using supervised learning on training data comprising training tuples, each training tuple comprising i) a plurality of murmur-grade values associated with a corresponding plurality of different auscultation positions for a respective heart, and ii) a ground-truth value indicative of the health of a valve of the respective heart. (See at least Page 3 – Section “The PCG Dataset”)
Regarding claim 8, Elola discloses The non-transitory computer-readable storage medium of claim 1,comprising instructions for:
receiving a plurality of sound recordings of the heart, wherein each sound recording is or has been captured by a microphone positioned at a different respective auscultation position; and
for each sound recording, inputting the sound recording to a second trained software model, implemented by the computer software, and operating the second trained software model to output a murmur-grade value associated with the respective auscultation position.
(See at least page 3 – “Contrary to other PCG datasets [5], [38]–[40], which typically consist of a single recording from a single precordial location for each subject, the CirCor dataset consists of multiple PCG recordings from multiple auscultation locations.” , “The PCGs were recorded with a sampling rate of 4000Hz, using the DigiScope Collector technology embedded in the Littmann 3200 stethoscope [41].”, and “As an intermediate step, we also evaluate the murmur severity (grade) for each PCG recording (murmur locations were also annotated).”, and page 5 – section “Ensemble Learning” which discloses the use of multiple models.)
Regarding claim 9, Elola discloses The non-transitory computer- readable storage medium of claim 8, wherein the second trained software model generates all of the murmur-grade values that are input to the first trained software model. (See at least page 5 – section “Ensemble Learning”)
Regarding claim 10, Elola discloses The non-transitory computer-readable storage medium of claim 8, wherein the second trained software model comprises a trained neural network. (See at least page 3 – “In order to classify each recording, 2-D deep convolutional neural networks (CNN)s were used. This section includes a description of the method, a simple data visualization, data preprocessing steps, an elaboration of the classification model, a final decision rule for murmur grading, and the evaluation metrics.”
Regarding claim 11, Elola discloses The non-transitory computer-readable storage medium of claim 8, wherein the second trained software model is configured to process each sound recording without using information that identifies which auscultation position the sound recording was obtained from. (Elola discloses determining if recordings from all locations are available or not in determining a murmur grade. The specific location of the recording is not considered in the determination, so the Examiner finds that Elola discloses the limitation.)
Regarding claim 12, Elola discloses The non-transitory computer-readable storage medium of claim 8, wherein the second trained software model has been trained using supervised learning on training data comprising training tuples, each training tuple comprising i) a respective sound recording of a respective heart, and ii) a respective ground-truth murmur-grade value for the sound recording. (See at least page 3 – “The entire dataset consists of 5272 PCG recordings from 1568 patients.”, and “The murmur grading was annotated by an expert clinician via listening to the audio recordings and visual inspection of the waveforms.”)
Regarding claim 13, Elola discloses The non-transitory computer- readable storage medium of claim 12, wherein each ground-truth murmur-grade value has been determined by one or more clinicians listening to the respective sound recording and determining a murmur-grade value. See at least page 3 – “The murmur grading was annotated by an expert clinician via listening to the audio recordings and visual inspection of the waveforms.”)
Regarding claim 17, Elola discloses The non-transitory computer-readable storage medium of claim 1,wherein the heart valve is an aortic value valve and wherein the value indicative of health of the heart valve is indicative of the presence of aortic stenosis. (See pages 5-6 – “ The analysis of the murmur’s severity and its corresponding grade are achieved by a joint analysis of several recordings from different auscultation locations. As a result, in our signal processing pipeline, it is necessary to merge the class continuous output (calculated using Equation 1) obtained for each 3 s window to make a decision about the recording first and then provide an overall decision about the patient.”, and Algorithm 1.” The Examiner asserts that murmur grades may be indicative of aortic stenosis; particularly murmur grades of 3 and above.)
Claims 18 and 19 feature limitations similar to those of claim 1, and are therefore rejected using the same rationale.
Claim 20 features limitations similar to those of claim 17, and is therefore rejected using the same rationale.
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.
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elola in view of Lee (US 2023/0355187).
Regarding claim 5, Elola does not explicitly disclose The non-transitory computer-readable storage medium of claim 1,wherein the first trained software model is a regression model. (See at least Lee, Para. [0081] – “In some embodiments, a machine learning algorithm is used to infer systolic and diastolic blood pressures from the available biometric and user profile data. A non-limiting example of a multi-variate linear regression model algorithm is seen below: probability=A.sub.0+A.sub.1(X.sub.1) A.sub.2(X.sub.2)+A.sub.3(X.sub.3)+A.sub.4(X.sub.4)+A.sub.5(X.sub.5)+A.sub.6(X.sub.6)+A.sub.7(X.sub.7) . . . wherein A.sub.i (A.sub.1, A.sub.2, A.sub.3, A.sub.4, A.sub.5, A.sub.6, A.sub.7, . . . ) are “weights” or coefficients found during the regression modeling; and X.sub.i (X.sub.1, X.sub.2, X.sub.3, X.sub.4, X.sub.5, X.sub.6, X.sub.7, . . . ) are data collected from the Subject. Any number of A.sub.i and X.sub.i variable can be included in the model. For example, in a non-limiting example wherein there are 3 X.sub.i terms, X.sub.i is the biometric data, X.sub.2 is the activity data, and X.sub.3 is the probability that an event has been detected or predicted. In some embodiments, the programming language “Python” is used to run the model.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Elola to utilize a multi-variate linear regression model as taught by Lee since both Elola and Lee are in the same field of endeavor (i.e., use of models to determine health status), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 6, Elola does not disclose The non-transitory computer- readable storage medium of claim 5, wherein the first trained software model is a multi-variate linear regression model. (See at least Lee, Para. [0081] – “In some embodiments, a machine learning algorithm is used to infer systolic and diastolic blood pressures from the available biometric and user profile data. A non-limiting example of a multi-variate linear regression model algorithm is seen below: probability=A.sub.0+A.sub.1(X.sub.1) A.sub.2(X.sub.2)+A.sub.3(X.sub.3)+A.sub.4(X.sub.4)+A.sub.5(X.sub.5)+A.sub.6(X.sub.6)+A.sub.7(X.sub.7) . . . wherein A.sub.i (A.sub.1, A.sub.2, A.sub.3, A.sub.4, A.sub.5, A.sub.6, A.sub.7, . . . ) are “weights” or coefficients found during the regression modeling; and X.sub.i (X.sub.1, X.sub.2, X.sub.3, X.sub.4, X.sub.5, X.sub.6, X.sub.7, . . . ) are data collected from the Subject. Any number of A.sub.i and X.sub.i variable can be included in the model. For example, in a non-limiting example wherein there are 3 X.sub.i terms, X.sub.i is the biometric data, X.sub.2 is the activity data, and X.sub.3 is the probability that an event has been detected or predicted. In some embodiments, the programming language “Python” is used to run the model.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Elola to utilize a multi-variate linear regression model as taught by Lee since both Elola and Lee are in the same field of endeavor (i.e., use of models to determine health status), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Examiner Notes
No prior art could be found for the limitations of claim 14 at this time. While reference Watrous (US 2011/0208080) appeared to disclose the limitation “receiving a respective quality indicator value for each of the plurality of received murmur-grade values” (See Para. [0052] – “Graphical display 232 may also include the determined median diastolic interval probabilities to provide a probability representative of the presence or absence of a diastolic and/or a continuous murmur condition. The determined probabilities may be shown with a range variance indicative of a confidence measure.”), no prior art could be found teaching the limitation “inputting the quality indicator values to the first trained software model”.
Conclusion
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Available at https://arxiv.org/pdf/2209.13385v1
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