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 .
Claim Objections
Claims 4, 14, 16 are objected to because of the following informalities:
In claim 4, the word “in” should be added so the claim reads “perform the following in real-time” (line 2)
In claim 14, “comprising” should be changed to “comprises” (line 1)
In claim 14, the word “of” should be removed so the claim reads “associated with one cohort of the at least two cohorts” (line 8)
In claim 16, “comprising” should be changed to “comprises” (line 1)
Appropriate correction is required.
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 5 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 5, it is unclear if the sensor is in connection with the same element that the processing system is housed within. For the purpose of continued examination, the examiner has interpreted the limitation to mean the sensor is in connection with the computational processing system; and the computational processing system is housed within a wearable device, hemodynamic monitoring system, or an electrocardiography device. See specification [0074].
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 non-statutory subject matter of abstract ideas under the mental processes and mathematical concepts groupings, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon and (2a) Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Step (1)
The claimed invention in claims 1-20 are directed to a system, and thus, the claims all fall under one of the four patent eligible categories.
Step (2a) Prong 1 (Judicial Exception)
Regarding claims 1-20, the recited steps are directed towards mental processes of performing concepts in a human mind or by a human using a pen and paper and utilizing mathematical concepts (See MPEP 2106.04(a)(2) subsections (I) and (III)).
Independent claim 1 recites:
receive physiological interval data;
fit the point-process model with global optimization to the physiological interval data; and
compute variability of the physiological interval data using the fitted point-process model with global optimization.
Under the broadest reasonable interpretation, these limitations require receiving physiological data, fitting a model to the data, and computing variability of the data using the model. These limitations are processes that, as drafted, cover that which can be wholly performed in a person’s mind via a series of mental observations and judgements and utilizing mathematical concepts. In particular, a person can fit a model to data, then compute variability of the data based on the fitted model. These are data gathering and processing steps (fit, compute) that reflect mental processes and mathematical relationships.
Accordingly, claim 1 is directed to a judicial exception including one or more abstract ideas, specifically mental processes and mathematical concepts.
The additional limitations in claims 2-20 (types of data, device housing, sensor types, model types, calculation of model parameters, machine learning model, prediction of a medical disorder) comprise additional abstract ideas and mathematical concepts and/or further limit the abstract ideas/mathematical concepts of claim 1.
Step (2a) Prong 2 (Integration into a Practical Application)
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
For claims 1-20, the judicial exception is not integrated into a practical application.
Regarding claim 1, the additional element of receiving physiological interval data amounts to recitation of a generic data sensing process. Under the broadest reasonable interpretation, these elements are nothing more than the pre-solution activity of mere data gathering using generic components and clinical testing. See MPEP 2106.05, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989).
Regarding claim 1, the additional elements of a computer system/computational processing system amount to recitation of a generic computer and processor. This additional element merely defines the field of use of the current claim. This additional element does not practically integrate the judicial exception because this element does not provide improvements to the functioning of a computer or to any the technical field under MPEP 2106.05(a). Furthermore, when the claims, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind. Likewise, performance of a claim limitation using generic computer components does not preclude the claim limitation from being in the mental processes grouping.
Dependent claims 3-20 also recite the additional elements of a sensor and wearable device. As in Alice Corp. v. CLS Bank, 573 U.S. 208, 223 (2014), limiting an abstract idea to a field of use or adding generic hardware does not integrate the exception into a practical application.
Step (2b) (Inventive Concept)
The claims also 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 judicial exception into a practical application, the additional elements of a computer and processor in the field of heart rate monitoring are well-understood, routine and conventional activities previously known in the industry as indicated in the following references:
Subramanian et al. (US Pre-Grant Publication 2022/0323002) recites a computer ([0164-0165]) and processor (processor 106, Fig. 1A).
Sullivan et al. (US Pre-Grant Publication 2016/0135706) recites a computer (computing device 550, Fig. 5) and processor ([0261], control unit 120 includes processors).
Dependent claims 3, 5-6 recite a sensor and wearable device which are also recited at a high level of generality and are considered to be well-known, routine and conventional in the art as indicated in the following references:
Subramanian et al. (US Pre-Grant Publication 2022/0323002) recites a sensor (HR sensor 102, Fig. 1A) and wearable device ([0055-0056], components can be integrated together on a single device (ECG, wearable, etc.)).
Sullivan et al. (US Pre-Grant Publication 2016/0135706) recites a sensor (ECG sensing electrodes 112, Fig. 2) and wearable device (wearable medical device 100, Fig. 2).
Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 are thus rejected under 35 USC 101 for reciting patent-ineligible subject matter- abstract ideas and mathematical concepts.
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 1-9, 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Subramanian et al. (US Pre-Grant Publication 2022/0323002), hereinafter ‘Subramanian’.
Regarding claim 1, Subramanian teaches a computer system (Fig. 1A), comprising:
a computational processing system (processor 106, Fig. 1A); and
memory (memory 120, Fig. 1A) comprising an application for fitting a point-process model with global optimization (step 410, Fig. 4, [0088], processor fits distributions to determine if the peaks accurately capture data) ([0062-0063], processor uses a point process model);
wherein the application directs the computational processing system to:
receive physiological interval data ([0015], processor coupled to sensors);
fit the point-process model with global optimization to the physiological interval data (step 604, Fig. 6, [0096], coefficient fitting); and
compute variability of the physiological interval data using the fitted point-process model with global optimization ([0104], assessing heart rate variability (HRV)).
Regarding claim 2, Subramanian teaches the system of claim 1, further comprising:
wherein the physiological interval data is heartbeat interval data ([0105], RR interval).
Regarding claim 3, Subramanian teaches the system of claim 1, further comprising:
wherein the computer system is a part of a health monitoring system (system 100, Fig. 1A) that comprises a sensor configured to capture physiological interval data ([0054], system includes a HR sensor 102, Fig. 1A), wherein the application directs the computational processing system to:
sense physiological parameters of a patient to yield the physiological interval data ([0056], device that obtains HR).
Regarding claim 4, Subramanian teaches the system of claim 3, further comprising:
wherein the application directs the computational processing system to perform the following real-time ([0062], processor performs processing in real time):
sense physiological parameters of a patient to yield the physiological interval data ([0062], HR sensor measures patient's HR);
receive physiological interval data ([0062], sensors send data to processor);
fit the point-process model with global optimization to the physiological interval data (step 604, Fig. 6, [0096], coefficient fitting); and
compute variability of the physiological interval data using the fitted point-process model with global optimization ([0104], assessing heart rate variability (HRV)).
Regarding claim 5, Subramanian teaches the system of claim 3, further comprising:
wherein the sensor is in connection with and the computational processing system is housed within a wearable device, a hemodynamic monitoring system, or an electrocardiography device ([0055-0056], components can be integrated together on a single device (ECG, wearable, etc.)).
Regarding claim 6, Subramanian teaches the system of claim 3, further comprising:
wherein the sensor is one of:
one or more leads of an electrocardiography machine or ECG-like device (([0054], ECG sensor), a blood pressure transducer catheter, a blood pressure cuff (blood pressure sensor 116, Fig. 1A), an ultrasound transducer, a magnetic resonance imaging scanner, or a photoplethysmography ([0056], pulse plethysmography).
Regarding claim 7, Subramanian teaches the system of claim 1, further comprising:
wherein the application directs the computational processing system to:
apply a distribution onto the physiological interval data ([0088], processor fits several distributions), wherein the distribution yields a global optimization when utilized with the point-process model ([0088], identifies accuracy of peaks).
Regarding claim 8, Subramanian teaches the system of claim 7, further comprising:
wherein the distribution is a gamma distribution (Fig. 5B, gamma distribution).
Regarding claim 9, Subramanian teaches the system of claim 1, further comprising:
wherein the point-process model with global optimization comprises a regression model (Step 214, Fig. 2A, [0062], regression model).
Regarding claim 16, Subramanian teaches the system of claim 1, further comprising:
wherein the memory comprising an application for fitting a state-space model with global optimization (Step 214, Fig. 2A, state space models, [0112], state space framework), wherein the application for fitting a state-space model with global optimization directs the computational processing system to:
receive covariate data (respiration sensor 114, Fig. 1A);
fit the state-space model with global optimization to the physiological interval data and the covariate data ([0117-0120], state space model); and
compute a relationship between the physiological interval data and the covariate data using the fitted state-space model with global optimization (Figs. 10E, 11E, [0125-0126], multimodal model, [0129], multimodal model is useful for analyzing transitions in a subject).
Regarding claim 17, Subramanian teaches the system of claim 16, further comprising:
wherein the covariate data is related to the autonomic nervous system ([0107], nervous system activity).
Regarding claim 18, Subramanian teaches the system of claim 17, further comprising:
wherein the covariate data comprises at least one of:
respiration, sleep, stress, posture, inflammation, or drugs (Figs. 10, 11, respiration data).
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 10-11, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US Pre-Grant Publication 2022/0323002) in view of Sullivan et al. (US Pre-Grant Publication 2016/0135706), hereinafter ‘Sullivan’.
Regarding claim 10, Subramanian teaches the system of claim 9, further comprising the use of statistical frameworks to process data (214, Fig. 2A), including a logistic regression model (224a, Fig. 2B). Subramanian does not teach a linear regression model.
Sullivan teaches a method for estimating the risk of a potential cardiac arrhythmia event based on physiological information (abstract), further comprising:
wherein the regression model is a generalized linear model ([0424], linear regression analysis).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Sullivan to include a linear regression model. Doing so would allow for model parameter estimation, as recognized by Sullivan [0424].
Regarding claim 11, Subramanian teaches the system of claim 1, further comprising the use of a neural network to analyze data (224c, Fig. 2B, [0068]), but does not specifically teach inferring weights for the physiological data.
Sullivan teaches a method for estimating the risk of a potential cardiac arrhythmia event based on physiological information (abstract), further comprising:
wherein the application directs the computational processing system to:
infer weights for the physiological interval data (Fig. 8F, [0379], weighted metrics).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Sullivan to include weighting the physiological data. Doing so would provide a basis for the model to predict the likelihood/probability of a medical event, as recognized by Sullivan [0462].
Regarding claim 13, Subramanian and Sullivan teach the system of claim 11. Sullivan teaches the system further comprising:
wherein the application directs the computational processing system to:
infer a shape parameter from the weights ([0403], shape parameter in parametric model for survival analysis).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Sullivan to include inferring a shape parameter from the weights. Doing so would allow for a set of medical events to be mapped onto a function, as recognized by Sullivan [0404].
Regarding claim 14, Subramanian and Sullivan teach the system of claim 11. Sullivan teaches the system further comprising:
wherein the memory comprising an application for training a machine-learning model ([0395], decision thresholds trained using machine learning);
wherein the application for training a machine-learning model directs the computational processing system to:
train a machine-learning model to predict a biological characteristic from weights inferred for the physiological interval data (stage 804, Fig. 8F, [0379], apply predictive machine learning algorithm to generate event estimation of risk scores);
wherein the data for training the model comprises inferred weights from at least two cohorts such that the machine-learning model is trained to predict the biological characteristic (0316-0327], training machine learning model);
wherein the biological characteristic is associated with one of cohort of the at least two cohorts ([0324], at risk of requiring treatment).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Sullivan to include training a machine learning algorithm to predict a biological characteristic. Doing so would allow for the system to determine and flag a patient with an increased risk, as recognized by Sullivan [0380].
Regarding claim 15, Subramanian and Sullivan teach the system of claim 14. Sullivan teaches the system further comprising:
wherein the machine-learning model is to train to predict a likelihood or presence of a medical disorder selected from:
orthostatic hypotension, postprandial hypotension, multiple system atrophy, pure autonomic failure, afferent baroreflex failure, familial dysautonomia, heart failure, metabolic disorder, diabetes, or cardiovascular disease ([0229], acute decompensated heart failure, diabetes, cardiac events/diseases).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Sullivan to include training a machine learning algorithm to predict a likelihood/presence of a medical disorder. Doing so would allow for the patient to prepare for or mitigate the adverse effects of the degradation, as recognized by Sullivan [0228].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US Pre-Grant Publication 2022/0323002) in view of Sullivan et al. (US Pre-Grant Publication 2016/0135706), further in view of Blanco et al. (US Pre-Grant Publication 2012/0245481), hereinafter ‘Blanco’.
Regarding claim 12, Subramanian and Sullivan teach the system of claim 11, but do not teach that the weights are unconstrained/can be positive or negative.
Blanco teaches a system for detecting signals to identify medical conditions (abstract), further comprising:
wherein the weights are unconstrained and capable of being negative or positive ([0092], component weights may be unconstrained).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian and Sullivan to incorporate the teachings of Blanco to include unconstrained weights. Doing so would allow for the computing of a log likelihood function, as recognized by Sullivan [0092].
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (US Pre-Grant Publication 2022/0323002) in view of Schamberg et al. (G. Schamberg, D. Ba and T. P. Coleman, "A Modularized Efficient Framework for Non-Markov Time Series Estimation," in IEEE Transactions on Signal Processing, vol. 66, no. 12, pp. 3140-3154, 15 June, 2018, doi: 10.1109/TSP.2018.2793870), hereinafter ‘Schamberg’.
Regarding claim 19, Subramanian teaches the system of claim 16, further comprising a Gaussian state space model ([0117]), but does not teach a Gauss-Markov process.
Schamberg teaches a signal processing method for point process models (abstract), further comprising:
wherein the state-space model comprises a Gauss-Markov process (Section IV, pgph. 1, state-space Gauss-Markov process).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Schamberg to include a Gauss-Markov process. Doing so would provide a statistical relationship between an observed parameter and an underlying state of a subject, as recognized by Schamberg (Section IV, pgph. 1).
Regarding claim 20, Subramanian teaches the system of claim 16, further comprising:
wherein the state-space model comprises a high dimensionality solution (claim 19, quantitative multi-dimensional measure).
Subramanian does not teach that the state-space model has an alternating direction method of multipliers.
Schamberg teaches a signal processing method for point process models (abstract), further comprising:
an alternating direction method of multipliers (Section I, pgph. 4, ADMM).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Schamberg to include an alternating direction method of multipliers. Doing so would enable models to be easily applied to the signal, as recognized by Schamberg (Section I, pgph. 4).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bashour et al. (US Pre-Grant Publication 2008/0167567) teaches a system for AF prediction from patient ECG data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH L OKONAK whose telephone number is (571)272-1594. The examiner can normally be reached Monday-Friday 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Klein can be reached at (571) 270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.L.O./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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