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
The amendment filed May 04, 2026 has been acknowledged. Claims 1, 3, and 6-15 remain pending in the application and are under examination.
Response to Arguments
Applicant’s arguments filed May 04, 2026 have been fully considered but are not persuasive or are moot.
Applicant arguments with respect to the U.S.C. 101 rejection have been considered but are not persuasive. Applicant argues that the claims are “directed to an improved monitoring system that improves functionality and efficiency” by providing means to “identify ejection fractions without in-person imaging”. Additionally, Applicant argues that the claims require “generating a notification which is sent to a communication device”.
Examiner asserts in response to Applicant’s arguments that identifying and classifying ejection fraction without in-person imaging is not represented within the claims and can be performed by the human mind, i.e. it is a mental process. Examiner asserts in response to Applicant’s arguments that the system is generating a notification to a communication device is merely a post-solution activity and does not integrate the abstract idea into a practical application.
Claim Objections
Claim 7 is objected to because of the following informalities: the phrase “of an abnormal patient condition” is repeated twice. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, and 6-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 3, and 6-15 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p. 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019).
Step 1: Independent claims 1, 14, and 15 are directed to a method, method, and system for determining the ejection fraction of a subject. Thus, they are directed to statutory categories of invention (Step 1: YES).
Step 2A, Prong 1:
Claims 1, 14, and 15 recite the following claim limitations which are directed to abstract ideas, specifically mental processes (see MPEP 2106.04(a)(2)):
In re claim 1:
“determining an ejection fraction or a classification of the ejection fraction” (fall under one of observation, evaluation, judgement, or opinion and mathematical concepts, i.e. mathematical functions)
In re claim 14, see above and the following limitations:
“calculates an extreme value of a loss function [0106] for regression [0108] of the ejection fraction from the pre-acquired cardiac current curve data or for classification of the ejection fraction from the pre-acquired cardiac current curve data.” (fall under one of observation, evaluation, judgement, or opinion and mathematical concepts, i.e. mathematical functions)
“and training the machine learning algorithm by an optimization algorithm” (mathematical concepts, i.e. mathematical calculations)
In re claim 15, see above.
These limitations are drawn to an abstract idea because they are, under their broadest reasonable interpretation, mere steps that are capable of being mentally performed or with a pen and paper. For example, determining an ejection fraction or classification of the ejection fraction are a matter of observation, evaluation, judgement, and opinion recognized by the courts as mental processes. Additionally, these limitations are drawn to an abstract idea because they are mathematical concepts, i.e. mathematical functions or calculations.
Step 2A, Prong 2:
Claims 1, 14, and 15 recite the following additional elements:
In re claim 1, “comprising the steps of:
receiving a first data set comprising pre-acquired cardiac current curve data, in particular one-channel cardiac current curve data, captured by an implantable medical device (data gathering)
applying a machine learning algorithm to the pre-acquired cardiac current curve data; and (mere instructions to implement an abstract idea on a generic computer)
outputting a second data set representing
the ejection fraction or
a classification of the ejection fraction
by the machine learning algorithm, wherein the machine learning algorithm is a classification-type algorithm
wherein the second data set comprises at least one of
a first class representing the ejection fraction of a normal patient condition and
a second class representing the ejection fraction of an abnormal patient condition
wherein the second data set further comprises a third class representing that the classification of the ejection fraction is indeterminable from the first data set, in particular from a specific heartbeat of the pre-acquired cardiac current curve data; and (insignificant extra-solution activity)
generating a notification to a communication device of a health care provider based on the output of the machine learning algorithm” (insignificant extra-solution activity)
In re claim 14, see above and the following limitations:
receiving a second training data set representing
an ejection fraction or
a classification of the ejection fraction (data gathering)
In re claim 15, see above.
The above limitations do not integrate the exception into a practical application of the exception because the elements are directed to mere data gathering and insignificant extra-solution activity.
The limitations “receiving a first data set” and “receiving a second training data set” are directed towards pre-solution activity (see MPEP 2106.05(g)) since they’re used to obtain information about the user (i.e. mere data gathering).
The limitation “applying a machine learning algorithm” is directed to mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05 (f). The machine learning algorithm is used to generally apply the idea without placing any limits on how the machine learning algorithm functions, such as how it is being applied to analyze the received data.
The limitation “outputting a second data set… wherein the second data set comprises…” is directed to insignificant post-solution activity (see MPEP 2106.05(g)), as it is just outputting the results found from analyzing the first data sets.
The limitation “generating a notification to a communication device” is directed to insignificant post-solution activity (see MPEP 2106.05(g)) as it is just outputting the results found by the machine learning program to a communication device.
The judicial exception does not integrate the claims as a whole into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claim amount to no more than insignificant extra-solution activity and mere data gathering.
Thus, none of the claims 1, 3, and 6-15 amount to significantly more than the abstract idea itself. Accordingly, claims 1, 3, and 6-15 are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to abstract ideas in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al., MPEP 2106.04(a)(2), MPEP 2106.04(d)(2), and MPEP 2106.05(g).
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.
Claims 1, 3, 6-10, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Attia et al. (US 2020/0397313) in view of Lan et al. (CN 110680282 B).
In re claim 1, Attia discloses a computer implemented method for determining [0008] an ejection fraction [0009] or a classification of the ejection fraction [0052], comprising the steps of:
receiving a first data set comprising pre-acquired cardiac current curve data [0058], in particular one-channel [0058] cardiac current curve data, captured by an implantable medical device ([0006] “the ECG may be acquired from… electrodes affixed to implanted devices”);
applying a machine learning algorithm [0016] to the pre-acquired cardiac current curve data;
outputting a second data set representing the ejection fraction [0045] or a classification of the ejection fraction ([0020], Note: range relates to the classification of ejection fractions into different categories) by the machine learning algorithm [0045],
wherein the machine learning algorithm is a classification-type algorithm [0058],
wherein the second data set comprises at least one of
a first class representing the ejection fraction of a normal patient condition ([0052] “normal ejection-fraction greater than 50-percent”) and
a second class representing the ejection fraction of an abnormal patient condition ([0052] “very low ejection-fraction below 35-percent”),
Attia lacks
wherein the second data set further comprises a third class representing that the classification of the ejection fraction is indeterminable from the first data set, in particular from a specific heartbeat of the pre-acquired cardiac current curve data; and
generating a notification to a communication device of a health care provider based on the output of the machine learning algorithm
Lan teaches a system that detects brain states to determine if it is normal, abnormal, or unable to be determined (pg 2, ln 14-15). A classification model is used to classify the data into different categories which include normal state classification, abnormal state classification, and uncertain data that is in unable to be classified properly (pg 15, ln 786-798). Once the data is classified into a category it can be communicated with a data management platform (pg 18, ln 960-964).
It would be obvious to one of ordinary skill in the art at the time the instant invention was filed to modify the system of Attia by classifying data into normal, abnormal, or undeterminable and then communicating the result of the classification as taught by Lan, as classifying and communicating the classification of this data could be useful for other analyses and letting the healthcare provider know they may need to rerun tests to try and get results that are not undeterminable.
In re claim 3, the proposed combination, all mapping directed to Attia, yields
wherein the machine learning algorithm is a regression-type algorithm ([0053] “may be regression models, machine-learning models, or both”),
wherein the second data set is given by at least one numeric value, in particular a sequence of numeric values, representing the ejection fraction ([0052] “an absolute estimate of the patient’s ejection-fraction… a particular value”).
In re claim 6, the proposed combination, all mapping directed to Attia, yields
wherein if at least one value of the second data set representing the ejection fraction is outside a predetermined numeric range or is above or below a predetermined threshold value [0062],
in particular if the at least one value is outside limits set individually for a patient by a physician [0062] and/or
if the at least one value differs by a predetermined amount from previously transmitted values [0007],
a notification is sent to the communication device of the health care provider [0057].
In re claim 7, the proposed combination, all mapping directed to Attia, yields wherein if the machine learning algorithm classifies the ejection fraction of an abnormal patient condition of an abnormal patient condition, a notification is sent to the communication device of the health care provider [0058, 0062].
In re claim 8, the proposed combination, all mapping directed to Attia, yields wherein the at least one value of the second data set representing the ejection fraction is evaluated
by performing a trend analysis ([0054] “generate a prognosis of the patient’s estimated future survival rate”) of at least one further value of the second data set representing the ejection fraction ([0054] “based on the patient’s ejection-fraction characteristic”), wherein
if the trend analysis meets predetermined criteria of an abnormal patient condition ([0062] “the system determines whether the estimated ejection-fraction characteristic, and optionally additional factors, meet one or more screening criteria that are to guide a decision whether to further evaluation of the patient’s condition is warranted”),
a notification is sent to the communication device of the health care provider [0057].
In re claim 9, the proposed combination, all mapping directed to Attia, yields wherein a reference value of the ejection fraction is compared to the second data set ([0064] “estimated ejection-fraction characteristic is compared to the target ejection-fraction”) outputted by the machine learning algorithm representing the ejection fraction to calibrate the output of the machine learning algorithm ([0064] “output error is then back-propagated through the network using gradient descent to update the current weights/parameters of the neural network”).
In re claim 10, the proposed combination, all mapping directed to Attia, yields wherein based on the reference value [0054] of the ejection fraction, a most appropriate machine learning algorithm is selected from a library of machine learning algorithms ([0054] “system may select an appropriate one of the models”).
In re claim 12, the proposed combination, all mapping directed to Attia, yields wherein the cardiac current curve data is acquired by the implantable medical device [0006] at predetermined intervals and/or on request [0047], in particular as a wide-field ECG between electrodes and a housing of the implantable medical device [0006], and wherein the cardiac current curve data is transmitted to a central server (fig. 2) via a patient communication device or smartphone [0076].
In re claim 14, see above (In re claim 1) and the following limitations (all mapping directed to Attia): computer implemented method [0008] for providing a trained machine learning algorithm [0045] configured to determine an ejection fraction [0009] or a classification of the ejection fraction [0045], comprising the steps of:
receiving a first training data set comprising pre-acquired cardiac current curve data [0058], in particular one-channel cardiac current curve data [0058], captured by an implantable medical device [0006];
receiving a second training data set representing an ejection fraction [0019] or a classification of the ejection fraction [0020]; and
training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function [0106] for regression [0108]
In re claim 15, see above (In re claim 1).
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Attia et al. (US 2020/0397313) in view of Lan et al. (CN 110680282 B) in view of Bang et al. (US 2022/0192600).
In re claim 11, the proposed combination lacks
wherein the first data set further comprises a heart rate, a thorax impedance and/or a patient activity captured by an implantable medical device.
Bang teaches an implantable cardiac monitor that uses a machine learning algorithm to determine subtle changes in ejection [0071]. The implantable cardiac monitor also uses machine learning to detect changes in cardiac data including heart rate, patient activity, and impedance [0049].
It would be obvious to one of ordinary skill at the time the instant invention was filed to modify the system of the proposed combination with an implantable cardiac monitor that can also monitor different types of cardiac functions as taught by Bang, as it is known patients with cardiac disease may need to track how their heart is performing over time to help reduce hospitalizations.
In re claim 13, the proposed combination, all mapping directed to Attia, yields wherein the first data set comprises a first cardiac current curve recorded by the implantable medical device at a first time interval [0047] and
The proposed combination lacks
a second cardiac current curve recorded by the implantable medical device at a second time interval, in particular offset from the first time interval
Bang teaches an implantable cardiac monitor that can identify changes in ejection sounds through analyzing heartbeat data. The system can analyze heartbeat data over different time periods to detect changes using machine learning or trend analysis [0071].
It would be obvious to one of ordinary skill in the art to at the time the instant invention was filed to modify the system of the proposed combination with the ability to take heartbeat data at different time intervals and compare the two to determine the changes as taught by Bang, as analyzing and comparing heart rate data over time would allow the patient and clinician to determine the stability of the patient’s disease.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HALEY N. PRUITT whose telephone number is (571)272-1955. The examiner can normally be reached M-T, 7:30 AM -5 PM. F, 7:30-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at (571)270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HALEY N PRUITT/Examiner, Art Unit 3796
/DAVID HAMAOUI/SPE, Art Unit 3796
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