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 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.
Claims 1 and 2 are 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.
In re claim 1, the limitations “a variation of the ejection fraction” and “a classification of the variation of the ejection fraction” are unclear. It is unclear what variation means, as variation could mean the statistical distribution of the calculated ejection fractions, the difference between highest and lowest ejection fraction values for a subject based off of an ECG reading, or the difference in a subject’s ejection fraction over time. As it is unclear what the variation refers to in this regard, and due to the nature of the limitation following and/or, the variation of the ejection fraction or of the classification of the ejection fraction is seen as optional and not selected for examination.
In re claim 2, the limitation, “Computer implemented method of claim 1, wherein if the second data set represents the variation of the ejection fraction, the second data set is used to calculate an absolute value of the ejection fraction” is unclear. Specifically, the recited "if" is unclear regarding whether the above recited limitation is optional for examination purposes if the “variation of the ejection fraction” was not chosen In re claim 1.
Furthermore, the limitation “absolute value” is unclear as ejection fraction values must be positive percentages, so even if the recited "variation" is interpreted as the difference between two ejection fraction values, the difference must be a positive integer as well.
Due to the numerous rejections under 35 U.S.C. 112(b) in claim 2, there is a lack of clarity. Thus, the scope could not be determined for the application of the prior art.
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 1-15 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 1-15 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 and/or, a variation of the ejection fraction or, a classification of the ejection fraction and/or, a classification of the variation 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 and/or the variation of the ejection fraction from the pre-acquired cardiac current curve data or for classification of the ejection fraction and/or classification of a variation 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 and/or
the variation of the ejection fraction or
a classification of the ejection fraction and/or
a classification of the variation of the ejection fraction
by 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 and/or
a variation of the ejection fraction or
a classification of the ejection fraction and/or
a classification of the variation 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.
Additionally, the limitation “outputting a second data set” 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 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. As discussed with respect to Step 2A, Prong 2, the additional elements in the claim amount to no more than insignificant extra-solution activity and mere data gathering.
The same analysis applies here in Step 2B and does not provide an inventive concept.
Claims 2-4, 9, 11, and 13 depend on claim 1 and recite the same abstract ideas as claim 1 for which they depend. Furthermore, these claims only contain recitations that further limit the abstract idea.
Claims 6-8 depend on claim 1 and recite the same abstract ideas as claim 1, by referencing the limitations in claim 1, with the additional limitation of “a notification is sent to a communication device of a healthcare provider”. These claims are directed to displaying further information on the user interface, the further information regarded as a judicial exception as it is directed to a mental process of observation, evaluation, or judgement.
Claim 12 depends on claim 1 and recites the same abstract ideas as claim 1, with the additional limitation of “data is transmitted to a central server via a patient communication device or smartphone”. This limitation does not integrate the exception into a practical application of the exception because the data being transmitted from one device to another is insignificant extra-solution activity.
Thus, none of the claims 1-15 amount to significantly more than the abstract idea itself. Accordingly, claims 1-15 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 § 102
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.
(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.
Claims 1, 3, 4, 6-10, 12, 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Attia et al. (US 2020/0397313)
In re claim 1, Attia discloses a computer implemented method [0008] for determining
an ejection fraction [0009] and/or
a variation of the ejection fraction (optional) or
a classification of the ejection fraction [0052] and/or
a classification of the variation of the ejection fraction (optional),
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; and
outputting a second data set [0045] representing
the ejection fraction [0045] and/or
the variation of the ejection fraction (optional) or
a classification of the ejection fraction ([0020], Note: range relates to the classification of ejection fractions into different categories) and/or
a classification of the variation of the ejection fraction (optional)
by the machine learning algorithm [0045].
In re claim 2, wherein
if the second data set represents the variation of the ejection fraction,
the second data set is used to calculate an absolute value of the ejection fraction. (see above 112b, In re claim 2)
Note: Since the variation of the ejection fraction is an optional feature, this claim limitation is satisfied by the prior art (Attia et al.).
In re claim 3, 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 ([0052] “a particular value”), in particular a sequence of numeric values, representing the ejection fraction [0052] and/or the variation of the ejection fraction.
In re claim 4, 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 and/or the variation of the ejection fraction of a normal patient condition ([0052] “normal ejection-fraction greater than 50-percent”) and
a second class representing the ejection fraction and/or the variation of the ejection fraction of an abnormal patient condition ([0052] “very low ejection-fraction below 35-percent”).
In re claim 6, wherein
if at least one value of the second data set representing the ejection fraction and/or the variation of 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 a communication device .
In re claim 7, wherein if the machine learning algorithm classifies the ejection fraction of an abnormal patient condition and/or the variation of the ejection fraction of an abnormal patient condition,
a notification is sent to a communication device of a health care provider [0058, 0062].
In re claim 8, Attia discloses wherein the at least one value of the second data set representing the ejection fraction and/or the variation of 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”) and/or the variation of the ejection fraction, 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 a communication device of a health care provider [0057].
In re claim 9, wherein a reference value of the ejection fraction and/or the variation 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 and/or a variation of 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, wherein based on the reference value [0054] of the ejection fraction and/or the variation of the ejection fraction, a most appropriate machine learning library is selected from a library of machine learning algorithms ([0054] “system may select an appropriate one of the models”).
In re claim 12, 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:
receiving a first training data set comprising pre-acquired cardiac current curve data (fig 6: 602, “ECG predictive input”)
receiving a second training data set (fig 6: 602, “target EF value”)
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).
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 5, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Attia et al. (US 2020/0397313) in view of Bang et al. (US 2022/0192600).
In re claim 5, Attia lacks wherein
the second data set further comprises a third class representing that the classification of the ejection fraction and/or the classification of the variation 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.
Bang teaches an implantable cardiac device that can analyze ECG data and determine changes in ejection sounds over time and that can identify and filter ECG data out if it is an outlier or inapplicable [0109].
It would be obvious to one of ordinary skill at the time the instant invention was filed to modify the system of Attia with the processor that can determine and filter out inapplicable data as taught by Bang, as it would be helpful for the algorithm to determine data that is inapplicable so that the tests could be repeated or for more data to be collected for analysis.
In re claim 11, Attia 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 Attia with an implantable cardiac monitor that can also monitor different types of cardiac functions, such as heart rate, impedance, and patient activity as taught by Bang, as it is known patients with cardiac disease may need to track more than just ejection fraction to determine how their heart is performing over time to help reduce hospitalizations.
In re claim 13, Attia discloses wherein the first data set comprises a first cardiac current curve recorded by the implantable medical device at a first time interval and
Attia 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, and wherein the machine learning algorithm is configured to determine the variation in the ejection fraction from the variation between the first cardiac current curve and the second cardiac current curve.
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 Attia 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sullivan et al. (US 2016/0135706) teaches a medical premonitory event estimation that can classify subject’s ejection fraction data using machine learning models [0369]. The machine learning models can include classification and regression trees [0370].
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