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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/14/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 13 and 14 are duplicate claims. Applicant is advised that should claim 13 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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) without significantly more. Claims 1-20 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: Claim 1 is drawn to a system. Claim 12 is drawn to a method. Claim 20 is drawn to a device.
Step 2A – Prong 1:
Claim 1 is drawn to an abstract idea, that under its broadest reasonable interpretation, covers a mental process but for the recitation of pre-solution activity of data gathering and generic computer components for collecting and processing data. In particular, claim 1 recites the following limitations:
A medical device system comprising:
a medical device comprising one or more electrodes and configured to generate electrical cardiac data based on a cardiac signal sensed from a patient via the one or more electrodes;
a memory configured to store a machine learning model and a plurality of sets of training data; and
processing circuitry in communication with the memory,
wherein the processing circuitry is configured to:
apply the machine learning model to the electrical cardiac data to determine a value of a metric of left ventricular (LV) dysfunction (mental process),
wherein the machine learning model is trained based on the plurality of sets of training data,
wherein each set of training data of the plurality of sets of training data includes
a set of training electrical cardiac data and
information indicating one or more values of the metric of LV dysfunction corresponding to the set of training electrical cardiac data; and
output the determined value of the metric of LV dysfunction to a computing device (mental process).
In re claim 12, see above (In re claim 1). Substantially, the same reasoning applies.
In re claim 20, see above (In re claim 1). Substantially, the same reasoning applies.
These limitations of claim 1 are drawn to an abstract idea because they are processes that, under their broadest reasonable interpretation, are steps merely comprised of mental processes.
Step 2A – Prong Two:
Claim 1 recites the following emphasized (indicated in bold) additional elements that are beyond the judicial exception:
A medical device system comprising:
a medical device comprising one or more electrodes and configured to generate electrical cardiac data based on a cardiac signal sensed from a patient via the one or more electrodes;
a memory configured to store a machine learning model and a plurality of sets of training data; and
processing circuitry in communication with the memory,
wherein the processing circuitry is configured to:
apply the machine learning model to the electrical cardiac data to determine a value of a metric of left ventricular (LV) dysfunction (mental process and/or mathematical concepts),
wherein the machine learning model is trained based on the plurality of sets of training data,
wherein each set of training data of the plurality of sets of training data includes
a set of training electrical cardiac data and
information indicating one or more values of the metric of LV dysfunction corresponding to the set of training electrical cardiac data; and
output the determined value of the metric of LV dysfunction to a computing device.
The additional elements do not integrate the exception into a practical application of the exception because the elements are directed to insignificant extra-solution activity. The medical device and one or more electrodes amount to no more than pre-solution activity of data gathering to receive electrical cardiac data. The training data and information indicating one or more values of the metric of LV dysfunction are insignificant extra-solution activity. The processing circuitry, machine learning model, and memory are computer elements that carry out abstract steps described in claim 1 (see 2106.05(g) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Further, the judicial exception does not integrate the claim as a whole into a practical application because the claimed invention does not improve another technology or technical field. The alleged improvement made by the claimed invention as argued by the application above sets forth the improvement in a conclusory manner and the claim does not include the components or steps of the invention that the improvement described.
In re claim 12, see above (In re claim 1). Substantially, the same reasoning applies.
In re claim 20, see above (In re claim 1). Substantially, the same reasoning applies. Claim 20 has an additional limitation of “A non-transitory computer-readable storage medium comprising program instructions”, subject to the same analysis of the processing circuitry discussed above In re claim 1.
Step 2B:
Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well understood, routine, conventional activity. The medical device and one or more electrodes amount to no more than pre-solution activity of data gathering to receive electrical cardiac data.
Regarding the limitation “a medical device comprising one or more electrodes and configured to generate electrical cardiac data based on a cardiac signal sensed from a patient via the one or more electrodes”, see Zhou (US 20040064062) which discloses an implantable medical device [0038] with electrodes [0028] configured to detect and predict arrythmias [0002] using left ventricular dysfunction as a parameter [0006]. Thus, the limitations directed to the medical device and electrodes are well-understood, routine, and conventional, as evidenced by the reference above.
Moreover, implementing an abstract idea on a generic computer, does not add significantly more. The processing circuitry, machine learning model, and memory are computer elements that carry out abstract steps described in claim 1. Reconsidering the claim limitations individually and as a combination, the claims fail to meet the requirements for eligibility under 35 U.S.C. 101. All uses of the recited abstract idea require the pre- solution data gathering.
In re claim 12, see above (In re claim 1). Substantially, the same reasoning applies.
In re claim 20, see above (In re claim 1). Substantially, the same reasoning applies. Claim 20 has an additional limitation of “A non-transitory computer-readable storage medium comprising program instructions”, subject to the same analysis of the processing circuitry discussed above In re claim 1.
In re claim 3, see above 35 U.S.C. In re claim 1. Claim 3 recites the same abstract idea as its parent claim, claim 1, with the additional limitation “wherein the medical device further comprises an accelerometer” containing an additional element indicated in bold. Regarding the limitation, see Hansen (US 20070066998) which discloses a cardiac sensor/stimulator (abstract) with an accelerometer that determines posture [0052] as a parameter which may affect an amount of fluid within a subject's thoracic region or a location of one or more tissue electrode in heart, which may change the evaluation [0077]. Thus, the limitations directed to the medical device and electrodes are well-understood, routine, and conventional, as evidenced by the reference above. Reconsidering the claim limitations individually and as a combination, the claims fail to meet the requirements for eligibility under 35 U.S.C. 101.
In re claim 11, see above (In re claim 3). Substantially, the same reasoning applies.
Claims 2, 4-11, 15-19 recite the same abstract idea as their respective parent claims. Furthermore, these claims only contain recitations that further limit the abstract idea.
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 1-2, 5-12, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Attia (US 20200397313).
In re claim 1, Attia discloses a medical device system (Fig. 1: 100) comprising:
a medical device (104 and 108; [0006]: “electrodes affixed to implanted devices or any combination thereof”)
comprising one or more electrodes (104) and
configured to generate electrical cardiac data based on a cardiac signal sensed from a patient via the one or more electrodes [0047-0048];
a memory (Fig. 12: 1204; [0077, 0091]) configured to store
a machine learning model (118; [0053]: “the ejection-fraction prediction model(s) 118 may be regression models, machine-learning models, or both”) and
a plurality of sets of training data (122; [0057, 0064]); and
processing circuitry (108, 1202) in communication with the memory [0008, 0077],
wherein the processing circuitry is configured to:
apply the machine learning model to the electrical cardiac data to determine a value of a metric of left ventricular (LV) dysfunction [0050-0054],
wherein the machine learning model is trained based on the plurality of sets of training data ([0057, 0064]),
wherein each set of training data of the plurality of sets of training data includes
a set of training electrical cardiac data [0064] and
information indicating one or more values of the metric of LV dysfunction corresponding to the set of training electrical cardiac data ([0064]: “Each pair includes an ECG predictive input that characterizes a particular patient's ECG and a target ejection-fraction characteristic for the patient”); and
output the determined value of the metric of LV dysfunction to a computing device (Fig. 2: 216; [0057-0058]: “The notifications manager 124 is configured to provide an estimated ejection-fraction characteristic, estimated survival rate, or both, for output”).
In re claim 2, Attia discloses:
wherein the electrical cardiac data comprises a plurality of sets of electrical cardiac data ([0055, 0058-0059, 0073]: the input of one or more cardiac cycles, i.e. a set of electrical cardiac data, is determined from continuous ECG collection, i.e. a plurality of sets of electrical cardiac data);
wherein to apply the machine learning model to the electrical cardiac data to determine the value of the metric of LV dysfunction, the processing circuitry is configured to:
apply the machine learning model to a set of electrical cardiac data of the plurality of sets of electrical cardiac data to determine the value of the metric of LV dysfunction [0051-0054] which corresponds to the set of electrical cardiac data of the plurality of sets of electrical cardiac data [0006-0007, 0051-0054] and
wherein the processing circuitry is further configured to:
apply the machine learning model to each other set of electrical cardiac data of the plurality of sets of electrical cardiac data to determine a value of the metric of LV dysfunction corresponding to each other set of electrical cardiac data of the plurality of sets of electrical cardiac data; and
([0059, 0073]: the model determines current ejection fraction from continuous ECG data; [0007]: increased frequency of screenings; [0061]: “a personalized model”; note: Attia discloses a personalized machine learning model, continuous ECG monitoring, and access to frequent screenings to determine ejection fraction via the medical device system, thus applying the model to a plurality of sets of electrical cardiac data)
output the determined value of the metric of LV dysfunction corresponding to each other set of electrical cardiac data of the plurality of sets of electrical cardiac data to the computing device ([0006-0007, 0051-0054, 0059, 0073]: the model outputs a current ejection fraction based on a set of electrical cardiac data).
In re claim 5, Attia discloses:
wherein the processing circuitry is further configured to train the machine learning model based on the plurality of sets of training data (Fig. 1: training subsystem 122 is part of ECG Processing System 110; [0057, 0064]), and
wherein by training the machine learning model based on the plurality of sets of training data, the processing circuitry is configured to cause the machine learning model to recognize one or more patterns corresponding to the metric of LV dysfunction and one or more characteristics of electrical cardiac data (EF Prediction Model(s) 118 is a part of ECG Processing System 110; [0051-0052]).
In re claim 6, Attia discloses wherein the processing circuitry is further configured to label each set of training data of the plurality of sets of training data (Fig. 6: 604 to 610; [0064]: “the estimated ejection-fraction characteristic is compared to the target ejection-fraction characteristic to determine an output error”).
In re claim 7, Attia discloses wherein to label each set of training data of the plurality of sets of training data, the processing circuitry is configured to:
identify, for each set of training data of the plurality of sets of training data, one or more characteristics of the set of training electrical cardiac data of the set of training data ([0054-0056]: “Morphological features are parameters that characterize the shape of an ECG waveform or a portion of the ECG waveform”) and
label the set of training electrical cardiac data of each set of training data of the plurality of sets of training data with the one or more characteristics of the set of training electrical cardiac data ([0054-0056]: “a feature extractor 126 that analyzes ECG data and determines values of any applicable morphological features to include the predictive input that will be processed by an ejection-fraction prediction model”).
In re claim 8, Attia discloses wherein the one or more characteristics of the set of training electrical cardiac data include any one or more of: one or more R-waves, one or more P-waves, one or more T-waves, a heart rate corresponding to the set of training electrical cardiac data, a heart rate variability corresponding to set of training electrical cardiac data, and an arrythmia indicated by the set of training electrical cardiac data ([0056]: “such as the P-wave, QRS-complex, or T-wave”).
In re claim 9, Attia discloses wherein to label each set of training data of the plurality of sets of training data, the processing circuitry is configured to:
identify, in the information indicating one or more values of the metric of LV dysfunction of the set of training data, a time corresponding to each value of the one or more values of the metric of LV dysfunction ([0055-0056]: "a predictive input contains a time-series of values”); and
associate the time corresponding to each value of the one or more values of the metric of LV dysfunction with a time of the set of training electrical cardiac data [0055-0056].
In re claim 10, Attia discloses wherein to apply the machine learning model to the electrical cardiac data to determine the value of LV dysfunction, the processing circuitry is configured to apply the machine learning model to the electrical cardiac data to determine a confidence that the value of LV dysfunction is lower than a threshold value of LV dysfunction [0007, 0074].
In re claim 11, Attia discloses wherein the metric of LV dysfunction comprises ejection fraction.
In re claim 12, see above 35 U.S.C. 102 rejection, In re claim 1.
In re claim 16, see above 35 U.S.C. 102 rejection, In re claim 5.
In re claim 17, see above 35 U.S.C. 102 rejection, In re claim 6.
In re claim 18, see above 35 U.S.C. 102 rejection, In re claim 7.
In re claim 19, see above 35 U.S.C. 102 rejection, In re claim 9.
In re claim 20, see above 35 U.S.C. 102 rejection, In re claim 1. In addition, Attia discloses a non-transitory computer-readable storage medium [0008] comprising program instructions that, when executed by processing circuitry of a medical device system comprising one or more electrodes and configured to generate electrical cardiac data based on a cardiac signal sensed from a patient via the one or more electrodes, cause the processing circuitry to [0008; 0085].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Attia (US 20200397313) in view of Dawoud (EP 3906841).
In re claim 3, Attia discloses:
wherein the processing circuitry is further configured to:
determine whether to output an alert based on the determined value of the metric of LV dysfunction ([0045]: “output an indication of the estimated ejection fraction of a mammal”; [0057]: ”the notifications manager 124 is configured to provide an estimated ejection-fraction characteristic, estimated survival rate, or both, for output to one or more users”).
Attia lacks:
wherein the medical device further comprises an accelerometer,
wherein the medical device is further configured to generate motion data based on a motion signal sensed by the accelerometer, and
wherein the processing circuitry is further configured to:
determine, based on the motion data, a motion value indicating an activity level of the patient,
wherein the motion value corresponds to the determined value of the metric of LV dysfunction; and
determine whether to output an alert based on the motion value and the determined value of the metric of LV dysfunction.
Dawoud discloses a system for predicting heart failure status [0064, 0172] that, like the medical device system disclosed by Attia, utilizes machine learning algorithms [0190] to determine a metric indicative of heart failure ([0064]; i.e. LV dysfunction; Instant App.: [0005]: “LV dysfunction may be related to heart failure (HF)”) and collects cardiac activity signals [0199]. In addition, Dawoud discloses an accelerometer (abstract) which generates accelerometer data (Fig. 2: 202; i.e. motion data) and determining a risk-weighted composite index based on the accelerometer data (Fig. 2: 216; Fig. 13: 1322; [0081]; i.e. motion value) which is used to diagnose heart failure (Fig. 2: 224; [0172]; correlated to heart failure/LV dysfunction) and determining to generate a treatment notification based on the risk-weighted composite index exceeding a threshold value (Fig. 13: 1322 and 1324; [0190-0192]).
The proposed combination would yield wherein the medical device further comprises an accelerometer, wherein the medical device is further configured to generate motion data based on a motion signal sensed by the accelerometer, and wherein the processing circuitry is further configured to: determine, based on the motion data, a motion value indicating an activity level of the patient, wherein the motion value corresponds to the determined value of the metric of LV dysfunction; and determine whether to output an alert based on the motion value and the determined value of the metric of LV dysfunction and wherein to determine whether to output the alert, the processing circuitry is configured to: determine whether the value of the metric of LV dysfunction is lower than a threshold metric of LV dysfunction; determine whether the motion value is greater than a threshold motion value; and determine whether to output the alert based on whether the value of the metric of LV dysfunction is lower than the threshold metric of LV dysfunction and whether the motion value is greater than the threshold motion value.
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the medical device system disclosed by Attia by providing an accelerometer which generates motion data which is used to determine a motion value corresponding to the determine value of the metric of LV dysfunction and utilize the processing circuitry to determine whether to output an alert based on the motion value as disclosed by Dawoud because motion data, like patient activity data, sleep and active time, and travel-related parameters, can indicate that a patient is more likely to experience a pathologic episode such as heart failure, stroke, syncope, arrythmia, heart attack, asystole, Brady event, neurological episodes, ventricular fibrillation (VF), ventricular tachycardia (VT), a diabetic seizure, and epileptic seizure, or any other type of seizure, episodes that may result from substantial reduction or change in pulmonary arterial pressure and the like (Dawoud: [0077-0078]).
In re claim 4, Attia discloses wherein to determine whether to output the alert, the processing circuitry is configured to:
determine whether the value of the metric of LV dysfunction is lower than a threshold metric of LV dysfunction (Fig. 5: 504; [0007, 0058, 0062-0063, 0067]: “If the estimated ejection fraction of the patient is below the threshold, a follow-on procedure for further evaluation can be performed on the patient”);
determine whether to output the alert based on
whether the value of the metric of LV dysfunction is lower than the threshold metric of LV dysfunction ([0057-0058]: “ejection-fraction categories defined by specified threshold ejection-fraction values”; [0062-0063, 0067]: “predicting a subject's survival rate from cardiac conditions, such as low or very low ejection fraction, from ECG data for the subject” and “The system can then provide the survival rate estimation for output”).
Attia lacks:
determine whether the motion value is greater than a threshold motion value; and
determine whether to output the alert based on
whether the motion value is greater than the threshold motion value.
Regarding the limitations “determine whether the motion value is greater than a threshold motion value; and determine whether to output the alert based on whether the motion value is greater than the threshold motion value.” See above the proposed combination in 35 U.S.C. 103 rejection, In re claim 3.
In re claim 13, see above 35 U.S.C. 102 rejection, In re claim 3.
In re claim 14, see above 35 U.S.C. 102 rejection, In re claim 3.
In re claim 15, see above 35 U.S.C. 102 rejection, In re claim 4.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Peterson (US 20210059540) discloses a medical device system comprising a medical device with one or more electrodes, a memory comprising a machine learning system that determines a value of a metric of left ventricular dysfunction based on electrical cardiac data which is applied by processing circuitry to electrical cardiac data.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HALLE M WELCH whose telephone number is (571)272-0168. The examiner can normally be reached Mon-Fri, 8:30 am to 5:00 pm..
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, David E 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HALLE MARGARET WELCH/Examiner, Art Unit 3796
/DAVID HAMAOUI/SPE, Art Unit 3796
Prosecution Insights