DETAILED ACTION
This Office Action is in response to the communication dated 19 June 2026 concerning Application No. 18/697,791 filed on 02 April 2024.
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 .
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 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.
Status of Claims
Claims 1-3 and 6-15 are pending and currently under consideration for patentability; claims 1 and 15 have been amended; claims 4 and 5 have been cancelled.
Response to Arguments
Applicant’s arguments dated 19 June 2026 have been fully considered, but they are not persuasive or moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant has amended independent claims 1 and 15 to incorporate limitations similar to those appearing in claims 4 and 5, which have been cancelled. The Examiner has addressed the amended limitations in the updated text of the rejection below.
Regarding the amended limitations, Applicant argues that “Albert discusses sending an alert when a classification of a QT interval is over a threshold,” which “does not remotely relate to a third class representing that the corrected QT-interval and/or the classification of the corrected QT-interval is indeterminable from the first data set” (Arguments, p. 9). The Examiner respectfully directs Applicant to Albert’s disclosure that “if a subject has been measured with a normal QT interval length, then on a subsequent measurement, the subject is measured with a longer QT interval length, that may indicate a need for further monitoring of the subject” (Albert, [0042]). By stating “a need for further monitoring of the subject,” Albert describes that the corrected QT interval and/or the classification of the corrected QT interval is indeterminable. Stated another way, because the QT interval is indeterminable, there is a need for further monitoring of the subject. Therefore, the Examiner respectfully maintains that Albert describes the use of a third class as recited in the claims.
Regarding claim 14, Applicant argues that paragraphs [0033] - [0035] “are devoid of any type of loss function or regression for machine learning” (Arguments, p. 10). The Examiner respectfully disagrees and submits that Albert’s discussion of quantifying the differences between a predicted value and an actual value, specifically the difference between estimated and actual QT intervals, in order to guide the optimization of the method, is a form of a loss function for machine learning.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 2, 6-10, and 13-15 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Albert et al. (US 2021/0121117 A1).
Regarding claims 1 and 15, Albert describes a computer implemented method for determining a QT-interval ([0012]: “devices and techniques for measuring the length of QT intervals in individuals…one or more machine learning models can be used to measure a QT interval length from an EKG signal”), comprising the steps of
receiving a first data set comprising pre-acquired cardiac current curve data captured by an implantable medical device ([0015]: “using a training set of EKGs from a number of subjects”; [0039]: EKG data 120)
applying a machine learning algorithm to the pre-acquired cardiac current curve data ([0039]: “EKG data 120 may then be input to machine learning model 125”)
outputting a second data set representing the QT-interval by the machine learning algorithm ([0035]: “the machine learning model 125 can be trained to provide an output of a QT interval length based on the training”)
wherein the machine learning algorithm is a classification-type algorithm, wherein the second data set comprises a first class representing a corrected QT-interval of a normal patient condition, a second class representing a corrected QT-interval of an abnormal patient condition ([0037], [0042], QTc under or over a threshold), and a third class representing that the corrected QT-interval and/or the classification of the corrected QT-interval is indeterminable from the first data set, in particular from a specific heartbeat of the pre-acquired cardiac current curve data ([0042]: “if a subject has been measured with a normal QT interval length, then on a subsequent measurement, the subject is measured with a longer QT interval length, that may indicate a need for further monitoring of the subject”)
Regarding claim 2, Albert describes wherein, if the second data set represents the determined QT-interval, the second data set is used to calculate the corrected QT-interval ([0042]).
Regarding claim 6, Albert describes wherein if at least one value of the second data set representing the corrected QT-interval is outside a predetermined numeric range, a notification is sent to a communication device of a health care provider ([0042]).
Regarding claim 7, Albert describes wherein the machine learning algorithm is further configured to output a third data set representing a heart rate, wherein the heart rate is determined by detecting an RR interval of a QRS complex of the pre-acquired cardiac current curve data ([0017]).
Regarding claim 8, Albert describes wherein if the machine learning algorithm outputs the second data set representing the QT-interval, the corrected QT-interval is calculated based on the QT-interval and the heart rate ([0042]).
Regarding claim 9, Albert describes wherein the machine learning algorithm determines the QT-interval by detecting a Q-wave and a T-wave and by determining a spacing between the Q-wave and the T-wave of the QRS complex of the pre-acquired cardiac current curve data ([0023]).
Regarding claim 10, Albert describes wherein a reference value of the QT-interval obtained by a twelve-channel ECG is compared to the second data set outputted by the machine learning algorithm representing the QT-interval to calibrate the output of the machine learning algorithm ([0036]).
Regarding claim 13, Albert describes wherein the cardiac current curve data is acquired by the implantable medical device at predetermined intervals ([0017], [0021]) and wherein the cardiac current curve data is transmitted to a central server via a patient communication device or smartphone ([0043]).
Regarding claim 14, Albert describes a computer implemented method for providing a trained machine learning algorithm configured to determine a QT-interval ([0012]), comprising the steps of:
receiving a first training data set comprising pre-acquired cardiac current curve data captured by an implantable medical device ([0015])
receiving a second training data set representing a QT-interval ([0015] - [0016])
training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of the QT-interval from the pre-acquired cardiac current curve data ([0033], [0035])
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Albert in view of Shusterman (US 2011/0004110 A1).
Regarding claim 3, Albert describes the computer implemented method of claim 1, including wherein the second data set is given by at least one numeric value representing the QT-interval ([0035]), but Albert but does not explicitly disclose wherein the machine learning algorithm is a regression-type algorithm. However, Shusterman also describes a computer implemented method for determining a subject’s QT interval ([0010]), including the use of a regression-type machine learning algorithm ([0103]). As Shusterman is also directed towards determining a subject’s QT interval and is in a similar field of endeavor, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to incorporate a regression-type algorithm similar to that described by Shusterman when using the method described by Albert, as doing so would be a matter of substituting one type of machine learning algorithm for another in order to obtain the predictable result of an accurate analysis of the subject’s cardiac mechanics, as described by Shusterman ([0077], [0103]).
Regarding claim 12, Shusterman describes wherein the first data set further comprises a thorax impedance captured by an implantable medical device ([0036], [0076]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Albert in view of Szabados et al. (US 2021/0244339 A1).
Regarding claim 11, Albert describes the computer implemented method of claim 10, including obtaining a reference value of the QT-interval by the twelve-channel ECG ([0036]), but Albert does not explicitly disclose wherein a most appropriate machine learning algorithm is selected from a library of machine learning algorithms based on the reference value. However, Szabados also describes a computer implemented method for determining a QT interval ([0103] - [0104]), including wherein a most appropriate machine learning algorithm is selected based on a reference value ([0039] - [0040], the hardware processor is configured to select the neural network from a plurality of neural networks based on a characteristic, which may be a characteristic of the wearable device; [0043] describes that the characteristics may be the user’s data). As Szabados is also directed towards measuring a QT interval and is in a similar field of endeavor, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to select the most appropriate machine learning algorithm, in a manner similar to that described by Szabados, when using the method described by Albert, as doing so advantageously allows the resulting method to derive a more accurate model of the user’s physiology.
Statement on Communication via Internet
Communications via Internet e-mail are at the discretion of the applicant. Without a written authorization by applicant in place, the USPTO will not respond via Internet e-mail to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. 122. Where a written authorization is given by the applicant, communications via Internet e-mail, other than those under 35 U.S.C. 132 or which otherwise require a signature, may be used. USPTO employees are NOT permitted to initiate communications with applicants via Internet e-mail unless there is a written authorization of record in the patent application by the applicant. The following is a sample authorization form which may be used by applicant:
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please refer to MPEP 502.03 for guidance on Communications via Internet.
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Ankit D. Tejani, whose telephone number is 571-272-5140. The Examiner may normally be reached on Monday through Friday, 8:30AM through 5:00PM EST. 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, Carl Layno, can be reached by telephone at 571-272-4949. 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 at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (in USA or Canada) or 571-272-1000.
/Ankit D Tejani/
Primary Examiner, Art Unit 3792
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