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 .
DETAILED ACTION
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-26 of U.S. Patent No. 11,813,065. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are directed to obvious variations of an arrhythmia monitoring system comprising a plurality of processors, at least one ECG channel, and a remote or external computer system in combination with the features as claimed in claims 1-26 of U.S. Patent No. 11,813,065.
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.
Claims 1-13 and 17-20 are rejected under 35 U.S.C. 103 as being obvious over Whiting et al (US 2018/0235537).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claim 1, Whiting discloses an arrhythmia monitoring system, comprising:
an external heart monitoring device 100 for a patient comprising:
a plurality of electrocardiogram (ECG) electrodes 112 configured to sense surface ECG activity of the patient;
ECG processing circuitry 120 configured to process the surface ECG activity of the patient to provide at least one ECG signal for the patient on at least one ECG channel; and
at least one first processor 218 operatively connected to the at least one ECG channel, the at least one first processor configured to:
receive the at least one ECG signal received via the at least one ECG channel, and
transmit the at least one ECG signal, see paragraph [0067].
Whiting teaches implementing machine learning tools such as a machine learning classifier trained on a large population, and using classification based machine learning tools, including neural networks. After training, the machine learning classifier can be validated and a specificity value for the machine learning classifier can be determined, see paragraph [0133].
This machine learning tool is readily adaptable to the external heart monitoring device 100 for patient monitoring and treatment, and implementing the tool is without undue experimentation. Based on this teaching in Whiting, one of ordinary skill in the art would have found it obvious to provide a gateway device, to implement a neural network comprising a non-transitory computer-readable medium comprising a rhythm change classifier, the rhythm change classifier comprising at least one neural network trained based on a historical collection of a plurality of ECG signal portions with known rhythm change information; and
at least one second processor 232 operatively connected to the non-transitory computer-readable medium, the at least one second processor configured to:
receive the at least one ECG signal from the external heart monitoring device 100,
detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the at least one ECG signal, the time data comprising at least one of a start time, a time interval, or any combination thereof, see paragraph [0042],
determine based on the detected time data at least one ECG signal portion associated with the detected time data corresponding to the predetermined rhythm change in the at least one ECG signal, see paragraph [0042], and
transmit the at least one determined ECG signal portion to a remote computer system, see paragraph [0090].
Regarding claim 2, Whiting discloses the at least one determined ECG signal portion comprises a plurality of determined ECG signal portions, wherein the remote computer system is in communication with the gateway device, the remote computer system configured to: receive the plurality of determined ECG signal portions, see paragraph [0137], from the external heart monitoring device, and analyze each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify a respective class for each respective determined ECG signal portion, wherein the class for at least two respective determined ECG signal portions comprises a first class, see paragraph [0037].
Regarding claim 3, Whiting discloses the remote computer system is further configured to transmit at least one message associated with the at least two respective determined ECG signal portions to a computing device associated with a technician, see paragraph [0036].
Regarding claim 4, Whiting discloses the computing device associated with the technician is configured to display a graphical user interface for batch review of the at least two respective determined ECG signal portions of the first class, see paragraph [0037].
Regarding claim 5, Whiting teaches the medical device classifies an event into one of a set of various zones, see paragraph [0037]. One of ordinary skill in the art would have found it obvious to analyze each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify the respective class for each respective determined ECG signal portion comprises bucketing the plurality of determined ECG signal portions into a plurality of buckets because buckets provide an equivalent arrangement as the zones wherein the first class comprises a first bucket of the plurality of buckets.
Regarding claim 6, Whiting discloses zoning i.e., bucketing the plurality of determined ECG signal portions into the plurality of buckets comprises grouping the plurality of determined ECG signal portions based on at least one of an output of a neural network, a similarity of features of the plurality of determined ECG signal portions, a similarity of vector representations of the plurality of determined ECG signal portions, or any combination thereof, see paragraph [0037].
Regarding claim 7, Whiting discloses the external heart monitoring device comprises a wearable patch, see paragraph [0065].
Regarding claim 8, Whiting teaches the external heart monitoring device comprises a wearable defibrillator, see paragraph [0062].
Regarding claim 9, Whiting renders obvious the remote computer system is in communication with the gateway device, the remote computer system configured to: receive the at least one determined ECG signal portion from the external heart monitoring device. One of ordinary skill in the art would have found it obvious to analyze the at least one determined ECG signal portion to classify a type of arrhythmia for the rhythm change in the at least one ECG signal because cardiac arrhythmias vary as to the risk associated with occurrence, see paragraph [0041].
Regarding claim 10, Whiting teaches using orthogonal axes for correlating and detecting heart sounds, see paragraph [0080]. One of ordinary skill in the art would have found it obvious to apply the same teaching to ECG channels because a first ECG channel and a second ECG channel are ECG analogs to the heart sound channels, wherein the at least one ECG signal comprises at least a first ECG signal associated with the first ECG channel and a second ECG signal associated with the second ECG channel. Correlating the first and second channels wherein the first respective ECG signal is orthogonal to the second respective ECG signal provides descriptive information for cardiac arrhythmia classifications and diagnosis.
Regarding claim 11, Whiting discloses: at least one sensor 224 and associated sensor circuitry 212 configured to sense non-ECG biometric data of the patient, wherein the at least one second processor is further configured to detect with the rhythm change classifier the predetermined rhythm change based on the at least one ECG signal and the non-ECG biometric data of the patient.
Regarding claim 12, Whiting discloses the at least one sensor 224 comprises a heart sound detector and wherein the non-ECG biometric data comprises heart sound data.
Regarding claim 13, Whiting teaches detecting a rhythm change using machine learning tools to model patterns based upon historical data and to classify future conditions based upon the historic models. One of ordinary skill in the art would have found it obvious to detect the predetermined rhythm change based on at least one of: at least one baseline ECG signal portion of the patient; at least one reference vector of the patient; at least one calibration measurement of the patient, the at least one calibration measurement based on at least one second ECG signal from second surface ECG activity sensed by a second plurality of ECG electrodes, the second plurality of ECG electrodes independent of the plurality of ECG electrodes of the external heart monitoring device; or at least one previous ECG signal portion because these criteria are used in the machine learning tools, see paragraph [0133].
Regarding claim 17, Whiting discloses an arrhythmia monitoring system, comprising:
an external heart monitoring device 100 for a patient comprising:
a plurality of electrocardiogram (ECG) electrodes 112 configured to sense surface ECG activity of the patient;
ECG processing circuitry 120 configured to process the surface ECG activity of the patient to provide at least one ECG signal for the patient on at least one ECG channel;
Whiting teaches implementing machine learning tools such as a machine learning classifier trained on a large population, and using classification based machine learning tools, including neural networks. After training, the machine learning classifier can be validated and a specificity value for the machine learning classifier can be determined, see paragraph [0133].
This machine learning tool is readily adaptable to the external heart monitoring device 100 for patient monitoring and treatment, and implementing the tool is without undue experimentation. Based on this teaching in Whiting, one of ordinary skill in the art would have found it obvious to implement a neural network comprising a non-transitory computer-readable medium comprising a rhythm change classifier, the rhythm change classifier comprising at least one neural network trained based on a historical collection of a plurality of ECG signal portions with known rhythm change information; and
at least one processor 218 operatively connected to the at least one ECG channel and the non-transitory computer-readable medium, the at least one processor configured to:
receive the at least one ECG signal received via the at least one ECG channel,
detect, with the rhythm change classifier, time data corresponding to a predetermined rhythm change in the at least one ECG signal, the time data comprising at least one of a start time, a time interval, or any combination thereof, see paragraph [0042],
determine, based on the detected time data, at least one ECG signal portion associated with the detected time data corresponding to the predetermined rhythm change in the at least one ECG signal, see paragraph [0042], and
transmit the at least one determined ECG signal portion, see paragraph [0090]; and a remote computer system 232 in communication with the external heart monitoring device, the remote computer system configured to:
receive the at least one determined ECG signal portion from the external heart monitoring device 100, and
analyze each respective determined ECG signal portion of the at least one determined ECG signal portion to classify a respective class for each respective determined ECG signal portion, see paragraph [0092].
Regarding claim 18, Whiting discloses the at least one determined ECG signal portion comprises a plurality of determined ECG signal portions, see paragraph [0137], and wherein the class for at least two respective determined ECG signal portions comprises a first class, see paragraph [0037].
Regarding claim 19, Whiting discloses the remote computer system is further configured to transmit at least one message associated with the at least two respective determined ECG signal portions to a computing device associated with a technician, see paragraph [0036], and wherein the computing device associated with the technician is configured to display a graphical user interface for batch review of the at least two respective determined ECG signal portions of the first class, see paragraph [0037].
20. The arrhythmia monitoring system of claim 18, wherein analyzing each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify the respective class for each respective determined ECG signal portion comprises bucketing the plurality of determined ECG signal portions into a plurality of buckets, wherein the first class comprises a first bucket of the plurality of buckets, and wherein bucketing the plurality of determined ECG signal portions into the plurality of buckets comprises grouping the plurality of determined ECG signal portions based on at least one of an output of a neural network, a similarity of features of the plurality of determined ECG signal portions, a similarity of vector representations of the plurality of determined ECG signal portions, or any combination thereof.
Claims 14-16 are rejected under 35 U.S.C. 103 as being obvious over Whiting et al (US 2018/0235537) in view of Tran (US 2020/0405148).
Regarding claim 14, Whiting meets all of the claimed features except for Siamese branches.
Tran teaches using a Siamese multi-scale feature extraction module to achieve a promising performance where multiscale convolutions along with skip connections are used to extract more useful common features from a multi-focus image pair, see paragraph [0047].
One of ordinary skill in the art would have found it obvious and desirable to combine the Siamese multi-scale feature extraction of Tran with the ECG channels of Whiting to extract features from the at least one ECG channel comprising a plurality of ECG channels, wherein the at least one ECG signal comprises at least one respective ECG signal associated with each respective ECG channel of the plurality of ECG channels, wherein the at least one neural network comprises a plurality of Siamese branches, each respective Siamese branch of the plurality of Siamese branches associated with a respective ECG channel of the plurality of ECG channels, and wherein the at least one neural network further comprises at least one further layer connected to the plurality of Siamese branches, see paragraphs [0133], [0134] and [0135].
Regarding claim 15, Tran teaches parallel training is advantageous to converge a model faster, see paragraph [0034], one of ordinary skill in the art would have found it obvious to configure each Siamese branch of the plurality of Siamese branches to comprises a plurality of convolutional layers, wherein dimensions of each of the plurality of convolutional layers of each respective Siamese branch are the same as the dimensions of each of the plurality of convolutional layers of each other Siamese branch to improve the convergence of the neural network model.
Regarding claim 16, Whiting teaches using orthogonal axes for correlating and detecting heart sounds, see paragraph [0080]. One of ordinary skill in the art would have found it obvious to apply the same teaching to ECG channels because a first ECG channel and a second ECG channel are ECG analogs to the heart sound channels, wherein the at least one ECG signal comprises at least a first ECG signal associated with the first ECG channel and a second ECG signal associated with the second ECG channel. Correlating the first and second channels wherein the first respective ECG signal is orthogonal to the second respective ECG signal provides descriptive information for cardiac arrhythmia classifications and diagnosis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Grouchy et al (US 2019/0200893) Gupta et al (2019/ 0214137) disclose related machine learning systems for cardiac monitoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to George Manuel whose telephone number is (571) 272-4952.
The examiner can normally be reached on regular business days.
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 on (571) 270-5213. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) 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.
/George Manuel/
Primary Examiner
Art Unit: 3792
1/23/2026