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 10/07/2024 is being considered by the examiner.
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 1-20 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.
Claims 1 recites “determining whether the multichannel ECG signal is indicative of AF based at least in part on the first information and the second information; determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information”. It is unclear as to what “at least in part entails”. All of the first information and the second information? Part of the first information and part of the second information? There is no definition of what at least in part entails therefore making the claim indefinite. Claims 2-9 are rejected by virtue of dependency on claim 1.
Claims 10 recites “determining whether the multichannel ECG signal is indicative of AF based at least in part on the first information and the second information”. It is unclear as to what “at least in part entails”. All of the first information and the second information? Part of the first information and part of the second information? There is no definition of what at least in part entails therefore making the claim indefinite. Claims 11-20 are rejected by virtue of dependency on claim 10.
Claims 10 recited “analyze a multichannel electrocardiogram (ECG) signal using the at least one of the plurality of ECG electrodes”, however it is unclear how the signal is “multichannel” if only at least one of the plurality of electrodes is being used? Multichannel means more than one electrode, therefore “using at least one of the plurality of ECG electrodes” does not make sense in the context of a multichannel signal. Clarification is needed within the claim to clarify whether more than one electrode is being used. Claims 11-20 are rejected by virtue of dependency on claim 10.
Claim 14 recites “wherein the multichannel ECG signal sensed using the at least one of the plurality of ECG electrodes while the patient is using the WMD comprises three or more channels of ECG signal”. However it is unclear as to whether there is more than one of the plurality of ECG electrodes being used to produce a “multichannel ECG signal”. Further it is unclear how at least one of the plurality of ECG electrodes is being used and three or more channels of ECG signal are being measured? How does at least one of a plurality turn into multiple if only one is being used? Further clarification is needed to determine if more than at least one of the plurality of ECG electrodes is being used. Claim 15 is rejected by virtue of dependency on claim 14.
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 therefore, 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 an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a method of analyzing a multichannel ECG signal while the patient is using the wearable medical device system, using a first algorithm, and analyzing the multichannel ECG signal using a second algorithm different from the first algorithm, and determining whether the multichannel ECG signal is indicative of atrial fibrillation and responsive to the determination providing a notification. This judicial exception is not integrated into a practical application because the generically recited processor elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a processor. The detection modules do not add meaningful limitations to the system as they are insignificant extra-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the detection ECG electrodes amount to merely data gathering of ECG electrical activity and amount to pre-solution activity well-understood, routine, and conventional in manner.
Regarding claims 1, the limitations “using a first algorithm to analyze a multichannel electrocardiogram signal”, “using a second algorithm with a different noise tolerance from the first algorithm to analyze the multichannel ECG signal”, “determining whether the multichannel EXG signal is indicative of suspected AF based at least in part on the first information and the second information”, “responsive to determination that the multichannel ECG signal is indicative of AF provide a notification” and “responsive to a determination that the multichannel is indicative of suspected AF provide a notification”, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “for use in a wearable medical device (WMD) system for detecting atrial fibrillation”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “for use in a wearable medical device system” language, “using a first and second algorithm” in the context of this claim encompasses the user manually analyzing the multichannel ECG signal. Similarly, the limitation of “determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information”, “responsive to determination that the multichannel ECG signal is indicative of AF provide a notification” and “responsive to a determination that the multichannel is indicative of suspected AF provide a notification”, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “for use in a wearable medical device (WMD) system for detecting atrial fibrillation” language, “determining”, and “providing” in the context of this claim encompasses the calculating and making a decision that the ECG signals indicate AF or suspected AF. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a computer and processor to perform the determining, providing and selecting steps. The computer and processor in both steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining and selecting information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, claim 1 does not amount to significantly more than the abstract idea of analyzing a multichannel ECG signal using a first algorithm, and analyzing the multichannel ECG signal using a second algorithm different from the first algorithm; and determining whether the multichannel ECG signal is indicative of atrial fibrillation since the recited additional elements, either alone or in combination, do not provide for an inventive concept.
Regarding claim 2, the claim refers to additional details of the abstract idea such as “further comprising using a noise detection algorithm to analyze the multichannel ECG signal, the noise detection algorithm outputting noise analysis information, and wherein determining whether the multichannel ECG signal is indicative of AF is further based on the noise analysis information”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 3, the claim refers to additional details of the abstract idea such as “wherein the noise analysis information includes information indicating which channel or channels of the multichannel ECG signal exceeded a noise criterion and which channel or channels of the multichannel ECG signal did not exceed the noise criterion”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of noise. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 4, the claim refers to additional details of the abstract idea such as “wherein the channel or channels that exceeded the noise criterion are analyzed using the first algorithm and the channel or channels that did not exceed the noise criterion are analyzed using the second algorithm”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 5, the claim refers to additional details of the abstract idea such as “further comprising: in response to the determination that the multichannel ECG signal is indicative of AF, measuring a duration of the multichannel ECG signal being indicative of AF; and determining an AF burden using the measured duration”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 6, the claim refers to additional details of the abstract idea such as “further comprising determining the AF burden over a predetermined time period without using any channel or channels of the multichannel ECG signal that exceeded a noise criterion”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 7, the claim refers to additional details of the abstract idea such as “further comprising determining a metric corresponding to an amount of time in which the multichannel ECG signal was determined to be indicative of detected AF and another metric corresponding to an amount of time in which the multichannel ECG signal was determined to be indicative of suspected AF”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 8, the claim refers to additional details of the abstract idea such as “wherein the first algorithm is used to determine the detected AF and the second algorithm is used to determine the suspected AF”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 9, the claim refers to additional details of the abstract idea such as “wherein the first algorithm comprises a high specificity AF algorithm and the second algorithm comprises a noise-tolerant AF algorithm with less specificity than the first algorithm”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 1. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claims 11, the limitations “using a first algorithm to analyze a multichannel electrocardiogram signal”, “using a second algorithm with a different noise tolerance from the first algorithm to analyze the multichannel ECG signal”, “determining whether the multichannel EXG signal is indicative of suspected AF based at least in part on the first information and the second information”, “responsive to determination that the multichannel ECG signal is indicative of AF provide a notification” and “responsive to a determination that the multichannel is indicative of suspected AF provide a notification”, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “for use in a wearable medical device (WMD) system for detecting atrial fibrillation”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “for use in a wearable medical device system” language, “using a first and second algorithm” in the context of this claim encompasses the user manually analyzing the multichannel ECG signal. Similarly, the limitation of “determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information”, “responsive to determination that the multichannel ECG signal is indicative of AF provide a notification” and “responsive to a determination that the multichannel is indicative of suspected AF provide a notification”, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “for use in a wearable medical device (WMD) system for detecting atrial fibrillation” language, “determining”, and “providing” in the context of this claim encompasses the calculating and making a decision that the ECG signals indicate AF or suspected AF. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a computer and processor to perform the determining, providing and selecting steps. The computer and processor in both steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining and selecting information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, claim 10 does not amount to significantly more than the abstract idea of analyzing a multichannel ECG signal using a first algorithm, and analyzing the multichannel ECG signal using a second algorithm different from the first algorithm; and determining whether the multichannel ECG signal is indicative of atrial fibrillation since the recited additional elements, either alone or in combination, do not provide for an inventive concept.
Regarding claim 11, the claim refers to an additional element, “wherein the plurality of ECG electrodes are dry electrodes”. This additional element limitation does not integrate the abstract idea into a practical application. The ECG electrodes do not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 12, the claim refers to an additional element, “wherein positioning the at least one of the plurality of ECG electrodes on the torso of the patient is performed by coupling with a garment.”. This additional element limitation does not integrate the abstract idea into a practical application. The garment does not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 13, the claim refers to an additional element, “wherein the garment is worn by patient”. This additional element limitation does not integrate the abstract idea into a practical application. The garment does not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 14, the claim refers to an additional element, “wherein the multichannel ECG signal sensed using the at one of the plurality of ECG electrodes while the patient is using the WMD comprises three or more channels of ECG signal”. This additional element limitation does not integrate the abstract idea into a practical application. The ECG electrodes do not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 15, the claim refers to an additional element, “wherein the WMD is a wearable cardioverter defibrillator”. This additional element limitation does not integrate the abstract idea into a practical application. The external defibrillator does not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 16, the claim refers to additional details of the abstract idea such as “wherein the determination that the multichannel ECG signal is indicative of AF is made remotely by receiving measurements of the multichannel ECG signal from the WMD”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 10. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 17, the claim refers to an additional element, “cloud-based service”. This additional element limitation does not integrate the abstract idea into a practical application. The cloud-based service does not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 18, the claim refers to additional details of the abstract idea such as “further comprising: in response to the determination that the multichannel ECG signal is indicative of AF, measuring a duration of the multichannel ECG signal being indicative of AF; and determining an AF burden using the measured duration”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 10. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
Regarding claim 19, the claim refers to an additional element, “remote system”. This additional element limitation does not integrate the abstract idea into a practical application. The remote system does not amount to significantly more than the judicial exception. Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor does it amount to significantly more than the abstract idea under step 2B.
Regarding claim 20, the claim refers to additional details of the abstract idea such as “further comprising: determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information; and responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected.”. This activity is performable in the human mind in a similar manner to that discussed with evaluation limitations of Claim 10. This amounts to a mental activity an individual can mentally perform on a multichannel ECG signal using pen and paper, to identify and determine whether the multichannel ECG signal is indicative of atrial fibrillation. See MPEP 2106.04(a)(2)(III). Therefore, this limitation does not amount to integration of the abstract idea into a practical application under Step 2A, Prong 2 nor do they amount to significantly more than the abstract idea under step 2B.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-11, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over -Dziubinski et al (US 2009/0171227) herein referred to as “Dziubinski” in view of Reaser et al. (US 2014/0114167) herein referred to as “Reaser”.
.
Regarding claim 1, Dziubinski discloses a method for use in a wearable medical device (WMD) system for detecting atrial fibrillation (AF) in a patient capable of being ambulatory while using the WMD system (use of the recordation module 110, Paragraph [0054]), the method comprising: using a first algorithm to analyze a multichannel electrocardiogram (ECG) signal sensed using a plurality of ECG electrodes while the patient is using the WMD system (the pre-classified signal cam be analyzed using a beat classification algorithm and/or an arrhythmia detection algorithm. The analyzed signal can be verified using a detection evaluation correction algorithm. The beat classification algorithm and the arrhythmia detection algorithm can generate the calculated annotations for every ECG beat., Paragraph [0013]), the first algorithm outputting first information (The beat classification algorithm and the arrhythmia detection algorithm can generate the calculated annotations for every ECG beat., Paragraph [0013]); using a second algorithm (The analysis algorithm can include a noise and distortion detection sub-algorithm (NDDA) for detecting noisy and non-linearly distorted ECG fragments, Paragraph [0013]), with a different noise tolerance from the first algorithm (The analysis algorithm can include a noise and distortion detection sub-algorithm (NDDA) for detecting noisy and non-linearly distorted ECG fragments. The NDDA can further estimate a broad band noise energy level of the signal and detect distortions generated by not properly attached electrodes, Paragraph [0013]), to analyze the multichannel ECG signal, the second algorithm outputting second information (The NDDA can further estimate a broad band noise energy level of the signal and detect distortions generated by not properly attached electrodes, Paragraph [0013]); determining whether the multichannel ECG signal is indicative of AF based at least in part on the first information and the second information (Further, an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task, Paragraph [0114]); determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information (The recordation module 110 can be an ECG digitization and transmission unit operable on two (2) electrodes (single channel), or three (3) electrodes (multi channel), Paragraph [0061]); responsive to a determination that the multichannel ECG signal is indicative of AF (further, an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
However Dziubinski does not explicitly disclose providing a notification that AF has been detected; and responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected.
Reaser discloses systems and devices to gather data to determine whether the subject is experiencing cardiac arrhythmia (Abstract) wherein the method includes providing a notification that AF has been detected; and responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected (said detector body having a processing unit, in its interior, which is operatively connected to said electrodes and can detect atrial fibrillation, and is configured to activate a first one of said indicator lights when said processing unit determines that atrial fibrillation is NOT occurring, and is configured to active a second one of said indicator lights when said processing unit determines that atrial fibrillation IS occurring, Paragraph [0098]).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski to incorporate the teachings of Reaser by including wherein the method includes providing a notification that AF has been detected; and responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected. The motivation to do so being to indicate when atrial fibrillation is occurring so untrained personnel can summon emergency personnel, and/or follow predetermined emergency procedures, without waiting for medical interpretation (Reaser, Paragraph [0098]).
Regarding claim 2, Dziubinski in view of Reaser discloses the method of claim 1.
Dziubinski further discloses wherein the method comprises using a noise detection algorithm to analyze the multichannel ECG signal (a Noise and Distortion Detection Algorithm (NDDA) 360 is used for detecting non-linearly distorted ECG fragments, usually caused by hard clipping. The detector analyzes consecutive samples, and in case of identical values over and under predefined maximum and minimum threshold, detects the distortion, Paragraph [0116]), the noise detection algorithm outputting noise analysis information, and wherein determining whether the multichannel ECG signal is indicative of AF is further based on the noise analysis information (the ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, at the same time the Noise and Distortion Detection Algorithm is used, Paragraph [01114]).
Regarding claim 3, Dziubinski in view of Reaser discloses the method of claim 2.
Dziubinski further discloses wherein the noise analysis information includes information indicating which channel or channels of the multichannel ECG signal exceeded a noise criterion and which channel or channels of the multichannel ECG signal did not exceed the noise criterion (further, QRS detection is performed by statistics based methods in an Intelligent QRS Detection Algorithm (IQDA) 320, which is utilizing information about average HR, higher order statistics description of the rhythm evolution, QRS complexes properties, such as shape and amplitude, T wave shape, base line behavior, noise level and many others, providing very robust decision results. The non-linear prediction of evolving HR (calculated for each beat) allows for calculating the expected QRS time-domain position. This information combined with peak shape analysis and peak level versus surrounding noise level analysis allow for robust QRS complexes detection. Peaks shape analysis and noise level estimation are further described in the BCA block 330 description and the NDDA block 360 description, Paragraph [0096], an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114], a Noise and Distortion Detection Algorithm (NDDA) 360 is used for detecting non-linearly distorted ECG fragments, usually caused by hard clipping. The detector analyzes consecutive samples, and in case of identical values over and under predefined maximum and minimum threshold, detects the distortion, Paragraph [0116]).
Regarding claim 4, Dziubinski in view of Reaser discloses the method of claim 3.
Dziubinski discloses wherein the channel or channels that exceeded the noise criterion are analyzed using the first algorithm (a Beat Classification Algorithm (BCA) 330 is used. Based on feature vector (FV) containing description of currently analyzed beat, statistical classifier and ANN system (Artificial Neural Network) performs classification with accordance to peak shape, width, amplitude, T-wave shape, and with reference to previously calculated feature vectors representing reference normal beats, reference pathological beats and as well as recently classified beats. After primary recognition, BCA performance is evaluated with reference to multidimensional prediction coefficients, describing evolution of the feature vectors. Based on the Euclidean distance between predicted FV and measured FV, unpredictability measure parameter for the current beat is estimated and primary recognition improved. The peak shape description parameters are obtained from a preprocessed ECG block, Paragraph [0097) and the channel or channels that did not exceed the noise criterion are analyzed using the second algorithm (an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
Regarding claim 5, Dziubinski in view of Reaser discloses the method of claim 1.
Dziubinski discloses wherein the method further comprises: in response to the determination that the multichannel ECG signal is indicative of AF, measuring a duration of the multichannel ECG signal being indicative of AF; and determining an AF burden using the measured duration (it is important to note that all information describing every ECG beat, such as TWA amplitude (for each lead); QT interval; similarity of each T wave to the reference T wave used for QT interval changes calculations; beat type; QRS location in time; arrhythmia type; ST segment elevation (for each lead); similarity to averaged PQRST complex; ADC network interference level (for each lead); and broad band noise level (for each lead) are transmitted to the server (and are available at the desktop application). Such a set of parameters has been carefully chosen to enable, even without viewing the accompanying ECG waveform, discriminating between clean ECG and misclassified artifacts. In case of doubts, the desktop application interface allows for requesting any ECG fragment. This allows the physician access to any ECG fragment stored in the microcomputer 121 memory at any time (such a request message is submitted to the microcomputer 121 application via the server application, and the requested ECG fragment is immediately returned to the server for further analysis., Paragraph [0071], an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
Regarding claim 6, Dziubinski in view of Reaser discloses the method of claim 5.
Dziubinski further comprising determining the AF burden over a predetermined time period without using any channel or channels of the multichannel ECG signal that exceeded a noise criterion (a Noise and Distortion Detection Algorithm (NDDA) 360 is used for detecting non-linearly distorted ECG fragments, usually caused by hard clipping. The detector analyzes consecutive samples, and in case of identical values over and under predefined maximum and minimum threshold, detects the distortion. In addition a broad band noise energy is estimated with the use of Unpredictability Measure (UM) algorithm. The UM algorithm is very much suitable for ECG analysis, because the electrocardiographic signal is quasi-periodic (similarly to audio signals) and chaotic phase of the spectrum components is related to parasite noise , Paragraph [0116], The T wave related parameters, i.e., ST segment elevation, QT interval duration and TWA amplitude can be calculated only in case of properly detected QRS complexes and correctly classified beats, therefore the QRS detection and classification component and the arrhythmia detection component of the system is of great importance and is the basis for proper T wave analysis. Many arrhythmia recognition, peak classification and auxiliary information algorithm blocks operate on predefined, or adaptively calculated parameters. The above-described systems and methods can be implemented in digital electronic circuitry, in computer hardware, firmware, and/or software. The implementation can be as a computer program product (i.e., a computer program tangibly embodied in an information carrier). The implementation can, for example, be in a machine-readable storage device and/or in a propagated signal, for execution by, or to control the operation of, data processing apparatus. The implementation can, for example, be a programmable processor, a computer, and/or multiple computers, Paragraph [0198]).
Regarding claim 7, Dziubinski in view of Reaser discloses the method of claim 1.
Dziubinski further discloses wherein the method comprises determining a metric corresponding to an amount of time in which the multichannel ECG signal was determined to be indicative of detected AF and another metric corresponding to an amount of time in which the multichannel ECG signal was determined to be indicative of suspected AF (The T wave related parameters, i.e., ST segment elevation, QT interval duration and TWA amplitude can be calculated only in case of properly detected QRS complexes and correctly classified beats, therefore the QRS detection and classification component and the arrhythmia detection component of the system is of great importance and is the basis for proper T wave analysis. Many arrhythmia recognition, peak classification and auxiliary information algorithm blocks operate on predefined, or adaptively calculated parameters. The above-described systems and methods can be implemented in digital electronic circuitry, in computer hardware, firmware, and/or software. The implementation can be as a computer program product (i.e., a computer program tangibly embodied in an information carrier). The implementation can, for example, be in a machine-readable storage device and/or in a propagated signal, for execution by, or to control the operation of, data processing apparatus. The implementation can, for example, be a programmable processor, a computer, and/or multiple computers, Paragraph [0198]).
Regarding claim 8, Dziubinski in view of Reaser discloses the method of claim 1.
Dziubinski further discloses wherein the first algorithm is used to determine the detected AF (a Beat Classification Algorithm (BCA) 330 is used. Based on feature vector (FV) containing description of currently analyzed beat, statistical classifier and ANN system (Artificial Neural Network) performs classification with accordance to peak shape, width, amplitude, T-wave shape, and with reference to previously calculated feature vectors representing reference normal beats, reference pathological beats and as well as recently classified beats. After primary recognition, BCA performance is evaluated with reference to multidimensional prediction coefficients, describing evolution of the feature vectors. Based on the Euclidean distance between predicted FV and measured FV, unpredictability measure parameter for the current beat is estimated and primary recognition improved. The peak shape description parameters are obtained from a preprocessed ECG block. The preprocessing routine is based on a linear operations utilizing moving average filter bank and difference function, Paragraph [0097]) and the second algorithm is used to determine the suspected AF (an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
Regarding claim 9, Dziubinski in view of Reaser discloses the method of claim 1.
Dziubinski further discloses wherein the method comprises wherein the first algorithm comprises a high specificity AF algorithm (a Detection Evaluation Algorithm (DEA) 350 is employed. Beat recognition and arrhythmia detection is performed for all fragments (even extremely distorted) of the ECG signal. In some cases, however, distortions and noise level may cause misclassifications. Based on the base line drift fluctuations, calculated with the use of 160 ms ECG fragments preceding x1 point for each QRS complex (see FIG. 7), distortions are detected. Also noise level, non-linear distortion disturbances (hard clipping) and neighbor similarity of consecutive QRS complexes (calculated with regard to FVs used in BCA 330) are used as a signal condition descriptor , Paragraph [0115]) and the second algorithm comprises a noise-tolerant AF algorithm with less specificity than the first algorithm (a Noise and Distortion Detection Algorithm (NDDA) 360 is used for detecting non-linearly distorted ECG fragments, usually caused by hard clipping. The detector analyzes consecutive samples, and in case of identical values over and under predefined maximum and minimum threshold, detects the distortion. In addition a broad band noise energy is estimated with the use of Unpredictability Measure (UM) algorithm. The UM algorithm is very much suitable for ECG analysis, because the electrocardiographic signal is quasi-periodic (similarly to audio signals) and chaotic phase of the spectrum components is related to parasite noise, Paragraph [0116]).
Regarding claim 10, Dziubinski discloses a method for use in a wearable medical device (WMD) for detecting atrial fibrillation (AF) in a patient while using the WMD (use of the recordation module 110, Paragraph [0054]), the method comprising: positioning at least one of a plurality of ECG electrodes on a torso of the patient (from a first electrode 112 and a second electrode 114 (single channel or single lead) or three or more electrodes (multi channel or multi-lead), Paragraph [0055]); using a first algorithm to analyze a multichannel electrocardiogram (ECG) signal sensed using the at least one of the plurality of ECG electrodes while the patient is using the WMD (the pre-classified signal cam be analyzed using a beat classification algorithm and/or an arrhythmia detection algorithm. The analyzed signal can be verified using a detection evaluation correction algorithm. The beat classification algorithm and the arrhythmia detection algorithm can generate the calculated annotations for every ECG beat., Paragraph [0013]), the first algorithm outputting first information (The beat classification algorithm and the arrhythmia detection algorithm can generate the calculated annotations for every ECG beat., Paragraph [0013]); using a second algorithm, with a different noise tolerance from the first algorithm, to analyze the multichannel ECG signal (The analysis algorithm can include a noise and distortion detection sub-algorithm (NDDA) for detecting noisy and non-linearly distorted ECG fragments. The NDDA can further estimate a broad band noise energy level of the signal and detect distortions generated by not properly attached electrodes, Paragraph [0013]), the second algorithm outputting second information (The NDDA can further estimate a broad band noise energy level of the signal and detect distortions generated by not properly attached electrodes, Paragraph [0013]); determining whether the multichannel ECG signal is indicative of AF based at least in part on the first information and the second information ((The recordation module 110 can be an ECG digitization and transmission unit operable on two (2) electrodes (single channel), or three (3) electrodes (multi channel), Paragraph [0061]); responsive to a determination that the multichannel ECG signal is indicative of AF (further, an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
However Dziubinski does not explicitly disclose wherein responsive to a determination that the multichannel ECG signal is indicative of AF, providing a notification that AF has been detected.
Reaser discloses systems and devices to gather data to determine whether the subject is experiencing cardiac arrhythmia (Abstract) wherein the method includes responsive to a determination that the multichannel ECG signal is indicative of AF, providing a notification that AF has been detected (said detector body having a processing unit, in its interior, which is operatively connected to said electrodes and can detect atrial fibrillation, and is configured to activate a first one of said indicator lights when said processing unit determines that atrial fibrillation is NOT occurring, and is configured to active a second one of said indicator lights when said processing unit determines that atrial fibrillation IS occurring, Paragraph [0098]).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski to incorporate the teachings of Reaser by including wherein the method includes responsive to a determination that the multichannel ECG signal is indicative of AF, providing a notification that AF has been detected. The motivation to do so being to indicate when atrial fibrillation is occurring so untrained personnel can summon emergency personnel, and/or follow predetermined emergency procedures, without waiting for medical interpretation (Reaser, Paragraph [0098]).
Regarding claim 11, Dziubinski in view of Reaser discloses the method of claim 10, wherein the plurality of ECG electrodes are dry electrodes (signal from a first electrode 112 and a second electrode 114 (single channel or single lead) or three or more electrodes (multi channel or multi-lead)., Paragraph [0055]).
Regarding claim 14, Dziubinski in view of Reaser discloses the method of claim 10.
Dziubinski further discloses wherein the multichannel ECG signal sensed using the at least one of the plurality of ECG electrodes while the patient is using the WMD comprises three or more channels of ECG signal (The recordation device 110 operates on signal from a first electrode 112 and a second electrode 114 (single channel or single lead) or three or more electrodes (multi channel or multi-lead), Paragraph [0055]).
Regarding claim 16, Dziubinski in view of Reaser discloses the method of claim 10.
Dziubinski further discloses wherein the determination that the multichannel ECG signal is indicative of AF is made remotely by receiving measurements of the multichannel ECG signal from the WMD (The system 100 includes, but is not limited to, a recordation module 110, a cardiac tele-rehabilitation module 120, and data transmission network 130, and a remote monitoring module 140, Paragraph [0054], Figure 2, The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other., Paragraph [0205], the ECG signal “S” obtained from a patient is input into the processing module 121. From the DC removal unit 210, the signal enters into the signal pre-processing unit 220 responsible for performing the signal pre-processing, QRS detection and beat classification. From the signal pre-processing unit 220, the signal is provided to the arrhythmia detection unit 240, that is responsible for arrhythmia detection according to predefined logic rules utilizing expert systems. In particular, the expert systems may employ neural networks, fuzzy logic, statistical methods, etc. At the same time, the signal from the DC removal unit 210 is provided to the detection and evaluation unit 230, where detection evaluation, noise and distortion detection, as well as auxiliary information calculation is determined. Signals from the detection and evaluation unit 230 and the arrhythmia detection unit 240 are then fed to the final processing unit 250, which includes preparation of information “V” in a user-readable format, Paragraph [0075]).
Regarding claim 17, Dziubinski in view of Reaser discloses the method of claim 16.
Dziubinski further discloses wherein the determination that the multichannel ECG signal is indicative of AF is made by a remote server or a cloud-based service (The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other., Paragraph [0205], the ECG signal “S” obtained from a patient is input into the processing module 121. From the DC removal unit 210, the signal enters into the signal pre-processing unit 220 responsible for performing the signal pre-processing, QRS detection and beat classification. From the signal pre-processing unit 220, the signal is provided to the arrhythmia detection unit 240, that is responsible for arrhythmia detection according to predefined logic rules utilizing expert systems. In particular, the expert systems may employ neural networks, fuzzy logic, statistical methods, etc. At the same time, the signal from the DC removal unit 210 is provided to the detection and evaluation unit 230, where detection evaluation, noise and distortion detection, as well as auxiliary information calculation is determined. Signals from the detection and evaluation unit 230 and the arrhythmia detection unit 240 are then fed to the final processing unit 250, which includes preparation of information “V” in a user-readable format, Paragraph [0075]).
Regarding claim 18, Dziubinski in view of Reaser discloses the method of claim 10.
Dziubinski further discloses wherein the method comprises: in response to the determination that the multichannel ECG signal is indicative of AF, measuring a duration of the multichannel ECG signal being indicative of AF; and determining an AF burden using the measured duration (it is important to note that all information describing every ECG beat, such as TWA amplitude (for each lead); QT interval; similarity of each T wave to the reference T wave used for QT interval changes calculations; beat type; QRS location in time; arrhythmia type; ST segment elevation (for each lead); similarity to averaged PQRST complex; ADC network interference level (for each lead); and broad band noise level (for each lead) are transmitted to the server (and are available at the desktop application). Such a set of parameters has been carefully chosen to enable, even without viewing the accompanying ECG waveform, discriminating between clean ECG and misclassified artifacts. In case of doubts, the desktop application interface allows for requesting any ECG fragment. This allows the physician access to any ECG fragment stored in the microcomputer 121 memory at any time (such a request message is submitted to the microcomputer 121 application via the server application, and the requested ECG fragment is immediately returned to the server for further analysis., Paragraph [0071], an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
Regarding claim 19, Dziubinski in view of Reaser discloses the method of claim 18.
Dziubinski further discloses wherein the method comprises wherein the AF burden is determined by a remote system (The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other., Paragraph [0205], the ECG signal “S” obtained from a patient is input into the processing module 121. From the DC removal unit 210, the signal enters into the signal pre-processing unit 220 responsible for performing the signal pre-processing, QRS detection and beat classification. From the signal pre-processing unit 220, the signal is provided to the arrhythmia detection unit 240, that is responsible for arrhythmia detection according to predefined logic rules utilizing expert systems. In particular, the expert systems may employ neural networks, fuzzy logic, statistical methods, etc. At the same time, the signal from the DC removal unit 210 is provided to the detection and evaluation unit 230, where detection evaluation, noise and distortion detection, as well as auxiliary information calculation is determined. Signals from the detection and evaluation unit 230 and the arrhythmia detection unit 240 are then fed to the final processing unit 250, which includes preparation of information “V” in a user-readable format, Paragraph [0075]).
Regarding claim 20, Dziubinski in view of Reaser discloses the method of claim 10.
Dziubinski further discloses wherein the method comprises: determining whether the multichannel ECG signal is indicative of suspected AF based at least in part on the first information and the second information (further, an Arrhythmia Detection Algorithm (ADA) 340 is utilized. After beats classification, ADA 340 performs arrhythmia detection with accordance with the predefined logic rules as shown below, however similarly to BCA 330 primary recognition process, the decision making module in ADA 340 is based on statistical classifier which adaptively (to the rhythm behavior) performs classification task. The ADA 340 algorithm monitors HR changes, and in case of rapid heart rate increases (assuming sufficiently high BPM) recognizes tachycardia rhythms. In case of chaotic rhythm, assuming sufficient similarity of consecutive beats, atrial fibrillation is detected, Paragraph [0114]).
However Dziubinski does not explicitly disclose wherein the method comprises responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected.
Reaser discloses systems and devices to gather data to determine whether the subject is experiencing cardiac arrhythmia (Abstract) wherein the method comprises responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected (said detector body having a processing unit, in its interior, which is operatively connected to said electrodes and can detect atrial fibrillation, and is configured to activate a first one of said indicator lights when said processing unit determines that atrial fibrillation is NOT occurring, and is configured to active a second one of said indicator lights when said processing unit determines that atrial fibrillation IS occurring, Paragraph [0098]).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski to incorporate the teachings of Reaser by including wherein the method comprises responsive to a determination that the multichannel ECG signal is indicative of suspected AF, providing a notification that suspected AF has been detected. The motivation to do so being to indicate when atrial fibrillation is occurring so untrained personnel can summon emergency personnel, and/or follow predetermined emergency procedures, without waiting for medical interpretation (Reaser, Paragraph [0098]).
Claim(s) 12-13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dziubinski in view of Reaser further in view of Medema et al. (US 20180272145 A1) herein referred to as “Medema”.
Regarding claim 12, Dziubinski in view of Reaser discloses the method of claim 10.
However Dziubinski in view of Reaser does not explicitly disclose wherein positioning the at least one of the plurality of ECG electrodes on the torso of the patient is performed by coupling with a garment.
Medema discloses ECG to be more securely affixed to the patient’s body (Abstract) wherein positioning the at least one of the plurality of ECG electrodes on the torso of the patient is performed by coupling with a garment (While the garment should keep the defibrillation electrodes in contact with the body, the main purpose of the garment is to hold the ECG electrodes in place against the body with sufficient pressure while minimizing the motion of the electrode on the skin, Paragraph [0017], garment 170 includes a harness, one or more belts or straps, etc. In such embodiments, those items can be worn around the torso or hips, over the shoulder, or the like. In other embodiments, garment 170 includes a container or housing, which may be waterproof, Paragraphs [0024]-[0025], Figure 1).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski in view of Reaser to incorporate the teachings of Medema by including wherein positioning the at least one of the plurality of ECG electrodes on the torso of the patient is performed by coupling with a garment. The motivation to do so being to hold the ECG electrodes in place against the body with sufficient pressure while minimizing the motion of the electrode on the skin (Medema, Paragraph [0017).
Regarding claim 13, Dziubinski in view of Reaser and Medema discloses the method of claim 12.
However, Dziubinski in view of Reaser does not explicitly disclose wherein the garment is worn by the patient.
Medema discloses wherein the garment is worn by the patient (garment 170 includes a harness, one or more belts or straps, etc. In such embodiments, those items can be worn around the torso or hips, over the shoulder, or the like. In other embodiments, garment 170 includes a container or housing, which may be waterproof, Paragraphs [0024]-[0025], Figure 1).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski in view of Reaser to incorporate the teachings of Medema by including wherein the garment is worn by the patient. The motivation to do so being to hold the ECG electrodes in place against the body with sufficient pressure while minimizing the motion of the electrode on the skin (Medema, Paragraph [0017).
Regarding claim 15, Dziubinski in view of Reaser discloses the method of claim 14.
Dziubinski discloses wherein patients who are found to be at high risk would therefore benefit from the placement of a defibrillator device which can stop arrhythmia and save the patient's life (Paragraph [0051]), however Dziubinski in view of Reaser does not explicitly disclose wherein the WMD is a wearable cardioverter defibrillator (WCD).
Medema discloses wherein the WMD is a wearable cardioverter defibrillator (WCD) (this disclosure is directed at improvements in monitoring a patient's condition (e.g., electrocardiogram) for an extended period of time in the application of a wearable cardiac defibrillator (WCD), Paragraph [0015], in inis particular example, the medical device is a wearable cardioverter defibrillator (WCD) system. FIG. 1A is a front view of the WCD system; and FIG. 1B is a rear view of the WCD system. FIGS. 1A and 1B may be collectively referred to as “FIG. 1.”, Paragraph [0022]).
It would have been prima facie obvious to one of ordinary skill in the art before
the effective filing date of the claimed invention to have modified Dziubinski in view of Reaser to incorporate the teachings of Medema by including wherein the WMD is a wearable cardioverter defibrillator (WCD. The motivation to do so being to utilize a device for monitoring a patient’s ECG signals wherein defibrillation can be performed during monitoring if medically necessary (Medema, Paragraph [0017]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Banet et al. (US 20140107509 A1) discloses a device to record cardiac information from a patient’s torso, Sullivan et al. (US 20160135706 A1) discloses a system for detecting a patient’s heart rhythm comprising a first and second algorithm, and Freeman et al. (US 20180272147 A1) discloses a wearable medical treatment system for recording cardiac sensing electrodes and a defibrillator.
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/D.S./Examiner, Art Unit 3794
/JOANNE M RODDEN/Supervisory Patent Examiner, Art Unit 3794
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