DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted March 3, 2025. The present application is a U.S. National Stage entry under 35 U.S.C. § 371 of International Application No. PCT/US2023/032504. filed September 12, 2023, which claims the priority from and benefit of U.S. Provisional Patent Application Serial No. 63/375,652, filed September 14, 2022. Claims 4 – 11 are amended. Claims 15 – 20 are new. Claims 1 – 20 are pending examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One
Claims 1 – 20 are drawn to a device, system, and method, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 11, and 15 recite receiving episode data for an acute health event detected by the sensor device; segment the episode data into a plurality of segments, wherein each segment of the plurality of segments consists of a respective portion of the episode data associated with a respective portion of the acute health event; classify the acute health event as one of a plurality of classifications.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the present invention relates to the monitoring of patient health, particularly during an acute episode (paragraph 6 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a need to improve the efficiency and effectiveness of the detection of acute health events (paragraph 6 of the published specification. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “computing device”, “communication circuitry configured to wirelessly communicate with a sensor device on a patient or implanted within the patient”, “one or more output devices”, “processing circuitry”, “sensor device”, “applying one or more machine learning models”, “applying one or more non-machine learning rules”
Claims 2 – 4: “computing device”
Claim 5: “computing device”, “sensor device”, “signal”, “Internet of Things”
Claim 6: “computing device”, “machine learning models comprise one or more neural networks”
Claims 7, 10: “computing device”, “non-machine learning models”
Claim 8: “computing device”, “smartphone”
Claim 9: “computing device”, “Internet of Things”
Claim 11: “system”, “sensor device”, “computing device”
Claim 12: “system”, “implantable medical device”
Claims 13 – 14: “system”, “insertable cardiac monitor”
Claim 15: “processing circuitry” “sensor device”, “communication circuitry”, ““machine learning models”, “non-machine learning models”, “one or more output devices”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0043] Patient computing devices 12 are configured for wireless communication with IMD 10. Computing devices 12 retrieve event data and other sensed physiological data from IMD 10 that was collected and stored by the IMD. In some examples, computing devices 12 take the form of Personal computing devices of patient 4. For example, computing device 12A may take the form of a smartphone of patient 4, and computing device 12B may take the form of a smartwatch or other smart apparel of patient 4. In some examples, computing devices 12 may be any computing device configured for wireless communication with IMD 10, such as a desktop, laptop, or tablet computer. Computing devices 12 may communicate with IMD 10 and each other according to the Bluetooth® or Bluetooth® Low Energy (BLE) protocols, as examples. In some examples, only one of computing devices 12, e.g., computing device 12A, is configured for communication with IMD 10, e.g., due to execution of software ( e.g., part of a health monitoring application as described herein) enabling communication and interaction with an IMD.
[0076] FIG. 3 is a block diagram illustrating an example configuration of a computing device 12 of patient 4, which may correspond to either (or both operating in coordination) of computing devices 12A and 12B illustrated in FIG. 1. In some examples, computing device 12 takes the form of a smartphone, a laptop, a tablet computer, a personal digital assistant (PDA), a smartwatch or other wearable computing device. In some examples, IoT devices 30, computing devices 38 and 42, AED 44, and/or drone 46 may be Configured similarly to the configuration of computing device 12 illustrated in FIG. 3.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 10, 12 – 14, and 16 – 20, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chakravarthy et al., herein after Chakravarthy (U.S. Publication Number 2020/0352466 A1).
The applied reference has a common inventor 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. 102(a)(2) 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) if the same invention is not being claimed; 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 in the reference 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.
Claim 1 (Previously Presented). Chakravarthy teaches a computing device (Figure 1 discloses a computing system) comprising:
communication circuitry configured to wirelessly communicate with a sensor device on a patient or implanted within the patient (Figure 1; paragraph 29 discloses medical device system in conjunction with a patient and a heart, used with an implantable medical device which may be leadless and in wireless communication with an external device);
one or more output devices (Figure 4; paragraph 61 discloses one or more output devices); and
processing circuitry (paragraph 61 discloses processing circuitry) configured to:
receive episode data for an acute health event detected by the sensor device via the communication circuitry, the episode data transmitted by the sensor device in response to detecting the acute health event (paragraph 47 discloses sensing circuitry may monitor signals from electrodes in order to monitor electrical activity of a heart of patient and produce cardiac electrogram data for patient to detect an episode of cardiac arrhythmia of patient. The IMD sends digitized cardiac electrogram data to network for processing by machine learning system);
segment the episode data into a plurality of segments, wherein each segment of the plurality of segments consists of a respective portion of the episode data associated with a respective portion of the acute health event (paragraph 47 discloses the IMD transmits one or more segments of the cardiac electrogram data in response to detecting, via feature delineation, an episode of arrhythmia; paragraph 99 discloses a machine learning system of computing system applies a machine learning model to the intermediate representation of the sensed cardiac electrogram to detect an episode of arrhythmia in patient) ;
classify the acute health event as one of a plurality of classifications (paragraph 8 discloses the computing device applies the machine learning model to compare cardiac features coinciding with the episode of arrhythmia with cardiac features of past episodes of arrhythmia in the patient so as to classify the episode of arrhythmia as an episode of arrhythmia of a particular type) by at least:
applying one or more machine learning models to each segment of the plurality of segments of the episode data (paragraph 47 discloses the IMD sends digitized cardiac electrogram data to network for processing by machine learning system. The IMD transmits one or more segments of the cardiac electrogram data in response to detecting, via feature delineation, an episode of arrhythmia); and
applying one or more non-machine learning rules to each segment of the plurality of segments (Figure 5; paragraph 82 discloses feature delineation to determine whether an episode of cardiac arrhythmia is detected in patient; paragraph 109 discloses that some episodes of arrhythmia are reviewed by the monitoring center or clinician intermittently to ensure that new or changing arrhythmias are not missed, indicating non-machine learning) and
determine whether to control the one or more output devices to output an alarm based on the classification (paragraph 37 discloses the external device may receive data, alerts, patient physiological information, or other information from IMD; paragraph 83 discloses if at least one of machine learning system or the feature delineation operation of detect an episode of cardiac arrhythmia, then computing system may generate a report of the arrhythmia and output the report to a clinician or monitoring center).
Claim 2 (Original). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the acute health event comprises a tachyarrhythmia (paragraph 92 discloses computing system applies feature delineation to detect arrhythmia such as bradycardia, tachycardia, pause, or atrial fibrillation based on rate and variability features in the cardiac electrogram data).
Method claim 16 repeats the subject matter of claim 2. As the underlying processes of claim 16 have been shown to be fully disclosed by the teachings of Chakravarthy in the above rejections of claim 2; as such, these limitations (Claim 16) are rejected for the same reasons given above for claim 2 and incorporated herein.
Claim 3 (Original). Chakravarthy teaches the computing device of claim 2. Chakravarthy teaches wherein the plurality of classifications include one or more of noise, oversensing, supraventricular tachycardia, supraventricular tachycardia with aberrancy, wide complex tachycardia, polymorphic ventricular tachycardia, monomorphic ventricular tachycardia, or ventricular fibrillation (paragraph 81 discloses computing system may apply QRS detection delineation and noise flagging (e.g., is the beat noisy or not) to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia; paragraph 92 discloses the computing system applies feature delineation to detect arrhythmia such as bradycardia, tachycardia, pause, or atrial fibrillation based on rate and variability features in the cardiac electrogram data).
Method claim 17 repeats the subject matter of claim 3. As the underlying processes of claim 17 have been shown to be fully disclosed by the teachings of Chakravarthy in the above rejections of claim 3; as such, these limitations (Claim 17) are rejected for the same reasons given above for claim 3 and incorporated herein.
Claim 4 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the episode data comprises electrocardiogram data (paragraph 99 discloses a machine learning model is applied to the intermediate representation of the sensed cardiac electrogram to detect an episode of arrhythmia in patient).
Method claim 18 repeats the subject matter of claim 4. As the underlying processes of claim 18 have been shown to be fully disclosed by the teachings of Chakravarthy in the above rejections of claim 4; as such, these limitations (Claim 18) are rejected for the same reasons given above for claim 4 and incorporated herein.
Claim 5 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the episode data comprises at least a portion of raw electrocardiogram data stored by the sensor device for the arrhythmia episode, a feature derived from at least a portion of the raw electrocardiogram data stored by the sensor device for the arrhythmia episode, another signal stored by the sensor device for the arrhythmia episode, a feature derived from the another signal, one or more signals from another computing device or an Internet of Things device, or one or more features derived from the one or more signals from the other computing device or the Internet of Things device (paragraph 40 discloses one or more of the other devices (which may be positioned on a housing of another device implanted or external to the patient) may include processing circuitry configured to receive signals from the electrodes or other sensors on the respective devices and/or communication circuitry configured to transmit the signals from the electrodes or other sensors to another device (e.g., external device 12) or server; paragraph 53 discloses the processing circuitry receives a raw signal from via sensing circuitry and/or sensors, and extracts one or more cardiac features from the raw signal; paragraph 61 discloses a computing device includes processing circuitry, one or more input devices, communication circuitry, one or more storage devices, user interface (UI) device(s), and one or more output devices).
Claim 6 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the one or more machine learning models comprise one or more neural networks (paragraph 27 discloses machine learning refers the use of a machine learning model, such as a neural network or deep-learning model, that is trained on training datasets to detect cardiac arrhythmia from cardiac electrogram data).
Claim 7 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the episode data comprises electrocardiogram data and, for each segment of the plurality of segments, the one or more non-machine learning rules are applied to one or more of:
morphological stability or variability of the electrocardiogram data (paragraph 52 discloses processing circuitry identifies one or more cardiac features, such as one or more of a mean heartrate of the patient, a minimum heartrate of the patient, a maximum heartrate of the patient, a PR interval of a heart of the patient, a variability of heartrate of the patient, one or more amplitudes of one or more features of an electrocardiogram (ECG) of the patient, or an interval between the or more features of the ECG of the patient, a T-wave alternans, QRS morphology measures, or other types of cardiac features);
frequency content of the electrocardiogram data (paragraph 54 discloses the patient data may include one or more of an average frequency or an average amplitude of a T-wave of an electrocardiogram of patient to detect the episode of cardiac arrhythmia); or
heart rate stability or variability (paragraph 52 discloses processing circuitry identifies one or more cardiac features, such as one or more of a mean heartrate of the patient, a minimum heartrate of the patient, a maximum heartrate of the patient, a PR interval of a heart of the patient, a variability of heartrate of the patient, one or more amplitudes of one or more features of an electrocardiogram (ECG) of the patient, or an interval between the or more features of the ECG of the patient, a T-wave alternans, QRS morphology measures, or other types of cardiac features).
Method claim 19 repeats the subject matter of claim 7. As the underlying processes of claim 19 have been shown to be fully disclosed by the teachings of Chakravarthy in the above rejections of claim 7; as such, these limitations (Claim 19) are rejected for the same reasons given above for claim 7 and incorporated herein.
Claim 8 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the computing device comprises a smartphone (paragraph 30 discloses a smart phone).
Claim 9 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein the computing device comprises an Internet of Things device (paragraph 31 discloses the external medical device is a wearable electronic device, such as the SEEQ™ Mobile Cardiac Telemetry (MCT) system or another type of wearable "smart" electronic apparel, such as a "smart" watch, "smart" patch, or "smart" glasses, which are considered IOT devices).
Claim 10 (Currently Amended). Chakravarthy teaches the computing device of claim 1. Chakravarthy teaches wherein one or more non-machine learning rules are applied to episode data indicative of one or more of respiration, perfusion, activity and/or posture, heart sounds, blood pressure, or blood oxygen saturation signals (paragraph 59 discloses sensors may include one or more sensors, such as one or more accelerometers, pressure sensors, optical sensors for oxygen saturation; paragraph 82 discloses feature delineation to determine whether an episode of cardiac arrhythmia is detected in patient; paragraph 109 discloses that some episodes of arrhythmia are reviewed by the monitoring center or clinician intermittently to ensure that new or changing arrhythmias are not missed, indicating non-machine learning).
Method claim 20 repeats the subject matter of claim 10. As the underlying processes of claim 20 have been shown to be fully disclosed by the teachings of Chakravarthy in the above rejections of claim 10; as such, these limitations (Claim 20) are rejected for the same reasons given above for claim 10 and incorporated herein.
Claim 11 (Currently Amended). Chakravarthy teaches a system comprising:
the sensor device (Figure 1; paragraph 47 discloses sensing circuitry may monitor signals from electrodes in order to monitor electrical activity of a heart of patient and produce cardiac electrogram data for patient to detect an episode of cardiac arrhythmia of patient); and
the computing device of claim 1 ((Figure 1; paragraph 29 discloses medical device system in conjunction with a patient and a heart, used with an implantable medical device which may be leadless and in wireless communication with an external device).
Claim 12 (Original). Chakravarthy teaches the system of claim 11. Chakravarthy teaches wherein the sensor device comprises an implantable medical device (paragraph 6 discloses a computing device receives cardiac electrogram data of a patient sensed by an implantable medical device).
Claim 13 (Original). Chakravarthy teaches the system of claim 12. Chakravarthy teaches wherein the implantable medical device comprises an insertable cardiac monitor (paragraph 30 discloses an insertable cardiac monitor).
Claim 14 (Original). Chakravarthy teaches the system of claim 13. Chakravarthy teaches wherein the episode data comprises electrocardiogram data and the insertable cardiac monitor (paragraph 6 discloses a computing device receives cardiac electrogram data of a patient sensed by an implantable medical device) comprises:
a housing configured for subcutaneous implantation in a patient, the housing having a length between 40 millimeters (mm) and 60 mm between a first end and a second end, a width less than the length, and a depth less than the width (paragraph 27 discloses the IMD may be implanted outside of a thoracic cavity of patient (e.g. subcutaneously in the pectoral location; paragraph 40 discloses a housing implanted in a patient);
a first electrode at or proximate to the first end (Figure 3 discloses an example of the leadless implantable medical device with electrodes 16A and 16B positioned at a particular distance from each other; paragraph 40 discloses one or more sensors (e.g. electrodes) being positioned on a housing of an IMD);
a second electrode at or proximate to the second end (Figure 3 discloses an example of the leadless implantable medical device with electrodes 16A and 16B positioned at a particular distance from each other; paragraph 40 discloses one or more sensors (e.g. electrodes) being positioned on a housing of an IMD); and
circuitry within the housing and configured to sense an electrocardiogram corresponding to the electrocardiogram data via the first electrode and the second electrode and detect the acute health event based on the electrocardiogram (paragraph 47 discloses sensing circuitry may monitor signals from electrodes in order to monitor electrical activity of a heart of patient and produce cardiac electrogram data for patient to detect an episode of cardiac arrhythmia of patient. The IMD sends digitized cardiac electrogram data to network for processing by machine learning system).
Claim 15 (New). Chakravarthy teaches a method comprising:
receiving, by processing circuitry, episode data for an acute health event detected by a sensor device via communication circuitry, the episode data transmitted by the sensor device in response to detecting the acute health event (paragraph 47 discloses sensing circuitry may monitor signals from electrodes in order to monitor electrical activity of a heart of patient and produce cardiac electrogram data for patient to detect an episode of cardiac arrhythmia of patient. The IMD sends digitized cardiac electrogram data to network for processing by machine learning system);
segmenting, by the processing circuitry, the episode data into a plurality of segments, wherein each segment of the plurality of segments consists of a respective portion of the episode data associated with a respective portion of the acute health event (paragraph 47 discloses the IMD transmits one or more segments of the cardiac electrogram data in response to detecting, via feature delineation, an episode of arrhythmia; paragraph 99 discloses a machine learning system of computing system applies a machine learning model to the intermediate representation of the sensed cardiac electrogram to detect an episode of arrhythmia in patient);
classifying, by the processing circuitry, the acute health event as one of a plurality of classifications (paragraph 8 discloses the computing device applies the machine learning model to compare cardiac features coinciding with the episode of arrhythmia with cardiac features of past episodes of arrhythmia in the patient so as to classify the episode of arrhythmia as an episode of arrhythmia of a particular type) by at least:
applying one or more machine learning models to each segment of the plurality of segments of the episode data (paragraph 47 discloses the IMD sends digitized cardiac electrogram data to network for processing by machine learning system. The IMD transmits one or more segments of the cardiac electrogram data in response to detecting, via feature delineation, an episode of arrhythmia); and
applying one or more non-machine learning rules to each segment of the plurality of segments (Figure 5; paragraph 82 discloses feature delineation to determine whether an episode of cardiac arrhythmia is detected in patient; paragraph 109 discloses that some episodes of arrhythmia are reviewed by the monitoring center or clinician intermittently to ensure that new or changing arrhythmias are not missed, indicating non-machine learning); and
determining, by the processing circuitry, whether to control the one or more output devices to output an alarm based on the classification (paragraph 37 discloses the external device may receive data, alerts, patient physiological information, or other information from IMD; paragraph 83 discloses if at least one of machine learning system or the feature delineation operation of detect an episode of cardiac arrhythmia, then computing system may generate a report of the arrhythmia and output the report to a clinician or monitoring center).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Warren et al. (WO 2005/011809 A2) discloses multiple electrode vectors for implantable cardiac treatment devices.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTINE K RAPILLO whose telephone number is (571)270-3325. The examiner can normally be reached Monday - Friday 7:30 - 4 pm.
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KRISTINE K. RAPILLO
Examiner
Art Unit 3682
/KRISTINE K RAPILLO/ Examiner, Art Unit 3682
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