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 .
Status of Claims
Claims 1, 4, 8-13, 15, 17, 19-20, 43, 75, and 599 are currently pending and under examination. Claims 2-3, 5-7, 14, 16, 18, 21-42, 44-74, and 76-598 are canceled. As per the amendments filed on 07/10/2026, claim 1 is amended.
Priority
The instant application (filed on 12/09/2021) is a non-provisional application filed under 35 USC 111(a). Acknowledgment is made of Applicant's claim for domestic priority based on provisional applications 63/199,208 (filed 12/14/2020) and 63/262,657 (filed 10/18/2021). Amended claims 1, 4, 8-13, 15, 17, 19-20, 43, 75, and 599 are adequately supported in 63/199,208 so as to receive the earliest effective filing date of 12/14/2020.
Response to Arguments
Applicant’s arguments, see Remarks pages 7-9 (Prior Art Rejection), filed 07/10/2026, with respect to the 35 U.S.C. § 103 rejection of claims 1, 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 have been fully considered. Regarding independent claim 1, Applicant argues:
Without conceding the propriety of this rejection, independent claim 1 has been amended. As amended, independent claim 1 recites a cardiac monitoring system for displaying contextual biometric information for arrhythmia events occurring in a patient, comprising: a device configured to be worn by the patient and a remote server. The device comprises a plurality of ECG electrodes disposed on the device and configured to sense ECG signals of the patient; and a motion sensor configured to acquire motion signals associated with the patient. The motion signals comprise motion information associated with at least an activity level of the patient and respiration of the patient and posture information of the patient associated with at least a posture of the patient.
The device configured to be worn by the patient is further configured to transmit the ECG signals and motion signals to the remote server. Additionally, independent claim 1 recites that the remote server is in communication with the device configured to be worn by the patient and further in communication with a user interface. The remote server comprises a database implemented in a non-transitory media and a processor in communication with the database. The processor is configured to store the ECG signals and the motion signals in the database and identify an arrhythmia event based on the received ECG signals. The processor is configured to identify an onset of the arrhythmia event based on the received ECG signals. The onset of the arrhythmia event is identified by comparing the received ECG signals to at least a first arrhythmia threshold for identifying bradycardia, a second arrythmia threshold for identifying tachycardia, and a third arrhythmia threshold for determining atrial fibrillation; determining that the arrhythmia event has occurred based on the comparison; and determining the onset of the arrhythmia event based on a time of the comparison of the received ECG signals to the at least first arrhythmia threshold, second arrhythmia threshold, and third arrhythmia threshold. In addition, the processor is configured to identify an offset of the arrhythmia event based on the received ECG signals.
The processor is also configured to determine, based on the motion signals, contextual biometric information of the patient providing context for the arrhythmia event, where the contextual biometric information comprises at least the activity level of the patient, the posture of the patient, and the respiration of the patient associated with the arrhythmia event, determine an arrhythmia contextual time period around the onset of the arrhythmia event and the offset of the arrhythmia event, where the arrhythmia contextual time period comprises a time period during which the onset of the arrhythmia event and the off set of the arrhythmia event occurred, and generate, based on at least the identified arrhythmia event, the identified onset of the arrhythmia, the identified off set of the arrhythmia event, and the contextual biometric information of the patient for the arrhythmia event, an arrhythmia report.
The arrhythmia report comprises at least a graphical timeline comprising the arrhythmia contextual time period, a biometric graphical representation of the contextual biometric information of the patient during the arrhythmia contextual time period, the biometric graphical representation comprising at least a representation illustrating changes of the activity level of the patient, the posture of the patient, and the respiration of the patient during the arrhythmia event, an arrhythmia onset graphical indicator corresponding to the onset of the arrhythmia event overlaid on the biometric graphical representation and indicating the onset of the arrhythmia event relative to the activity level of the patient, the posture of the patient, and the respiration of the patient, and an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation and indicating the offset of the arrhythmia event relative to the activity level of the patient, the posture of the patient, and the respiration of the patient. The processor is further configured to display, on the user interface, the arrythmia report.
The combination of Hughes and Giftakis is not understood to teach or suggest all of the features required by amended independent claim 1 as indicated by the Examiners during the interview of July 1, 2026. For at least the reasons set forth above, the Applicant believes that the subject matter of amended independent claim 1 is not taught or suggested by the combination of Hughes and Giftakis and respectfully requests reconsideration of the rejection of claim 1. (07/10/2026 Remarks, pages 7-9)
This argument is persuasive. The amendments to claim 1 now require:
• wherein the motion signals comprise motion information associated with at least an activity level of the patient and respiration of the patient and posture information of the patient associated with at least a posture of the patient
• the contextual biometric information comprising at least the activity level of the patient, the posture of the patient, and the respiration of the patient associated with the arrhythmia event,
• the biometric graphical representation comprising at least a representation illustrating changes of the activity level of the patient, the posture of the patient, and the respiration of the patient during the arrhythmia event,
• an arrhythmia onset graphical indicator corresponding to the onset of the arrhythmia event overlaid on the biometric graphical representation and indicating the onset of the arrhythmia event relative to the activity level, the posture of the patient, and the respiration of the patient,
• indicating the offset of the arrhythmia event relative to the activity level of the patient, the posture of the patient, and the respiration of the patient
As discussed during the 07/01/2026 interview and presented in the 07/10/2026 remarks, the claim now requires a motion sensor to produce contextual information related to activity level, posture, and respiration. Hughes was previously shown to use an accelerometer to produce contextual information for comparison with arrhythmia data (col 14, lines 66-67, col 15, lines 1-42). This 1- to 3-axis accelerometer data is used to compute patient activity level as context for comparison with arrhythmia data (col 14, lines 66-67, col 15, lines 1-21). Hughes additionally discloses the orientation of the patient’s body can be detected from the motion data for comparison with arrhythmia data (col 15, lines 22-33). Giftakis teaches accelerometer information is used to generate a motion signal comprised of patient posture or activity ([0033]) where the accelerometer data is displayed (Fig. 10, [0224]) as contextual information for an ECG waveform which defines an arrhythmia event ([0238-0239]). However, neither Hughes nor Giftakis teaches the motion data includes a respiration waveform. Hughes only discusses a respiratory artifact in the ECG data being used to compute respiration rate (col 15, lines 43-55), rather than from the previously defined motion signal. Therefore, the rejection of independent claim 1 is withdrawn. However, upon further consideration, a new grounds of rejection is made newly in view of Pandia (US 2011/0098583 A1).
Regarding the dependent claims, Applicant argues:
Claims 4, 8-13, 15, 17, 19-21, 43, 57, and 75, depend, either directly or indirectly, from and add further limitations to amended independent claim 1. Therefore, claims 4, 8-13, 15, 17, 19-21, 43, 57, and 75 are believed to be patentable for at least the reasons discussed hereinabove in connection with amended independent claim 1. Reconsideration and withdrawal of the rejection of claims 4, 8-13, 15, 17, 19-21, 43, 57, and 75 are respectfully requested. (07/10/2026 Remarks, page 9)
This argument is persuasive. The rejection of claim 1 was withdrawn so that dependent claims 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 are similarly withdrawn. Claims 21 and 57 are canceled. However, upon further consideration, a new grounds of rejection is made newly in view of Pandia (US 2011/0098583 A1).
Summary: The 35 U.S.C. § 103 rejections of claims 1, 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 are withdrawn. New 35 U.S.C. § 103 rejections newly in view of Pandia are added for claims 1, 4, 8-13, 15, 17, 19-20, 43, 75, and 599.
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 4, 8-13, 15, 17, 19-20, 43, and 75 are rejected under U.S.C 103 as being unpatentable over Hughes (U.S. 9,597,004 B2) in view of Pandia (US 2011/0098583 A1) and Giftakis (U.S. 2011/0245629 A1).
Regarding Claim 1, Hughes discloses a cardiac monitoring system (col 1, lines 27-30) for displaying contextual biometric information for arrhythmia events occurring in a patient (col 14, lines 66-67, col 15, lines 1-21), the device comprising:
• a device configured to be worn by the patient (col 1, lines 47-58), comprising:
• a plurality of ECG electrodes disposed on the device and configured to sense ECG signals of the patient (col 11, lines 31-48);
• and a motion sensor configured to acquire motion signals associated with the patient (col 2, lines 13-27),
- wherein the motion signals comprise motion information associated with at least an activity level of the patient (col 14, lines 66-67, col 15, lines 1-21 – activity level of patient via accelerometer) and posture information of the patient associated with at least a posture of the patient (col 15, lines 22-33 – orientation of patient via accelerometer);
• wherein the device configured to be worn by the patient is further configured to transmit the ECG signals and motion signals to a remote server (col 3, lines 18-61);
• and the remote server in communication with the device configured to be worn by the patient and further in communication with a user interface (col 6, lines 6-29), the remote server comprising:
• a database implemented in a non-transitory media (col 5, lines 36-57, col 36, lines 10-37); and
• a processor in communication with the database (col 6, lines 6-29), the processor configured to:
• store the ECG signals and the motion signals in the database (col 36, lines 17-27),
• identify an arrhythmia event based on the received ECG signals (column 4, lines 17-51 – the ECG signal is used to determine the cardiac rhythm of an ECG segment),
• identify an onset of an arrhythmia event based on the received ECG signals (col 31, lines 12-24), wherein the onset of the arrhythmia event is identified by:
comparing the received ECG signals to at least a first arrhythmia threshold for identifying a heart rate based arrhythmia, a second arrhythmia threshold for identifying tachycardia, and a third arrhythmia threshold for determining atrial fibrillation (col 4, lines 24-29 – “b. Estimating a confidence statistic for each rhythm type based on the inferred frequency and duration of the rhythm across the collection of R-R interval time series for the given user, c. Evaluating if the confidence statistic for each inferred rhythm exceeds a pre-determined threshold value”). Hughes discloses different rhythm types which are tested for, including atrial fibrillation (col 10, lines 21-48) and tachycardia (col 15, lines 3-21) (which are clearly intended as cardiac rhythms to be identified with the device and method in Hughes). Hughes acknowledges the identification of cardiac rhythms based on heart rate changes (col 15, lines 4-7 – “arrhythmias that require observation of less prominent waves (for example P-wave) in addition to rate changes such as Supraventricular Tachycardia pose challenges”);
determining that the arrhythmia event has occurred based on the comparison (col 4, lines 3-32 – the cardiac rhythms are identified by the system once exceeding the ECG comparison’s statistical threshold: “Providing rhythm information back to the calling software only for those inferred rhythms for which the confidence statistic exceeds the threshold value” – lines 30-32); and
determining the onset of the arrhythmia event based on a time of the comparison of the received ECG signals to the at least first arrhythmia threshold, second arrhythmia threshold, and third arrhythmia threshold (col 4, lines 60-64 – “wherein the server is configured to infer the most probable rhythms and their onset/offset times from the R-R interval time series and time stamp, the server configured to filter the most probable rhythms according to a first criteria into a filtered data set” where the threshold previously disclosed in col 4, lines 24-29 is used to confirm the first criteria is met);
• identify an offset of the arrhythmia event based on the received ECG signal (column 4, lines 60-64 – the offset time of the detected rhythm is identified),
• determine, based on the motion signals, contextual biometric information of the patient providing a context for the arrhythmia event (col 14, lines 66-67, col 15, lines 1-42 – the accelerometer readings are collected along with ECG data for comparison during an arrhythmia event to determine what the patient is doing during the event), the contextual biometric information comprising at least the activity level of the patient and the posture of the patient associated with the arrhythmia event (col 15, lines 11-21 – describes patient activity level data being compared to other waveforms; col 15, lines 22-33 – describes orientation data being compared to other waveforms);
• determine an arrhythmia contextual time period around the onset of the
arrhythmia event and the offset of the arrhythmia event (col 4, lines 60-64 – arrhythmia time period with onset and offset determined), the arrhythmia contextual time period comprising a time period during which the onset of the arrhythmia event and the offset of the arrhythmia event occurred (col 14, lines 66-67, col 15, lines 1-42 - accelerometer data being captured during the arrhythmia time period provides contextual information regarding the wearer’s activities during the identified arrhythmia event period),
• generate, based on at least the identified arrhythmia event, the identified onset of the arrhythmia, the identified offset of the arrhythmia event, and the contextual biometric information of the patient for the arrhythmia event, an arrhythmia report (Hughes discloses the reporting of arrhythmia data to the user (col 31, lines 52-59) where the accelerometer data is provided with the arrhythmia data to determine patient activity during the cardiac event (col 15, lines 15-21 – “with motion artifact detection, a single-axis accelerometer measurement optimized to a particular orientation may aid in more specifically determining the activity type such as walking or running. This additional information may help explain symptoms more specifically and thereby affect the subsequent course of therapeutic action”).
Hughes discloses ECG-derived respiration is used as contextual information to determine if events such as sleep apneas have occurred (col 15, lines 43-55). However, Hughes does not disclose the respiration signal as being derived from the motion sensor signal used to produce activity and posture data.
The heart monitor device in Pandia would be considered “reasonably pertinent” (see MPEP 2141.01(a)1) to the claimed arrhythmia detection invention because Pandia teaches a single chest-worn accelerometer used to measure respiration, patient motion, and heart activity ([0150-0151]). Motion signals from the accelerometer are used to track patient activity and positional information, such as a patient falling, where filters can be applied to further isolate heart activity (via heart sounds) and respiration (from chest motions during breathing) ([0154-0157]). Electrocardiogram signals are independently acquired to provide information about the electrical activity of the heart ([0087], [0089]) and used with motion data to infer about a patient’s lifestyle ([0157]). ECG-derived respiration is compared with accelerometer-derived respiration in Figs. 21-22 to demonstrate the agreement of the two methods when producing respiration information ([0170-0172]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the motion sensor (accelerometer) contained within a chest patch in Hughes by incorporating the chest-worn accelerometer which isolates patient activity, respiration, and heart activity from the motion signal in Pandia. This would have been obvious because both Hughes and Pandia discuss the use of motion data as providing contextual information for correlating patient activities with cardiac events. Pandia provides a solution/improvement of a respiration measure (equivalent to ECG-derived respiration, such as that used in Hughes) contained within a single compact motion sensor which can also measure patient activity and heart signals in a minimally-invasive and non-intrusive manner. Therefore, a person of ordinary skill in the art would be motivated to improve the motion sensor of Hughes by incorporating the chest-worn accelerometer which isolates patient activity, respiration, and heart activity from the motion signal in Pandia.
Hughes generally discloses the use of a display without disclosing the specific features of the user interface (col 35, lines 57-62). Therefore, Hughes does not disclose the generation of graphical elements as part of an arrhythmia report, the graphical elements to be displayed comprising:
• a graphical timeline comprising the arrhythmia contextual time period,
• a biometric graphical representation of the contextual biometric information of the patient during the arrhythmia contextual time period the biometric graphical representation comprising at least a representation illustrating changes of the activity level of the patient the posture of the
patient, and the respiration of the patient during the arrhythmia event,
• an arrhythmia onset graphical indicator corresponding to the onset of the arrhythmia event overlaid on the biometric graphical representation and indicating the onset of the arrhythmia event relative to the activity level of the patient, the posture of the patient, and the respiration of the patient, and
• an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation and indicating the offset of the arrhythmia event relative to the activity level of the patient, the posture of the patient, and the respiration of the patient.
Giftakis, in the same field of endeavor of acquiring contextual data during medical events ([0002]), such as an arrhythmia ([0238-0239]), teaches a display (Fig. 10, [0230-0231]) which temporally matches physiological waveforms, from which a physiologic event is calculated, and corresponding biometric waveforms or indicators, such as motion and posture ([0233]). Figure 10 shows graphical timelines of ECG (164), EEG (76), accelerometer data (78), and an interpretation of posture based on accelerometer data (80) temporally aligned to discern contextual information between the waveforms ([0230-0231]). In Figure 10, seizure activity is identified over a specified region of the EEG data (166) and arrhythmia activity is identified over a specified region of the ECG data (170). The processor can automatically or the user can manually identify areas of interest based on physiologic events using visual tools such as window 88 to highlight the relevant segment of the graphical timelines (Fig. 5, [0147-0148], [0157], [0185]). The screens in Figures 5 and 10 demonstrate different features where Figure 10 adds the cardiac signal, but the features are not interpreted as incompatible ([0224]) where the sliding feature 88 shown in Fig. 5 can be applied to highlight a variety of displayed signals ([0157]). Giftakis teaches the identification of a data segment indicating an arrhythmia and is being compared to contextual motion data ([0239]). Given sliding window 88 has been established as highlighting different types of data sets in [0157], the sliding window would be applicable to the ECG data segment (meaning it has a beginning and end) as well.
Giftakis states: “The display of the temporal correlation between the bioelectrical brain signal and the patient posture indicator, may allow a user to visually ascertain the physiological activity of a patient during seizures, which can be useful for identifying portions of the bioelectrical brain signal that are relevant to the occurrence of a particular type of seizure… differentiating between different types of seizures may be useful for patient monitoring and evaluation, as well as medical device programming” ([0029]). Giftakis also applies this analysis to ECG data to detect arrhythmias ([0238-0239]).
The display system in Giftakis is oriented toward displaying biometric information relative to a physiologic electrical event (in this case seizure event identification occurs from an EEG signal, [0122], and arrhythmia event identification occurs from an ECG signal, [0238-0239]). Regarding use of a motion sensor, Hughes states: “an electronic device for monitoring physiological systems may comprise a measuring instrument configured to detect motion signals in at least one axis. This measuring instrument may be an accelerometer that can be configured to detect motion signals in three axes” (col 2, lines 22-27). The motion sensor in Giftakis “may include a sensing module (also referred to as a sensor) that generates a signal indicative of patient motion (e.g., patient posture and/or activity), such as one or more two-axis or three-axis accelerometers, piezoelectric crystals, or pressure transducers” ([0033] – patient activity data generated from the motion sensor). Therefore, both Hughes and Giftakis collect ECG signals and raw motion data in a similar format using one-, two-, or three-axis accelerometers for assessing patient activity.
Hughes also does not specifically disclose bradycardia as an arrhythmia to be identified. Giftakis, when discussing how to identify the presence of arrhythmias, includes bradycardia as a typical arrhythmia which would be evaluated ([0221]). Given Hughes, as previously discussed, tests for heart rate arrhythmias and explicitly identifies tachycardia, it would be reasonable that bradycardia could and would be evaluated as well.
It would have been obvious to a person of ordinary skill in the art to combine the arrhythmia detector and generated waveforms in Hughes with the display in Giftakis. The display in Giftakis allows the user a visual medium for the user to more readily identify the relationship between physiologic events and biometric information. In this case, the only difference between the claimed invention and the prior art is the lack of actual combination of the elements in a single prior art reference. One of ordinary skill in the art could have combined the elements as claimed by known methods (the outputs of Hughes match the inputs being displayed on the graphical interface in Giftakis) and that in combination, each element merely performs the same function as it does separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding Claim 4, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the processor is configured to determine, based on the motion signals, the contextual biometric information of the patient for the arrhythmia event during the arrhythmia contextual time period (col 15, lines 11-21 – describes patient activity level data being compared to other waveforms; col 15, lines 22-33 – describes orientation data being compared to other waveforms; col 15, lines 43-55 – ECG-derived respiration to determine when respiratory events like sleep apnea occur). As stated in claim 1, the proposed combination with Pandia discloses an accelerometer-derived respiration which has been shown to be comparable to ECG-derived respiration ([0157]).
Regarding Claim 8, the cardiac monitoring system according to Claim 4 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the arrhythmia contextual time period comprises at least one of: a two-hour time period during which the onset of the arrhythmia event occurred, an hour-time period during which the onset of the arrhythmia event occurred, a 30-minute time period during which the onset of the arrhythmia event occurred, a 15-minute time period during which the onset of the arrhythmia event occurred, or a 10-minute time period during which the onset of the arrhythmia event occurred (col 29, lines 9-36). Hughes teaches “features are extracted on a windowed basis, with the window size varying for example between 1 hour or multiple hours to a few seconds” (col 29, lines 18-20).
Regarding Claim 9, the cardiac monitoring system according to Claim 4 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes does not disclose the arrhythmia contextual time period is user-specified via the user interface.
As stated in claim 1, the proposed combination with Giftakis yields a user selecting a portion of the patient data to be viewed in greater detail ([0157], [0160]). Giftakis states “in some examples, a user, instead of or in addition to processor may identify the particular segment of patient data, e.g. by moving sliding window to highlight the particular segment of patient data” ([0157]). In another embodiment, the “user may be able to enter a particular date and/or time or range of dates and/or times that are of interest and user interface can display the data that corresponds to the particular dates and/ or times upon the request of the user” [[0160]). Further, user-defined time periods allow for “more detailed patient data for the particular patient data of interest” ([0157]).
Regarding Claim 10, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the device configured to be worn by the patient comprises:
• a patch (col 7, lines 51-53), the plurality of ECG electrodes disposed on the patch (col 11, lines 31-48); and
• a sensor unit configured to be removably attached to the patch and in electrical communication with the plurality of ECG electrodes (col 9, lines 2-13), the sensor unit comprising the motion sensor (col 14, lines 51-67, col 15, lines 1-42).
Regarding Claim 11, the cardiac monitoring system according to Claim 10 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses wherein the patch is an adhesive patch configured to be adhesively coupled to skin of the patient (col 16, lines 49-60, col 18, lines 3-26).
Regarding Claim 12, the cardiac monitoring system according to Claim 11 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the adhesive patch is disposable (col 9, lines 2-13, col 23, lines 29-38). According to these passages in Hughes, the patch is only worn for a specified amount of time (typically 2-3 weeks) before removal.
Regarding Claim 13, the cardiac monitoring system according to Claim 11 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the adhesive patch is configured to be continuously adhesively coupled to the skin of the patient for at least one of: 3-5 days, 5-7 days, 7-10 days, 10-14 days, or 14-30 days. Hughes teaches the device can be attached for “as many as 14-21 days or more” (col 17, lines 21-30) and “the system fundamentally allows a device worn for up to about: 14, 21, or 30 days or beyond without battery recharging or replacement” (col 27, lines 62-67).
Hughes establishes the device can be used 30 days or more without replacement (i.e. not breaking skin contact). While not directly suggesting each interval in the instant claim, the 30 day or more statement in Hughes establishes the device could be removed any time before 30 days. MPEP 2144.05 states “In the case where the claimed ranges ‘overlap or lie inside ranges disclosed by the prior art’ a prima facie case of obviousness exists.” There is no evidence of an “unexpected result or criticality” on the analysis from the discussed range interpretations.
Regarding Claim 15, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the plurality of ECG electrodes are configured to be disposed at predetermined anatomical locations on the patient's body (col 22, lines 55-67, col 23, lines 1-18), and wherein the motion sensor comprises one or more accelerometers at least one of mechanical coupled or electrically coupled to one or more of the plurality of ECG electrodes (col 14, lines 28-67, col 15, lines 1-42 – accelerometer part of patch structure which contains ECG electrodes).
Regarding Claim 17, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses a portable gateway configured to transmit the sensed ECG signals and acquired motion signals to the remote server (col 27 lines 36-61 - server description with ECG - and col 14, lines 66-67, col 15, lines 1-42 – motion data collection). As stated in claim 1, the proposed combination with Pandia discloses an accelerometer-derived respiration signal which has been shown to be comparable to ECG-derived respiration ([0157]).
Regarding Claim 19, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the arrhythmia event comprises at least one of ventricular tachycardia, bigeminy, a supraventricular ectopic beat, supraventricular tachycardia, atrial fibrillation, ventricular fibrillation, a pause, a 2nd AV block, a 3rd AV block, bradycardia, or non-ventricular tachycardia (col 31, lines 12-24).
Regarding Claim 20, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the user interface comprises a desktop computer, a laptop computer, or a portable personal digital assistant (col 35, lines 12-53). Note Hughes teaches a user-interface on these devices, but that interface does not explicitly include the display of overlaid contextual biometric information. The same recitation of a desktop computer, a laptop computer, or a portable personal digital assistant can be observed in Giftakis for its data display overlay ([0075]-[0076]).
Regarding Claim 43, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses wherein the contextual biometric information further comprises heart rate recovery of the patient (col 14, lines 66-67, col 15, lines 1-21). Hughes does not use the term “heart rate recovery.” This term is interpreted as the difference between peak heart rate during exercise and heart rate a designated time period after exercise cessation. Hughes does mention “if a sudden surge in the patient's activity level is detected at the same time as the increase in heart rate. Broadly speaking, the provision of activity information to clinical professionals may help them discriminate between exercise-induced arrhythmia versus not” (col 15, lines 11-15). This excerpt establishes the ability of Hughes to associate higher activity levels (such as during exercise) with altered heart rates.
Regarding Claim 75, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses the contextual biometric information further comprises a sleep status of the patient (col 15, lines 43-48).
Claim 599 is rejected under U.S.C 103 as being unpatentable over Hughes (U.S. 9,597,004 B2) in view of Pandia (US 2011/0098583 A1), Giftakis (U.S. 2011/0245629 A1), and Cao (U.S. 2016/0213275 A1).
Regarding Claim 599, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Pandia and Giftakis, as indicated hereinabove. Hughes further discloses comparing the received ECG signals to the third arrhythmia threshold for determining atrial fibrillation (col 10, lines 21-48 – atrial fibrillation intended as cardiac rhythm to be tested for) comprises determining R-R intervals between successive R-waves of the received ECG signals (col 25, lines 25-29 – “The R-R interval time series 902 inputted to the system may include a series of measurements of the timing interval between successive heartbeats. Typically each interval represents the time period between two successive R peaks as identified from an ECG signal”). Hughes does not explicitly disclose determining a difference between successive R-R intervals as ΔR-R values, and detecting atrial fibrillation based on an analysis of sequences of R-R intervals and ΔR-R values.
Cao, in the same field of endeavor of arrhythmia detection ([0002]), teaches using the differences between R-R intervals in the R-R interval time series to determine if an arrhythmia has occured ([0041]), specifically atrial fibrillation ([0040] – “in order to determine whether an atrial fibrillation event is occurring, the device may plot RR intervals between determined sensed R-waves using a Lorentz scatter plot and make the decision as to whether an atrial fibrillation event is occurring based on the resulting interval differences determined from the plotted intervals”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Hughes’s evaluation of R-R interval times for detecting atrial fibrillation by incorporating the technique using differences between R-R intervals in Cao. This would have been obvious because both Hughes and Cao discuss detection of atrial fibrillation using R-R interval data and Cao provides a solution/improvement which incorporates heart rate variability as a factor in statistical detection of atrial fibrillation. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Hughes by incorporating the technique using differences between R-R intervals in Cao.
Conclusions
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Schmitt, whose telephone number is 703-756-1345. The examiner can normally be reached on Monday-Friday from 9:00 am to 5:00 pm.
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/Benjamin A. Schmitt/
Examiner
Art Unit 3796
/LYNSEY C Eiseman/Primary Examiner, Art Unit 3796
Prosecution Insights