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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/30/2026 has been entered.
Status of Claims
Claims 1, 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 are currently pending and under examination. Claims 2-3, 5-7, 14, 16, 18, 22-42, 44-56, 58-74, and 76-598 are canceled. As per the amendments filed on 01/30/2026, claims 1 and 4 are amended.
Response to Arguments
Applicant’s arguments, see Remarks pages 7-10 (Prior Art Rejection), filed 01/30/2026, with respect to the 35 U.S.C. § 103 rejection of claim 1 over Hughes (U.S. 9,597,004 B2) in view of Giftakis (U.S. PG Pub 2011/0245629 A1) have been fully considered. Regarding Claim 1, Applicant argues:
In addition, the combination of Hughes and Giftakis fails to teach or suggest at least one processor configured to "determine an arrhythmia contextual time period around the onset of the arrhythmia event and the offset of the arrhythmia event, 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" as required by amended independent claim 1, in combination with the other claimed features.
The Office Action relies on the description in Hughes of a "sensor transmit[ing] the full resolution cardiac signal to the server for a time period surrounding each of the filtered events" (see column 5, lines 1-3 of Hughes) as corresponding to the claimed determining of an arrhythmia contextual time period. However, this description in Hughes provides no teaching or suggestion of "determining 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 compris[es] a time period during which the onset of the arrhythmia event and the offset of the arrhythmia event occurred" as required by amended independent claim 1. 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. (pages 9-10, 01/30/2026 Remarks)
This argument is not persuasive. The term “offset” in this case in interpreted as the end time of the identified arrhythmia period (Specification, [00258-00260]) where changes to ECG rhythms are used to make the determination (Specification, [00251]). The amended limitations discussing an offset are:
Claim 1
• identify an offset of the arrhythmia event based on the received ECG signal,
• determine an arrhythmia contextual time period around the onset of the arrhythmia event and the offset of the arrhythmia event, 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,
• 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
As discussed in the 01/30/2026 Remarks, Hughes discloses “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” (col 4, lines 60-64) and accelerometer data being captured during the arrhythmia time period to provide contextual information regarding the wearer’s activities during the arrhythmia event (col 14, lines 66-67, col 15, lines 1-42). Hughes discloses reporting arrhythmia data to the user (col 31, lines 52-59). Therefore, Hughes discloses the three limitations presented above.
Applicant further argues:
The combination of Hughes and Giftakis is not understood to teach or suggest all of the features required by amended independent claim 1. For example, the combination of Hughes and Giftakis fails to teach or suggest at least one processor configured to "identify an offset of the arrhythmia event based on the received ECG signals" and generate an arrhythmia report comprising at least "an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation" as required by amended independent claim 1, in combination with the other claimed features.
While Hughes mentions that the server may "infer the most probable rhythms and their onset/offset times from the R-R interval time series and time stamp" (see column 4, lines 60-64 of Hughes), there is no teaching or suggestion in Hughes of providing both an arrhythmia onset graphical indicator corresponding to the onset of the arrhythmia event overlaid on the biometric graphical representation and an arrhythmia off set graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation as required by amended independent claim 1. Giftakis does not cure this deficiency. Giftakis discloses that "a user may control the motion of a sliding window 88" (see paragraph [0148] of Giftakis, for example). However, there is no teaching or suggestion that this sliding window corresponds to either arrhythmia onset or an arrhythmia offset. Accordingly, Hughes, whether considered alone or in combination with Giftakis, is not understood to teach or suggest at least one processor configured to "identify an offset of the arrhythmia event based on the received ECG signals" and generate an arrhythmia report comprising at least "an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation" as required by amended independent claim 1. (page 9, 01/30/2026 Remarks)
This argument is not persuasive. The amended limitation discussing a graphical indicator of an offset is:
Claim 1
• an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation
12. Hughes generically discloses the use of a display for displaying data without disclosing specifics about the user interface (col 35, lines 57-62). However, Giftakis is relied upon to provide a specific user interface as discussed in the 10/30/2025 final rejection (pages 8-10). Therefore, Giftakis will be examined with respect to the above amended limitation.
Giftakis teaches the identification of an arrhythmia segment 170 showing a beginning and an end of the segment (Fig. 10, [0239]). The screens in Figures 5 and 10 demonstrate different features where Figure 10 adds the cardiac signal, but the features are not presented as incompatible ([0224]).
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Giftakis – Comparison of Figures 5 and 10 showing similarities between interfaces.
Giftakis teaches a data segment (having a beginning and end) of interest can be highlighted on the interface via sliding window 88:
In some examples, graphical user interface 67 may include a feature that allows a user to view a selected portion of patient data in greater detail. For example, processor 60 may identify a segment of patient data that includes a particular event of interest, e.g., seizure activity within bioelectrical brain signal 76 or a behavioral event illustrated by particular patient posture indicators 80, and highlight the segment of patient data, e.g., via sliding window 88. ([0157])
Sliding window 88’s position can either be set automatically by the processor ([0147]) or manually by the user ([0148]). With respect to the cardiac signal, Giftakis teaches:
If processor 60 determines that the segment of cardiac signal 164 that temporally corresponds to the behavioral event is indicative of an arrhythmia within the heart of patient 12, processor 60 identifies the portion of the cardiac signal 164 indicating the arrhythmia (178). In the example illustrated in FIG. 10, for example, processor 60 may identify segment 170 of cardiac signal 164 that is indicative of arrhythmia and that at least partially temporally corresponds to segment 165 of patient motion signal 76 and/or posture state indicators 80B, SOC. Segment 170 of cardiac signal 164 can be, for example, a sub-segment of the segment of cardiac signal 164 that is temporally correlated to the behavioral event detected based on patient motion signal 78 or patient posture indicators 80. ([0239])
Based on the above, Giftakis teaches the identification of a data segment indicating an arrhythmia which is associated with contextual motion data. 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. This interface in Giftakis combined with the system in Hughes would display ECG (with onset/offset of arrhythmia periods included) and motion signals to visually provide and highlight contextual information. Therefore, the rejection of claim 1 is maintained.
Regarding the claims depending on claim 1, Applicant argues:
Claims 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599, 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, 75, and 599 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, 75, are respectfully requested. (page 10, 01/30/2026 Remarks)
This argument is not persuasive. The rejection of claim 1 was maintained and the rejections of dependent claims 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 would similarly be maintained.
Summary: The 35 U.S.C. § 103 rejections of claims 1, 4, 8-13, 15, 17, 19-21, 43, 57, 75, and 599 are maintained.
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, 19-21, 43, 57, and 75 are rejected under U.S.C 103 as being unpatentable over Hughes (U.S. 9,597,004 B2, see previously cited) in view of Giftakis (U.S. PG Pub 2011/0245629 A1, cited on 01/16/2024 IDS).
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 at least one of motion information or posture information of the patient (col 14, lines 66-67, col 15, lines 1-42);
• 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 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), the contextual biometric information comprising at least an activity level of the patient (col 15, lines 11-21 – describes patient activity level 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 the use of a display without disclosing the contents of the user interface (col 35, lines 57-62). 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, an arrhythmia onset graphical indicator corresponding to the onset of the arrhythmia event overlaid on the biometric graphical representation, and an arrhythmia offset graphical indicator corresponding to the offset of the arrhythmia event overlaid on the biometric graphical representation. Hughes also does not specifically disclose bradycardia as an arrhythmia to be identified.
Giftakis, in the same field of endeavor of acquiring contextual data during medical events ([0002]), 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.
Giftakis, when discussing how to identify the presence of arrhythmias, includes bradycardia as a typical arrhythmia which would be evaluated ([0221] – “In other examples, syncope can be triggered by a cardiac arrhythmia, such as bradycardia, tachycardia, etc., that may lead to a fall event. In some examples, syncope associated with a neurocardiogenic syndrome can also lead to a seizure”). 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.
Therefore, Claim 1 is obvious over Hughes in view of Giftakis.
Regarding Claim 4, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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 14, lines 66-67, col 15, lines 1-42).
Therefore, Claim 4 is obvious over Hughes in view of Giftakis.
Regarding Claim 8, the cardiac monitoring system according to Claim 4 is obvious over Hughes in view of 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). 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. A person of ordinary skill in the art would have been motivated to use a wide array of window times based on case-by-case monitoring considerations.
Therefore, Claim 8 is obvious over Hughes in view of Giftakis.
Regarding Claim 9, the cardiac monitoring system according to Claim 4 is obvious over Hughes in view of Giftakis, as indicated hereinabove. Hughes does not disclose the arrhythmia contextual time period is user-specified via the user interface.
Giftakis, in the same field of endeavor of acquiring contextual data during medical events ([0002]), teaches a user can select 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]).
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 medium 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.
Therefore, Claim 9 is obvious over Hughes in view of Giftakis.
Regarding Claim 10, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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).
Therefore, Claim 10 is obvious over Hughes in view of Giftakis.
Regarding Claim 11, the cardiac monitoring system according to Claim 10 is obvious over Hughes in view of 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).
Therefore, Claim 11 is obvious over Hughes in view of Giftakis.
Regarding Claim 12, the cardiac monitoring system according to Claim 11 is obvious over Hughes in view of 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.
Therefore, Claim 12 is obvious over Hughes in view of Giftakis.
Regarding Claim 13, the cardiac monitoring system according to Claim 11 is obvious over Hughes in view of 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.
Therefore, Claim 13 is obvious over Hughes in view of Giftakis.
Regarding Claim 15, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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).
Therefore, Claim 15 is obvious over Hughes in view of Giftakis.
Regarding Claim 17, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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).
Therefore, Claim 17 is obvious over Hughes in view of Giftakis.
Regarding Claim 19, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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).
Therefore, Claim 19 is obvious over Hughes in view of Giftakis.
Regarding Claim 20, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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 overlaid data display ([0075]-[0076]).
Therefore, Claim 20 is obvious over Hughes in view of Giftakis.
Regarding Claim 21, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Giftakis, as indicated hereinabove. Hughes further discloses the contextual biometric information further comprises a posture of the patient (col 15, lines 22-33).
Therefore, Claim 21 is obvious over Hughes in view of Giftakis.
Regarding Claim 43, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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.
Therefore, Claim 43 is obvious over Hughes in view of Giftakis.
Regarding Claim 57, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Giftakis, as indicated hereinabove. Hughes further discloses the contextual biometric information further comprises respiration of the patient (col 15, lines 48-55).
Therefore, Claim 57 is obvious over Hughes in view of Giftakis.
Regarding Claim 75, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of Giftakis, as indicated hereinabove. Hughes further discloses the contextual biometric information further comprises a sleep status of the patient (col 15, lines 43-48).
Therefore, Claim 75 is obvious over Hughes in view of Giftakis.
Claim 599 is rejected under U.S.C 103 as being unpatentable over Hughes (U.S. 9,597,004 B2, see previously cited) in view of Giftakis (U.S. PG Pub 2011/0245629 A1, cited on 01/16/2024 IDS) and Cao (U.S. PG Pub 2016/0213275 A1, see previously cited).
Regarding Claim 599, the cardiac monitoring system according to Claim 1 is obvious over Hughes in view of 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 compute an arrhythmia ([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.
Therefore, Claim 599 is obvious over Hughes in view of Giftakis and Cao.
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 8:30 am to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached on 571-270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Benjamin A. Schmitt/
Examiner
Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
Prosecution Insights