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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/22/2025 has been entered.
Response to Arguments
The amendments and remarks filed 12/22/2025 are considered unpersuasive by the Examiner.
On p. 11 of the Remarks filed 12/22/2025, Applicant states that the teachings of Kalidas do not comprise a sorting operation including a noise criterion applied to ECG signal values and summarizes the requirement of this claim limitation as necessitating “applying a noise criterion to classify or sort an entire record as clean or noisy”.
This is not found persuasive as nothing in the claims require any “entire record” to be evaluated. Applicant has claimed only that the criterion is applied to any number of ECG signal values, and the Examiner’s position is that this is at least two ECG signal values, and does not require any “entire record” of any particular length.
Further on p. 12 of the Remarks filed 12/22/2025, Applicant states that “the electrode motion noise of Kalidas is not the same as noise criterion applied to the values of the ECG signal,” but does not further elaborate on the perceived distinction between these two concepts.
The electrode motion noise of Kalidas is noise in the obtained signal that is caused by electrode motion. The noise criterion of Kalidas is to sort the ECG signal values as noisy, clean, or to-be-discarded based on detected noise in the ECG signal that is caused by electrode motion. Therefore, this appears to be unpersuasive.
Therefore, the previous grounds for rejection are maintained.
However, in the interest of moving prosecution forward, new grounds for rejection are additionally presented in view of Firoozabadi et al. (U.S. Patent Application Publication No. 2018/0242872).
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.
Claim(s) 1-4, 7, 51-54, 57, 76-79, 82, and 100-102 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (U.S. Patent Application Publication No. 2019/0209853) hereinafter referred to as Kim; in view of Kalidas et al. (U.S. Patent Application Publication No. 2022/0015711) hereinafter referred to as Kalidas.
Regarding claim 1, Kim teaches a pre-analyzing computer system for pre-analyzing and characterizing a data record of a parameter of a patient (¶[0065] available to external clients…for later review by external users), the data record created by a wearable medical system (WMS) worn by the patient (¶[0027] ambulatory medical device), the pre-analyzing computer system comprising:
the WMS worn by the patient comprises:
an energy storage module configured to store an electrical charge (¶[0047]),
an electrode (¶[0031], ¶[0048] electrodes),
a support structure configured to be worn by the patient so as to maintain the electrode on a body of the patient (¶[0030] support structure),
a sensor configured to sense a parameter of the patient, the parameter including an Electrocardiogram (ECG) signal of the patient (¶[0041] electrodes to detect ECG data),
a measurement circuit configured to render a patient input responsive to the sensed parameter, the patient input including values for the ECG signal (¶[0039], ¶[0051], ¶[0054]), and
a WMS processor (¶[0054] processor) configured to:
determine, from the patient input, whether or not a shock criterion is met (¶[0041] detecting…whether the patient is in need of a shock, ¶[0055]), and cause, responsive to the shock criterion being met, at least some of the stored electrical charge to be discharged via the electrode through the patient while the support structure is worn by the ambulatory patient so as to deliver a shock to the patient (¶[0056]),
detect, from the patient input, when an alert criterion is met (¶[0089]),
capture at least some of the values of the ECG signal when the alert criterion is met (¶[0045] records of episodes and intervention, ¶[0065]),
create the data record so that the data record is a standalone computer file and has as contents at least some of the captured values (¶[0065] create a record), and
cause the data record to be transmitted to the pre-analyzing computer system (¶[0065] available to external clients…for later review by external users),
Kim does not teach the pre-analyzing computer system including at least:
one or more pre-analyzing computer system processors distinct from the WMS processor and not controlled by the WMS processor; and
a non-transitory computer- readable pre-analyzing storage medium having stored thereon instructions which, when executed by the one or more computer pre-analyzing computer system processors, result in operations including at least:
receiving, by the pre-analyzing computer system, the data record that has been caused to be transmitted;
parsing the contents of the received data record;
applying a sorting criterion to the parsed contents to determine a given score for the data record, the given score being one of a set including at least a first score and a second score, wherein the sorting criterion comprises a noise criterion applied to the values of the ECG signal;
performing a characterizing action with reference to the data record responsive to the given score being the first score, and not performing the characterizing action with reference to the data record responsive to the given score being the second score, and
performing an alternative action with reference to the data record responsive to the given score being the second score, and not performing the alternative action with reference to the data record responsive to the given score being the first score, the alternative action being different from the characterizing action.
Attention is brought to the Kalidas reference, which teaches a pre-analyzing computer system (¶[0044] server) including at least:
one or more pre-analyzing computer system processors distinct from the WMS processor and not controlled by the WMS processor (¶[0044] remote server, including operating on the cloud); and
a non-transitory computer- readable pre-analyzing storage medium having stored thereon instructions which, when executed by the one or more computer pre-analyzing computer system processors (¶[0044] server, ¶[0201] memory), result in operations including at least:
receiving, by the pre-analyzing computer system, a data record that has been caused to be transmitted (¶[0045]);
parsing the contents of the received data record (¶[0045] results review component);
applying a sorting criterion to the parsed contents to determine a given score for the data record (¶[0070] scores), the given score being one of a set including at least a first score and a second score (¶[0065] signal quality assessment step, scores include more or less than 0.5 and exceeding 0.9), wherein the sorting criterion comprises a noise criterion (¶[0070] the scores represent a noise criterion) applied to the values of the ECG signal (¶[0064] applied to the denoised ECG signal, which comprises ECG signal values, Fig. 2 and 8 shows a flowchart for this application to the obtained signal values);
performing a characterizing action with reference to the data record responsive to the given score being the first score (¶[0065] high noise segments are suppressed), and not performing the characterizing action with reference to the data record responsive to the given score being the second score (¶[0065] high quality segments are not suppressed, ¶[0070]); and
performing an alternative action (Fig. 8, decision step element “EM noise score <=0.9” with YES or NO alternative paths, the YES path considered to be the alternative action) with reference to the data record responsive to the given score being the second score (¶[0065] more or less than 0.5 and not exceeding 0.9), and not performing the alternative action with reference to the data record responsive to the given score being the first score (¶[0096] omitting steps for missed beat detection, false beat removal, and threshold update phase), the alternative action being different from the characterizing action (¶[0096] these omitted steps differ from ¶[0065] suppressing high noise segments).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable patient monitor of Kim to include robust noise classification, as taught by Kalidas, because Kalidas teaches that it is “imperative that beat detection algorithms and related processes are extremely robust to noise without compromising on detection accuracy, especially under arrhythmic conditions” (Kalidas ¶[0072]).
Regarding claim 2, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the set includes exactly two scores (¶[0070] more or less than 0.5).
Regarding claim 3, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the set includes three or more scores (¶[0070] more than 0.5, less than 0.5 and greater than 0.9.
Regarding claim 4, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the sorting criterion has been trained by artificial intelligence training from other data records to which scores were assigned previously (¶[0069] noise classification model).
Regarding claim 7, Kim as modified teaches the pre-analyzing computer system of claim 6.
Kalidas further teaches wherein the noise criterion includes a High-Frequency noise criterion (¶[0041], ¶[0053], ¶[0127]).
Regarding claims 51-54, 57, 76-79, and 82, the claims are directed to a method and computing system comprising substantially the same subject matter as claims 1-4 and 7 and are rejected under substantially the same sections of Kim and Kalidas.
Regarding claims 100-102, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kim further teaches wherein the WMS further comprises a motion detector configured to detect motion event data indicative of a change in posture of the patient from a baseline posture (¶[0043], ¶[0069] posture change detector).
Claim(s) 1-4, 7, 51-54, 57, 76-79, 82, and 100-102 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (U.S. Patent Application Publication No. 2019/0209853) hereinafter referred to as Kim; in view of Kalidas et al. (U.S. Patent Application Publication No. 2022/0015711) hereinafter referred to as Kalidas; in view of Firoozabadi et al. (U.S. Patent Application Publication No. 2018/0242872) hereinafter referred to as Firoozabadi.
Regarding claim 1, Kim teaches a pre-analyzing computer system for pre-analyzing and characterizing a data record of a parameter of a patient (¶[0065] available to external clients…for later review by external users), the data record created by a wearable medical system (WMS) worn by the patient (¶[0027] ambulatory medical device), the pre-analyzing computer system comprising:
the WMS worn by the patient comprises:
an energy storage module configured to store an electrical charge (¶[0047]),
an electrode (¶[0031], ¶[0048] electrodes),
a support structure configured to be worn by the patient so as to maintain the electrode on a body of the patient (¶[0030] support structure),
a sensor configured to sense a parameter of the patient, the parameter including an Electrocardiogram (ECG) signal of the patient (¶[0041] electrodes to detect ECG data),
a measurement circuit configured to render a patient input responsive to the sensed parameter, the patient input including values for the ECG signal (¶[0039], ¶[0051], ¶[0054]), and
a WMS processor (¶[0054] processor) configured to:
determine, from the patient input, whether or not a shock criterion is met (¶[0041] detecting…whether the patient is in need of a shock, ¶[0055]), and cause, responsive to the shock criterion being met, at least some of the stored electrical charge to be discharged via the electrode through the patient while the support structure is worn by the ambulatory patient so as to deliver a shock to the patient (¶[0056]),
detect, from the patient input, when an alert criterion is met (¶[0089]),
capture at least some of the values of the ECG signal when the alert criterion is met (¶[0045] records of episodes and intervention, ¶[0065]),
create the data record so that the data record is a standalone computer file and has as contents at least some of the captured values (¶[0065] create a record), and
cause the data record to be transmitted to the pre-analyzing computer system (¶[0065] available to external clients…for later review by external users),
Kim does not teach the pre-analyzing computer system including at least:
one or more pre-analyzing computer system processors distinct from the WMS processor and not controlled by the WMS processor; and
a non-transitory computer- readable pre-analyzing storage medium having stored thereon instructions which, when executed by the one or more computer pre-analyzing computer system processors, result in operations including at least:
receiving, by the pre-analyzing computer system, the data record that has been caused to be transmitted;
parsing the contents of the received data record;
applying a sorting criterion to the parsed contents to determine a given score for the data record, the given score being one of a set including at least a first score and a second score, wherein the sorting criterion comprises a noise criterion applied to the values of the ECG signal;
performing a characterizing action with reference to the data record responsive to the given score being the first score, and not performing the characterizing action with reference to the data record responsive to the given score being the second score, and
performing an alternative action with reference to the data record responsive to the given score being the second score, and not performing the alternative action with reference to the data record responsive to the given score being the first score, the alternative action being different from the characterizing action.
Attention is brought to the Kalidas reference, which teaches a pre-analyzing computer system (¶[0044] server) including at least:
one or more pre-analyzing computer system processors distinct from the WMS processor and not controlled by the WMS processor (¶[0044] remote server, including operating on the cloud); and
a non-transitory computer- readable pre-analyzing storage medium having stored thereon instructions which, when executed by the one or more computer pre-analyzing computer system processors (¶[0044] server, ¶[0201] memory), result in operations including at least:
receiving, by the pre-analyzing computer system, a data record that has been caused to be transmitted (¶[0045]);
parsing the contents of the received data record (¶[0045] results review component);
applying a sorting criterion to the parsed contents to determine a given score for the data record (¶[0070] scores), the given score being one of a set including at least a first score and a second score (¶[0065] signal quality assessment step, scores include more or less than 0.5 and exceeding 0.9), wherein the sorting criterion comprises a noise criterion (¶[0070] the scores represent a noise criterion) applied to the values of the ECG signal (¶[0064] applied to the denoised ECG signal, which comprises ECG signal values, Fig. 2 and 8 shows a flowchart for this application to the obtained signal values);
performing a characterizing action with reference to the data record responsive to the given score being the first score (¶[0065] high noise segments are suppressed), and not performing the characterizing action with reference to the data record responsive to the given score being the second score (¶[0065] high quality segments are not suppressed, ¶[0070]); and
performing an alternative action (Fig. 8, decision step element “EM noise score <=0.9” with YES or NO alternative paths, the YES path considered to be the alternative action) with reference to the data record responsive to the given score being the second score (¶[0065] more or less than 0.5 and not exceeding 0.9), and not performing the alternative action with reference to the data record responsive to the given score being the first score (¶[0096] omitting steps for missed beat detection, false beat removal, and threshold update phase), the alternative action being different from the characterizing action (¶[0096] these omitted steps differ from ¶[0065] suppressing high noise segments).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable patient monitor of Kim to include robust noise classification, as taught by Kalidas, because Kalidas teaches that it is “imperative that beat detection algorithms and related processes are extremely robust to noise without compromising on detection accuracy, especially under arrhythmic conditions” (Kalidas ¶[0072]).
The Examiner’s position is that Kalidas teaches applying a sorting criterion to the parsed contents to determine a given score for the data record, the given score being one of a set including at least a first score and a second score, wherein the sorting criterion comprises a noise criterion applied to the values of the ECG signal based on the teachings of Kim and Kallidas.
However, in case Applicant disagrees with the BRI of the rejection and to move prosecution forward, attention is drawn to the Firoozabadi reference, which teaches applying a sorting criterion to the parsed contents to determine a given score for the data record, the given score being one of a set including at least a first score and a second score, wherein the sorting criterion comprises a noise criterion applied to the values of the ECG signal (¶[0073], ¶¶[0075-0090], HFN is high-frequency noise, LFN is low-frequency noise).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the computer system of Kim as modified to include additional specificity in noise sorting criteria, as taught by Firoozabadi, because Firoozabadi teaches a beneficial “novel and unique evaluating” of high-frequency and low-frequency noise levels within ECG segments (Firoozabadi ¶[0096]).
Regarding claim 2, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the set includes exactly two scores (¶[0070] more or less than 0.5).
Regarding claim 3, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the set includes three or more scores (¶[0070] more than 0.5, less than 0.5 and greater than 0.9.
Regarding claim 4, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kalidas further teaches wherein the sorting criterion has been trained by artificial intelligence training from other data records to which scores were assigned previously (¶[0069] noise classification model).
Regarding claim 7, Kim as modified teaches the pre-analyzing computer system of claim 6.
Kalidas further teaches wherein the noise criterion includes a High-Frequency noise criterion (¶[0041], ¶[0053], ¶[0127]).
Regarding claims 51-54, 57, 76-79, and 82, the claims are directed to a method and computing system comprising substantially the same subject matter as claims 1-4 and 7 and are rejected under substantially the same sections of Kim, Kalidas, and Firoozabadi.
Regarding claims 100-102, Kim as modified teaches the pre-analyzing computer system of claim 1.
Kim further teaches wherein the WMS further comprises a motion detector configured to detect motion event data indicative of a change in posture of the patient from a baseline posture (¶[0043], ¶[0069] posture change detector).
Conclusion
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/AMANDA L STEINBERG/ Examiner, Art Unit 3792