Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zaldi et al. (U.S. Patent Application Publication 2017/0225001) in view of Irusta et al. et al. (A high-temporal resolution algorithm to discriminate shockable from Non-shockable rhythms in adults and children, Resuscitation, 83, 2012, pp. 1090-1097) in further view of Zaldi et al. (U.S. Patent Application Publication 2017/0225001) (themselves).
Regarding claim 1, Zaldi et al. disclose a WCD system comprising:
a support structure (“medical device 700” comparing “garment 710,” see [0162] and figure 7, and other alternate/equivalent counterparts in other embodiments) configured to be worn by a patient;
an energy storage module (“battery 810,” see [0168] and figure 8);
a discharge circuit (comprising: 1) “therapy delivery circuit 802,” and 2) “electrodes 820,” see [0168]-[0169] and figures 7 and 8) coupled to the energy storage module;
a transducer {the present specification discloses “Sensing electrodes 209 are types of transducers that can help 25 sense an ECG signal, e.g. a 12-lead signal,” see page 9, lines 24-25 of the present specification} (“electrocardiogram (ECG) electrodes 822,” see [0177]-[0178] and figure 8) configured to render, from a sensed Electrocardiogram (ECG) of the patient, a physiological input that includes ECG data of the patient; and
a processor (“processor 818,” see [0178],[0182] and figure 8) configured (through the use of programming (see [0189]) and algorithms (abstract,[0027], [0035], [0041]) configures to:
perform analysis of the physiological input to detect whether a shockable condition exists (see step 308, [0101] and figure 4A),
perform a check step for high accuracy (see for example step 310, [0102] and figure 4A),
provide defibrillation therapeutic pulses, (see [0169] and claim 1 for example),
control, responsive to detecting that the shockable condition exists, the discharge circuit to discharge a stored electrical charge through the patient to deliver a shock to the patient while the support structure is worn by the patient (see [0101], [0161], [0166], and claim 1 for example).
Zaldi et al. disclose further: 1) “machine learning ” (see [0090]), 2) “the high-accuracy clauses are determined heuristically by testing various candidate clauses for accuracy against a database of pre-stored patient data to determine clauses that have low false positive rates” (see [0090]), and 3) “the clauses can be determined, for example, by using a machine learning process on the database to identify conditions that indicate the presence of shockable rhythms with low false positive rates,” (see [0090]).
Zaldi et al. also disclose “new or modified rules will be used over the longer time interval to achieve a sufficient level of accuracy. In some cases, the algorithm may submit a vote of shockable or non-shockable over multiple time segments within the extended time period. Accordingly, the initial high accuracy clause(s) may make up an initial rule set which only requires the initial time period (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, or 3 seconds). The adjusted rule set requires both the initial time period plus one or more additional time periods (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 seconds, 6-9 seconds).
See [0035]
Zaldi et al. fail to explicitly recite the processor is configured to:
1) perform a first-level analysis of the physiological input to detect whether a shockable condition exists, and
2) perform, responsive to an outcome of the first-level analysis being that a shockable condition does not exist, a second-level analysis of the physiological input to detect whether the shockable condition exists.
Like Zaldi et al., Irusta et al. disclose an algorithm to discriminate shockable from non-shockable rhythms in patients (adults and children) and the use of automatic external defibrillator (AED) and teach a control algorithm that:
1) perform a first-level analysis of the physiological input to detect whether a shockable condition exists (“diagnose the ECG using a short analysis segment of 3.2 s” to determine if there is a shockable event, see page 1090, last paragraph), and
2) perform, responsive to an outcome of the first-level analysis being that a shockable condition does exist, a second-level analysis of the physiological input to detect whether the shockable condition exists (see 1) “processed by the complete algorithm for a shock/no-shock decision,” see page 1090, last paragraph, and 2) “Likely shockable segments are further analysed in terms of regularity (stability), spectral distribution and heart rate to be finally classified as shockable (VF or rapid VT), slow VT or nonshockable,” see page 1091, “2.2. Complete EAP algorithm” section),
in order to provide “an accurate high-temporal resolution AED rhythm recognition algorithm for AED valid for adult and children” that “shortens pre-shock pauses which may contribute to improve resuscitation outcome” (see ”conclusion” on page1096).
NOTE: this is not sufficient yet. The prior art Irusta et al. only provides or teaches: A) if the first level (i.e., short analysis) provides a shockable event/activity, B) then perform the second level (i.e., complete or full blown analysis).
What is needed for a proper 103-obviousness rejection is now a teaching of A) if the first level analysis detects there is no shockable condition, B) then do the full-blown second level analysis to determine whether there is a shockable condition.
This could and would be done with algorithms that are being trained in order to provide the needed accuracy for AED/WCD use.
Like Zaldi et al., themselves, and Irusta et al., Zaldi et al. disclose the use of algorithms and machine learning and teach “testing various candidate clauses” or testing various steps (see [0090]) in order to improve accuracy to high levels.
This means Zaldi et al. provides its own motivation for testing the accuracy of the determination by the first-level analysis that there is no shockable event/activity/condition by testing it against the second-level full-blown analysis in order to achieve “the high-accuracy clauses” in order to provide high accuracy for the algorithm.
Therefore, at the time of the invention it would have been known by one of ordinary skill in the art to modify the invention of Zaldi et al., as taught by Irusta et al., to provide an algorithm that 1) performs a first-level analysis of the physiological input to detect whether a shockable condition exists, and performs, responsive to an outcome of the first-level analysis being that a shockable condition does exist, a second-level analysis of the physiological input to detect whether the shockable condition in order to provide “an accurate high-temporal resolution AED rhythm recognition algorithm for AED valid for adult and children” that “shortens pre-shock pauses which may contribute to improve resuscitation outcome,” and as further taught by Zaldi et al. (themselves), to check the accuracy of steps (or clauses of) the algorithm, like the determination by the first-level that there is no shockable event/condition/activity by checking that determination against (or with) the second-level (full-blown) analysis in order to train and provide the algorithm with “high-accuracy clauses” in order to provide high accuracy for the algorithm to improve the efficacy of AED/WCD for patients.
Regarding claim 2, Zaldi et al. disclose the claimed invention including “the first-level analysis is performed on a first portion of the physiological input,” (“initial time period (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, or 3 seconds),” see [0035]).
Regarding claim 3, Zaldi et al. disclose the claimed invention including “the first-level analysis is performed on the first ECG data as the first ECG data is streaming only in a single direction.” In paragraph [0112] the present application’s U.S. Patent Application Publication 2023/0271022 it is disclosed “continuously streaming data can only be processed in one direction,” i.e., continuous streaming or recording of ECG data is done in a single direction. Since Zaldi et al. disclose ECG data is periodically continuously checked (see [0039]) this meets the claimed further limitation of claim 3. Also see [0103], [0108], and [0129] for example.
Regarding claim 4, Zaldi et al. disclose the claimed invention including “the second-level analysis is performed on a second portion of the physiological input wherein the first portion includes second ECG data from a second time segment,” (“The adjusted rule set requires both the initial time period plus one or more additional time periods (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 seconds, 6-9 seconds),” see [0035]).
Regarding claim 5, Zaldi et al. disclose the claimed invention including “the first portion includes first ECG data from a first time segment,” (“initial time period (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, or 3 seconds),” see [0035]).
Regarding claim 6, Zaldi et al. disclose the claimed invention including “second portion includes second ECG data from a second time segment,” (“The adjusted rule set requires both the initial time period plus one or more additional time periods (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 seconds, 6-9 seconds),” see [0035]).
Regarding claim 7, Zaldi et al. disclose the claimed invention including “second time segment has a different time duration than the first time segment,” (“The adjusted rule set requires both the initial time period plus one or more additional time periods (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 seconds, 6-9 seconds),” see [0035]).
Regarding claim 8, Zaldi et al. disclose the claimed invention including “the second time segment overlaps with the first time segment at least in part,” (“The adjusted rule set requires both the initial time period plus one or more additional time periods (e.g., 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 seconds, 6-9 seconds),” see [0035]).
Regarding claim 9, Zaldi et al. disclose the claimed invention including “the second-level analysis is performed responsive to a checking input being received,” (here the checking input is interpreted as met by: 1) the accuracy check in figure 4A, or 2) the “motion sensor” or data provided by the “motion sensor” see [0006], [0122], and [0128] for example).
Regarding claim 10, Zaldi et al. disclose the claimed invention including “a timer configured to generate the checking input at a preset time,” (see [0128] for the incorporation of Elghazzawi et al. (U.S. Patent 6,961,612) the timer for CPR chest compressions (motion sensing), see Elghazzawi et al. col. 3:42-50, and claim 2 for example).
Regarding claim 11, Zaldi et al. disclose the claimed invention including “a motion detector configured to generate the checking input responsive to a motion of the patient that is detected by the motion detector,” (the “motion sensor” or data provided by the “motion sensor” see [0006], [0122], and [0128] for example).
Regarding claim 12, Zaldi et al. disclose the claimed invention including “a the first-level analysis is configured to detect a possible shockable condition,” see [0034]-[0035].
Regarding claim 13, Zaldi et al. disclose the claimed invention including “the second-level analysis is configured to detect an actual shockable condition,” see [0034]-[0035].
Regarding claim 14, Zaldi et al. disclose the claimed invention including “the possible shockable condition and the actual shockable condition are different,” since they are the determinations of completely different steps, see [0034]-[0035].
Regarding claim 15, Zaldi et al. disclose the claimed invention including “when shocking condition is not detected, the first-level analysis is repeated,” since this happens when the required level of accuracy has not obtained yet, see figure 4A.
Regarding claim 16, Zaldi et al. disclose the claimed invention including “when the shocking condition is not detected, neither the first-level analysis nor the second-level analysis is then performed for a preset pause time,” since this happens when the required level of accuracy has been obtained and no shockable event/activity has been detected in the first level analysis, see figure 4A.
Regarding claim 19, Zaldi et al. fail to disclose the claimed “the processor includes:
a first computational module configured to perform the first-level analysis, and a
second computational module distinct from the first computational module,
wherein the second computational module configured to perform the second-level
analysis.”
Like Zaldi et al., Irusta et al. disclose an algorithm to discriminate shockable from non-shockable rhythms in patients (adults and children) and the use of automatic external defibrillator (AED) and teach using a first computational module configured to perform the first-level analysis (the part of the algorithm responsible for the short/initial analysis) exists (“diagnose the ECG using a short analysis segment of 3.2 s” to determine if there is a shockable event, see page 1090, last paragraph)), and a second computational module distinct from the first computational module (the more complete/full-blown ECG analysis, (see 1) “processed by the complete algorithm for a shock/no-shock decision,” see page 1090, last paragraph, and 2) “Likely shockable segments are further analysed in terms of regularity (stability), spectral distribution and heart rate to be finally classified as shockable (VF or rapid VT), slow VT or nonshockable,” see page 1091, “2.2. Complete EAP algorithm” section)), wherein the second computational module configured to perform the second-level analysis in order to provide “an accurate high-temporal resolution AED rhythm recognition algorithm for AED valid for adult and children” that “shortens pre-shock pauses which may contribute to improve resuscitation outcome” (see ”conclusion” on page1096).
Therefore, at the time of the of invention it would have been obvious to one of ordinary skill in the art to modify the invention of Zaldi et al., as taught by Irusta et al., to use a first computational module configured to perform the first-level analysis, and a second computational module distinct from the first computational, wherein the second computational module configured to perform the second-level analysis in order to provide “an accurate high-temporal resolution AED rhythm recognition algorithm for AED valid for adult and children” that “shortens pre-shock pauses which may contribute to improve resuscitation outcome.”
Allowable Subject Matter
Claims 17-18, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AARON F ROANE/Primary Examiner, Art Unit 3792