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 .
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed 01/30/2024 has been considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1
Claim 1 recites a system (machine), and claim 11 recites a method.
Step 2A, Prong 1
Claims 1 and 11 recite the limitations of determining an electromechanical relationship between a derived electrical cardiac activation rate and a derived mechanical cardiac activation rate. These steps, given their broadest reasonable interpretation, can be practically performed in the human mind. Namely, a person could derive an electrical and/or mechanical activation rate based on collected signals and compare the two to determine an electromechanical relationship. Therefore, the claims are directed to a mental process abstract idea.
Step 2A, Prong 2
Claims 1 and 11 do not include any additional elements that integrate the abstract idea mental process into a practical application.
Claims 1 and 11 include the additional elements of an ECG system, an ultrasound imaging system and a processor. The ECG and ultrasound imaging systems amount to insignificant, extra-solution activity of data gathering and the processor is generically claimed such that it amounts to generic computer implementation of the abstract idea. Therefore, none of these elements amount to integrating the abstract idea into practical application.
Step 2B
Claims 1 and 11 do not include any additional elements that amount to significantly more than the abstract idea.
Claims 1 and 11 include the additional elements of an ECG system, an ultrasound imaging system and a processor. The ECG and ultrasound imaging systems amount to insignificant, extra-solution activity of data gathering and the processor is generically claimed such that it amounts to generic computer implementation of the abstract idea. It can also be appreciated that the claimed additional elements represent well-understood, routine, conventional activity in the art and amount only to extra-solution activity and computer implementation of the abstract idea. Therefore, none of the elements amount to significantly more than the abstract idea.
Claims 2, 5, and 6 only include additional extra-solution activity of data gathering.
Claims 3, 4, 7-10, and 12-14 only further define the abstract idea, namely the mental steps of observing and analyzing data.
Claim 15 only adds generic computer structure.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, 7-9, and 11-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al (US 20190125311 A1).
Regarding claim 1, Zhang teaches a system for assessing electromechanical remodeling during cardiac fibrillation (see Fig. 2, [0023]; system detects mechanical activity of heart using ultrasound and electrical activity using ECG and comparatively represents the electromechanical activity), comprising:
an ECG system (11) for generating an ECG signal (see Fig. 2, [0027]; ECG monitoring unit 11 including leads and a processing circuit);
an ultrasound imaging system (10) for generating ultrasound data at least for anatomical imaging (see Fig. 2, [0024-0025]; ultrasound imaging unit 10); and
a processor (see Fig. 2, [0029-0031]; data processing unit 12) adapted to:
derive movement information from the ultrasound data (see [0029]; data processing unit 12 may detect mechanical movements of the atrium and the ventricle based on the echocardiography images derived from ultrasound echo);
derive a mechanical cardiac activation rate during cardiac fibrillation from the movement information (see Fig. 4, [0039-0044]; method to calculate cardiac motion parameters from echocardiography images);
derive an electrical cardiac activation rate during cardiac fibrillation from the ECG signal (see [0049]; data processing unit may determine whether the rhythm and frequency of P wave and the QRS wave in the ECG is consistent with the systole markers, it can be appreciated that this must therefore include determining the rhythm and frequency associated with the ECG signal); and
determine an electromechanical relationship between the electrical cardiac activation rate and the counterpart mechanical cardiac activation rate indicative of the electromechanical remodeling stage (see [0049]; data processing unit determines relationship between mechanical activity and electrical activity to determine if there is a mis-match of electromechanical activities).
Regarding claim 3, Zhang teaches the system of claim 1, wherein the electromechanical relationship identifies electromechanical dissociation if the electrical cardiac activation rate or rates are greater than the mechanical cardiac activation rate or rates (see [0049]; frequency dissociation found to have occurred when the mechanical signals and the ECG signals have respective frequencies, see Fig. 9 where there are 5 ECG signals and 4 mechanical signals detected in the same time period, which triggers the identification of electromechanical dissociation).
Regarding claim 4, Zhang teaches the system of claim 3, wherein the processor is adapted to derive a parameter representing the level of electromechanical dissociation as a difference between the electrical cardiac activation rate and the mechanical cardiac activation rate (see [0049]; the processing unit makes a comparison between the derived mechanical signals and electrical signals which results in a parameter that triggers the data processing unit to generate an alarm indication if an inconsistency is met). It can be appreciated that in paragraph [0049], Zhang describes multiple scenarios of different levels/types of electromechanical dissociation including an electrical signal detected with no corresponding mechanical signal, a mechanical signal with no corresponding electrical signal, or electrical and mechanical signals with different frequencies, where the electrical and mechanical activation rates are compared to one another to determine a difference between the two, and indicate respective levels of dissociation and cardiac events.
Regarding claim 5, Zhang teaches the system of claim 1, comprising a single or multiple lead ECG system (see [0028]; ECG monitoring unit 11 may include multiple leads).
Regarding claim 7, Zhang teaches the system of claim 1, wherein the processor is adapted to process the ultrasound imaging system data to:
obtain a 2D anatomical image video (see Fig. 3, [0036]; the echocardiography images may be two-dimensional);
segment the myocardial wall from the 2D anatomical image video (see [0037]; features of the echocardiography images may be extracted such as the left ventricular posterior wall, ventricular septum, or left atrial cavity wall, which are segments of the myocardial wall);
obtain tissue velocity information in respect of the segmented myocardial wall (see Fig. 4, [0043]; obtain cardiac motion parameters); and
derive the mechanical activation rate for the myocardial wall (see Fig. 3, [0039-0045]; calculate cardiac motion parameters and generate systole markers of the atrium and ventricle).
Regarding claim 8, Zhang teaches the system of claim 1, wherein:
deriving the mechanical cardiac activation rate comprises using a spectral domain method or a time domain method to the motion information (see Fig. 5, [0046]; motion information modeled against time axis T; and
deriving the electrical cardiac activation rate comprises using a spectral domain method or a time domain method to the ECG signal (see Fig. 3, [0035]; step 21, the ECG information varying over time outputted by the ECG monitoring unit may be received). It can be appreciated that Zhang teaches time consistency between the ECG and echocardiography signals so that they may be compared in the same time domain ([0031]).
Regarding claim 9, Zhang teaches the system of claim 1, for assessing atrial fibrillation remodeling, wherein the processor is further adapted to cancel a ventricular tissue velocity component of the obtained tissue velocity information before deriving the mechanical cardiac activation rate (see Fig. 4, [0037-0043]; the echocardiography images are smoothed to identify the left atrial chamber, the motion parameters of the atrium may be obtained according to characteristic time points of the atrial movement cycle). See also Fig. 5 where the signals for the left atrium and left ventricle are separated from one another.
Regarding claim 11, Zhang teaches a method of determining an electromechanical relationship between an electrical cardiac activation rate and a mechanical cardiac activation rate during cardiac fibrillation (see [0023]; comparatively represents electromechanical cardiac activity using ultrasound monitoring and ECG signals), comprising:
obtaining an ECG signal (see Fig. 3, [0035]; step 21 receive ECG information);
obtaining ultrasound data (see Fig. 3, [0034]; step 20 receive ultrasound echoes);
deriving movement information from the ultrasound data (see [0029]; data processing unit 12 may detect mechanical movements of the atrium and the ventricle based on the echocardiography images derived from ultrasound echo);
deriving a mechanical cardiac activation rate during cardiac fibrillation from the movement information (see Fig. 4, [0039-0044]; method to calculate cardiac motion parameters from echocardiography images);
deriving an electrical cardiac activation rate during cardiac fibrillation from the ECG signal (see [0049]; data processing unit may determine whether the rhythm and frequency of P wave and the QRS wave in the ECG is consistent with the systole markers, it can be appreciated that this must therefore include determining the rhythm and frequency associated with the ECG signal); and
determining the electromechanical relationship between the electrical cardiac activation rate and the counterpart mechanical cardiac activation rate indicative of the electromechanical remodeling stage (see [0049]; data processing unit determines relationship between mechanical activity and electrical activity to determine if there is a mis-match of electromechanical activities).
Regarding claim 12, Zhang teaches the system of claim 11, wherein the deriving the mechanical cardiac activation rate comprises:
creating a 2D anatomical image video from the ultrasound data (see Fig. 3, [0036]; the echocardiography images generated from ultrasound echoes may be two-dimensional);
segmenting the myocardial wall from the 2D anatomical image video (see [0037]; features of the echocardiography images may be extracted such as the left ventricular posterior wall, ventricular septum, or left atrial cavity wall, which are segments of the myocardial wall);
obtaining tissue velocity information in respect of the myocardial wall (see Fig. 4, [0043]; obtain cardiac motion parameters); and
deriving the mechanical activation rate for the myocardial wall (see Fig. 3, [0039-0045]; calculate cardiac motion parameters and generate systole markers of the atrium and ventricle).
Regarding claim 13, Zhang teaches the method of claim 11, comprising identifying electromechanical dissociation if the electrical cardiac activation rate or rates are greater than the mechanical cardiac activation rate or rates (see [0049]; frequency dissociation found to have occurred when the mechanical signals and the ECG signals have respective frequencies, see Fig. 9 where there are 5 ECG signals and 4 mechanical signals detected in the same time period, which triggers the identification of electromechanical dissociation).
Regarding claim 14, Zhang teaches the method of claim 13, wherein the method further comprises deriving a parameter representing the level of electromechanical dissociation as a difference between the electrical cardiac activation rate and the mechanical cardiac activation rate (see [0049]; the processing unit makes a comparison between the derived mechanical signals and electrical signals which results in a parameter that triggers the data processing unit to generate an alarm indication if an inconsistency is met). It can be appreciated that in paragraph [0049], Zhang describes multiple scenarios of different levels/types of electromechanical dissociation including an electrical signal detected with no corresponding mechanical signal, a mechanical signal with no corresponding electrical signal, or electrical and mechanical signals with different frequencies, where the electrical and mechanical activation rates are compared to one another to determine a difference between the two, and indicate respective levels of dissociation and cardiac events.
Regarding claim 15, Zhang teaches a computer program comprising computer program code means which is adapted, when said program is run on a computer, to implement the method of claim 11 (see [0058]; method may be implemented by programs executed by related hardware, such as processor and may be stored on a computer readable storage medium).
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US 20190125311 A1) in view of Nestaas (US 20090105580 A1).
Regarding claim 2, Zhang teaches the system of claim 1. Zhang is silent regarding where the processor is adapted to derive movement information by applying Tissue Doppler Imaging.
Nestaas teaches a system for myocardial tissue imaging wherein the movement information is derived by applying Tissue Doppler Imaging (see Nestaas [0023]; calculating a tissue velocity derived value based on a set of tissue Doppler images).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zhang’s ultrasound imaging system with the tissue Doppler imagining system as taught by Nestaas. One of ordinary skill in the art would have been motivated to make this modification in order to obtain a large amount of data regarding the tissue velocity of the target tissue in multiple dimensions (Nestaas [0002-0003]). It can be appreciated that one of ordinary skill in the art would be familiar with Tissue Doppler Imaging as tissue velocity imagining is generally divided into echocardiographic methods, such as Tissue Doppler Imaging using ultrasound, and magnetic resonance methods (Nestaas [0002]).
Claim(s) 6 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (US 20190125311 A1) in view of Bukkapatnam et al (US 20140207005 A1).
Regarding claim 6, Zhang teaches the system of claim 5. Zhang is silent regarding wherein the ECG system is a single lead system with the ECG lead II.
Bukkapatnam teaches a system for real time monitoring of cardiovascular dynamics using an ECG system wherein the ECG system is a single lead system (see Bukkapatnam [0021]; using a single lead of an ECG to derive a model of a patient’s heart) with the ECG lead II (see Bukkapatnam [0017], [0047]; ECG lead II has been found suitable for use in some embodiments).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zhang’s ECG lead system with the single lead system with ECG lead II as taught by Bukkapatnam. One of ordinary skill in the art would have been motivated to make this modification in order to capture important time and frequency patterns and provide real time estimates to cardiac factors without using a large variety of leads (Bukkapatnam [0051]).
Regarding claim 10, Zhang teaches the system of claim 1 wherein the processor is adapted to process the ECG signal with respect to an atrial ECG signal (see Zhang [0049]; data processing unit determined whether the atrial systole marker appears at the same time as the P wave appears). Zhang is silent regarding wherein the processor is adapted to process the ECG signal to:
remove ventricular components thereby to derive an atrial ECG signal; and derive an electrical atrial activation rate from the atrial ECG signal.
Bukkapatnam teaches wherein the processor is adapted to process the ECG signal to:
remove ventricular components thereby to derive an atrial ECG signal; and derive an electrical atrial activation rate from the atrial ECG signal (see Fig. 3, [0066-0068]; to derive the atrial activation function, which is assumed to be contemporaneous with the atrial systole period, the times of the P peaks and R peaks are selected to represent the atrial ECG signal and used to calculate the atrial activation rate).
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Zhang’s system for processing an ECG signal using an atrial ECG signal as taught by Bukkapatnam. One of ordinary skill in the art would have been motivated to make this modification in order to construct activation functions that derive an electrical atrial activation rate to model cardiac parameters (Bukkapatnam [0067]) in a form that can be compared to a mechanical activation rate in order to assess electromechanical remodeling.
Conclusion
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/A.J.S./Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792