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 .
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.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because a computer program product comprising computer program code instructions could read on a transitory computer program code. The examiner suggests adding the term “non-transitory” in front of “computer program product” for claim 1.
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) 1-4, 8-12, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over. US2020/0359912 to Gharib et al. “Gharib”, in view of NPL: “AI-Assisted chocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics” to Chiou et al, and further in view of NPL: “Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance” to Bonnemain et al.
Regarding claims 1, 9, and 16, Gharib discloses a system, method, and computer program product comprising computer program code instructions which, when executed by a processor, enables the processor to carry out a method (Paragraph 0096, system 1000 may include functional blocks that can represent functions implemented by a processor, software, or combination thereof (e.g., firmware)) for classifying a patient's diastolic function, the method comprising:
receiving a plurality of 2D echocardiographic images of the patient's heart (Page 0091, for the method 500, at step 630, measuring by the at least one processor, the contractility feature by an imaging modality including..echocardiogram);
to generate as output the estimate of left ventricular end-diastolic pressure (LVEDP) ) {Paragraph 0092, wherein in step 720 which is also part of method 500, the at least one processor may calculate the LVEDP as a function of the intrinsic frequencies); and
providing, to a user via a user interface (Paragraph 0083), an indication of one or more LVEDPS to the user interface (Paragraph 0086)/
However, Gharib does not disclose analyzing, by a trained diastolic function prediction algorithm, the plurality of 2D echocardiographic images of the patient's heart to estimate left ventricular end-diastolic pressure (LVEDP), wherein the trained diastolic function prediction algorithm is trained to receive as input one or more 2D echocardiographic images, and to generate as output the estimate of left ventricular end-diastolic pressure (LVEDP); classifying the patient's diastolic function as normal or abnormal based on the estimated LVEDP; and
Providing an indication of the patient's diastolic function as normal or abnormal.
Chiou teaches analyzing, by a trained (Page 2092, trained from 4-chamaber view images obtained from the echocardiographic files) diastolic function prediction algorithm, the plurality of 2D echocardiographic images of the patient's heart (All A4C view frames were extracted form the Digital Imaging and Communications in Medicine (DICOM) files, Page 2094, left column, image processing) to determine a diastolic function index (See “central illustration”, Page 2102, wherein the 1D CNN takes segmented A4C views and produces as a end result the diastolic indices such as E/e’, to determine if a person has HFpEF) ), wherein the trained diastolic function prediction algorithm is trained to receive as input one or more 2D echocardiographic images (See central illustration, Page 2102, Step 1), and to generate as output an diastolic function index classifying the patient's diastolic function as normal or abnormal (1D CNN model showed remarkable performance in HFpEF model prediction and discriminated impaired diastolic mechanics, Page 2100, right column; Page 2102, left column, CNN model provided diagnostic interpretation for HFpEF, heart failure with preserved ejection fraction, which would read on abnormal diastolic function); and
providing, an indication of the patient's diastolic function as normal or abnormal (Page 2102, outputting the Diastolic indices, “central illustration, Page 2102; Page 2102, left column, CNN model provided diagnostic interpretation for HFpEF, heart failure with preserved ejection fraction, which would read on an abnormal indication).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Gharib , wherein the method includes analyzing, by a trained diastolic function prediction algorithm, the plurality of 2D echocardiographic images of the patient's heart to estimate left ventricular end-diastolic pressure (LVEDP), wherein the trained diastolic function prediction algorithm is trained to receive as input one or more 2D echocardiographic images, and to generate as output the estimate of left ventricular end-diastolic pressure (LVEDP); classifying the patient's diastolic function as normal or abnormal based on the estimated LVEDP; and providing an indication of the patient's diastolic function as normal or abnormal, as taught by Chiou, in order to provide a fast and precise tool that tracks dynamic changes in LV, let atrial length, width, area, and volume in the A4C (Page 2103, left column).
However, the modifications of Gharib and Chiou do not explicitly disclose using the diastolic function prediction algorithm to estimate left ventricular end-diastolic pressure (LVEDP).
Boinnemain teaches using a neural network to explicitly calculate LVEDP (DNN is used to solve the 0D model, Page 2, with 4 parameters of table 2 including LVEDP).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib and Chiou, algorithm is used to explicitly calculate LVEDP, as taught by Boinnemain, in order to have a fast way to predict LV parameters (Page 6, right column), such as LVEDP.
Regarding claims 2 and 10, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above.
Chiou teaches wherein the method further comprises the step of selecting, by a trained image selection algorithm, a subset of the plurality of received 2D echocardiographic images of the patient's heart for analysis, wherein the image selection algorithm is trained to select 2D echocardiographic images as being optimal for analysis, wherein said analyzing step comprises analyzing the selected subset of the plurality of received 2D echocardiographic images of the patient's heart (See Page 2102, Central illustration, step 1, image preprocessing and image segmentation, where the images are separated between LV and LA from the base A4C view).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, Boinnemain, the method further comprises the step of selecting, by a trained image selection algorithm, a subset of the plurality of received 2D echocardiographic images of the patient's heart for analysis, wherein the image selection algorithm is trained to select 2D echocardiographic images as being optimal for analysis, wherein said analyzing step comprises analyzing the selected subset of the plurality of received 2D echocardiographic images of the patient's heart, as taught by Chiou, in order to better extract the values between LV and LA.
Regarding claims 3 and 11, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above,
Chiou teaches that although the exemplary model did not use clinical information about the subject, such use has been can be done (Used AI machine to classify patient information and parameters, Page 2102, left column).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, and Boinnemain, wherein the method further comprises the step of receiving (140) clinical information about the subject, wherein the trained diastolic dysfunction prediction algorithm also analyzes the received clinical information to classify the patient's diastolic function as normal or abnormal, as taught by Chiou, in order to improve precision (Page 2102, left column).
Regarding claim 4 and 12, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above.
Chiou teaches wherein the method further comprises the step of receiving, via a user interface, input from a user to initiate an analysis by the trained diastolic dysfunction prediction algorithm (using a computer to select the participants 4 chamber view images from echocardiographic files, Page 2092, right column, and starting the step by step image analysis and preprocessing, Page 2094, left column).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, and Boinnemain, wherein the method further comprises the step of receiving, via a user interface, input from a user to initiate an analysis by the trained diastolic dysfunction prediction algorithm, as taught by Chiou, in order to prepare the size of the images to aid in the value extraction process.
Regarding claim 8, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 above.
Gharib discloses wherein the computer program product comprises a non-transitory computer-readable storage medium comprising computer program code instructions which, when executed by a processor, enables the processor to carry out the method (Paragraph 0133).
Claim(s) 5, 6, 7, 13, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gharib, Chiou, and Bonnemain as applied to claims 1 and 9 above, and further in view of NPL: “Echocardiographic evaluation of left ventricular end diastolic pressure in patients with diastolic heart failure” to Zhang et al. “Zhang”.
Regarding claim 5 and 13, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above.
However, the modifications of Gharib, Chiou, Boinnemain do not disclose wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or less than 10 mmHg.
Zhang teaches where wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or less than 10 mmHg (Page 2, method, Zhang teach <15mmH was reduced heart failure).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, Boinnemain, wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or less than 10 mmHg, as taught by Zhang, since Zhang’s values are close to the disclosed range, and it would have been obvious to optimize, such as increase participants in the study, to reduced the range of normal LVEDP to 10mmH or below.
Regarding claim 6 and 14, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above.
However, the modifications of Gharib, Chiou, Boinnemain do not disclose wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or greater 15 mmHg.
Zhang teaches where wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or greater 15 mmHg (Page 2, method, Zhang teach >15mmHg was increased heart failure).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, Boinnemain, wherein the patient's diastolic function is classified as normal when the estimated LVEDP is equal to or greater 15 mmHg, as taught by Zhang, since Zhang’s range for abnormal patients are also greater than 15mmHg.
Regarding claim 7 and 15, the modifications of Gharib, Chiou, Boinnemain disclose all the features of claim 1 or 9 above.
However, the modifications of Gharib, Chiou, Boinnemain do not disclose wherein the patient's diastolic function is classified as normal when the estimated LVEDP is between 10 and 15mmHg.
Zhang teaches where wherein the patient's diastolic function is classified as normal when the estimated LVEDP is between 15mmHg (Page 2, method, Zhang teach <15mmH was reduced heart failure, and greater than 15 mmHg was increased heart failure).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as described by Gharib, Chiou, Boinnemain, wherein the patient's diastolic function is classified as normal when the estimated LVEDP is between 10 between 15mmHg, as taught by Zhang, since Zhang’s values are close to the disclosed range, and it would have been obvious to optimize, such as increase participants in the study, to conclude that values near the delineation of 15mmHg would be borderline abnormal.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milton Truong whose telephone number is (571)272-2158. The examiner can normally be reached 9AM - 5PM, MON-FRI.
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/MT/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798
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