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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/28/2026 has been entered. Claims 49-50 remain pending in the application.
Response to Amendment
Claims 49-50 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Final Office Action mailed 10/03/2025.
Response to Arguments
Applicant’s arguments filed 01/28/2026 with respect to claim 49 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Given the amendments to claim 49, reference to Codella is being relied upon to teach dependent claim 50 more-consistently with the instant claim language, as shown below.
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.
Claims 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over Codella et al. (US 20170116728 A1, published April 27, 2017) in view of Abolmaesumi et al. (US 20190125298 A1, published May 2, 2019 with a priority date of April 21, 2016), hereinafter referred to as Codella and Abolmaesumi, respectively.
Regarding claim 49, Codella teaches a cardiac platform (Fig. 1) comprising:
a neural network system receiving imaging information related to a heart of at least one patient and comprising a core neural network and a plurality of downstream neural networks, said core neural network to perform shared feature extraction from only said imaging information, wherein each of the plurality of downstream neural networks to receive an output of the shared feature extraction (see para. 0029 – “The embodiment shown in FIG. 1 comprises: a feature engine 102 [core neural network] that calculates several groups of image features characteristic of texture and content of input images 100 [cardiac CT image as imaging information] and a cognitive engine 116 comprising a plurality of classifiers 118 through 128 [plurality of downstream neural networks], each receiving the output of the feature engine.”);
the cardiac platform to generate, from an output of at least one of the distinct downstream procedures, at least one of the following measurements: the dimensions of the left ventricle in systole and diastole, right ventricular assessment, LA (Left atrium) size, measurement of the aortic valve annulus, the aortic sinuses, the ascending aorta, the pulmonary valve, the mitral valve annulus and the tricuspid valve annulus, RA (Right atrium) size, RV (Right ventricle) size, and to perform at least one of the following functions from at least one of said measurements: LV EF (left ventricle ejection fraction), LV Volume, LV function, RV/LV Ratio (Right ventricle), AO (Aorta), MV (Mitral valve), PV (Pulmonary valve), TV (Tricuspid valve), Pericardial Effusion, Segmental Abn. (abnormalities), Aortic Measurements, and IVC (Inferior vena ceva) size (see para. 0006 – “In cardiac imaging, the viewpoint of the image is an essential input for any algorithm designed to measure clinical features of the heart, such as detection of left ventricle, valves, thickness of the pericardium, etc.”; see para. 0027 – “As such, a viewpoint recognition system for CT imaging should correctly label sagittal, axial and coronal planes. There are also oblique planes that are obtained to assess cardiac chamber morphology, size and function. Short axis view (SHA) through the entire left ventricle is useful in calculation of left ventricle volume and ejection fraction, whereas the function of the left ventricle should be reviewed in long axis views which include the two chamber (2C) and four chamber (4C) views. There are also three chamber and five chamber views that are useful to study the aortic valve and left ventricle outflow.”).
Codella teaches a cardiac imaging platform, but does not explicitly teach performing cardiac ultrasound imaging.
Whereas, Abolmaesumi, in an analogous field of endeavor, teaches cardiac ultrasound imaging (Fig. 8; see para. 0079 – “Referring to FIG. 8, the neural network 360 includes 5 image quality assessment neural networks, each including the same shared layers 362 but including a different set of view category specific layers 370, 372, 374, 376, and 378…Each of the 5 image quality assessment neural networks takes as an input a sequence of 20 echocardiographic images 380 and outputs a view category specific quality assessment value.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified performing cardiac imaging, as disclosed in Codella, by performing cardiac ultrasound imaging, as disclosed in Abolmaesumi. One of ordinary skill in the art would have been motivated to make this modification in order to perform certain quantified clinical measurement of anatomical features and/or to assist in diagnosing a medical condition or a characteristic of the heart, as taught in Abolmaesumi (see para. 0036).
Furthermore, regarding claim 50, Codella further teaches having an online mode and an offline mode (see para. 0048 – “Training [offline] and testing [online] strategy 130: A leave-one-sample out strategy was used for training and testing.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
J. Park et al, “Automatic Cardiac View Classification of Echocardiogram”, IEEE International Conference on Computer Vision, pp. 1-8, Nov. 2007 discloses given an input cardiac video sequence, we first detect LV candidates by applying the LV detectors: one LV candidate per LV detector. Using the detected LV structures, we construct corresponding global templates and feed them into corresponding LV-DD multi-class view classifiers. We arrive at the final classification by combining the multiple classification results from the view classifiers (Fig. 2).
Krishnan et al. (US 20050059876 A1, published March 17, 2005) discloses obtaining image data of a heart of a patient, obtaining features from the image data of the heart, which are related to motion of the myocardium of the heart, and automatically assessing regional myocardial function of one or more regions of a myocardial wall using the obtained features.
Chen et al. (US 20100217097 A1, published August 26, 2010) discloses feature extraction is performed on visual data, and downstream of the visual feature extraction module are classifiers, where classifiers operate to identify predetermined patterns of features from each stream (Fig. 2).
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/N.C./Examiner, Art Unit 3798