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) submitted on 01/05/2026 has been considered by the examiner.
Response to Amendment
The amendment filed 03/23/2026 has been entered. Claims 1-14 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 01/09/2026.
Response to Arguments
Applicant's arguments filed 03/23/2026 have been fully considered but they are not persuasive.
The applicant argues that “Allan's system is specialized for LA segmentation and does not perform, nor is it trained on, procedures such as view detection, systole/diastole detection, or valve localization” (see pg. 6, para. 3 of applicant’s remarks), and the examiner disagrees. Allan teaches part segmentation and view detection (Fig. 2; see pg. 41, col. 2, para. 4 – “…LA segmentation contours are then used in the testing step [of a trained neural network] to perform apical view classification [view detection] and ROI selection on a new echo image.”; see Fig. 3 – “During testing [of a trained neural network], the jICA mixing coefficients from the reconstruction of image intensity maps in apical views are used to determine LA segmentation contours [part segmentation]…”).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “…the claimed neural network is trained on a plurality of different, complementary procedures…This training on multiple, varied procedures yields a robust and versatile core neural network capable of performing a wide range of analytical tasks…” (see pg. 6, para. 5 of applicant’s remarks)) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Claims 1 and 8 recites “use at least one neural network trained on multiple recognition and analysis procedures”, so under reasonable interpretation, the procedures (at least two of view detection, systole/diastole detection, part segmentation, and valve localization) can be trained using two separate neural networks, as taught in Allan (see 102(a) rejection below).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,4, 6, 9, 12, and 14 of U.S. Patent No. 11,478,226 B2 in view of Allan. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are an obvious variant of the claim set from the patent only including minor differences in structure.
Claims 1 and 8 of the instant invention and claims 1 (see col. 19, lines 4-7) and 9 (see col. 20, lines 8-9) of US patent '226 similarly recites receiving imaging information related to a patient. The instant invention specifies receiving ultrasound imaging information related to a heart of a patient (in view of Allan, see 102(a) rejection below).
Claims 1 and 8 of the instant invention and claims 1 (see col. 19, lines 17-23) and 9 (see col. 20, lines 16-21) of US patent '226 similarly recites using at least one neural network trained on multiple recognition and analysis procedures to detect at least one anomaly and/or to classify a severity of at least one anomaly.
Claims 2 and 9 of the instant invention and claims 4 (see col. 19, lines 33-45) and 12 (see col. 20, lines 31-43) of US patent '226 similarly recites detecting an anomaly of the heart. The instant invention specifies different measurements of the heart (in view of Allan, see 102(a) rejection below).
Claims 3 and 10 of the instant invention and claims 4 (see col. 19, lines 33-45) and 12 (see col. 20, lines 31-43) of US patent '226 similarly recites detecting an anomaly of the heart. The instant invention specifies the anomaly is an abnormality in wall motion (in view of Allan, see 102(a) rejection below).
Claims 4 and 11 of the instant invention and claims 4 (see col. 19, lines 33-45) and 12 (see col. 20, lines 31-43) of US patent '226 similarly recites detecting an anomaly of the heart. The instant invention specifies the anomaly is one of: hypokynesis, diskynesia or paradoxical motion of any part of a left ventricular wall and/or a septum (in view of Allan, see 102(a) rejection below).
Claims 5 and 12 of the instant invention and claims 4 (see col. 19, lines 33-45) and 12 (see col. 20, lines 31-43) of US patent '226 similarly recites wherein said at least one neural network is also configured to make at least one cardiac measurement.
Claims 6 and 13 of the instant invention and claims 4 (see col. 19, lines 33-45) and 12 (see col. 20, lines 31-43) of US patent '226 similarly recites detecting systole and diastole, parts, and valve of a heart. The instant invention specifies different cardiac measurements (in view of Allan, see 102(a) rejection below).
Claims 7 and 14 of the instant invention and claims 6 (see col. 19, line 53) and 14 (see col. 20, line 51) of US patent '226 similarly recites wherein said at least one cardiac measurement is ejection fraction.
Claims 1-14 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/048,873 (reference application) in view of Allan. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are an obvious variant of the claim set from the patent only including minor differences in structure.
Claims 1-14 of the instant invention and claim 1 of copending Application No. 18/048,873 similarly recite receiving ultrasound imaging information related to a heart of a patient, and using at least one neural network trained on multiple recognition and analysis procedures to detect at least one anomaly and/or to classify a severity of at least one anomaly by performing different measurements of the heart.
The instant invention specifies detecting different types anomalies of the heart (in view of Allan, see 102(a) rejection below). This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 and 8 recites a judicial exception (abstract idea). The “receive ultrasound imaging information…” and “…detect at least one anomaly and/or to classify a severity…” steps do not specify how receive information and detect/classify an anomaly. The physician can print and view an ultrasound image (receive ultrasound imaging information) to determine an anomaly and its severity in the printed image using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
General system elements (i.e., “specifically configured computer hardware arrangement”, neural network) related to the insignificant extra solution activity steps that do not integrate the abstract idea into a practical application as it does not impose any meaningful limits on practicing the abstract idea.
Claims 2-4 and 9-11 merely specifies what is considered an anomaly. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claims 5 and 12 merely specifies an output of the neural network. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claims 6-7 and 13-14 merely specifies what is considered a cardiac measurement. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
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.
Claims 1-2, 5-9, and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated G. Allan et al, "Simultaneous Analysis of 2D Echo Views for Left Atrial Segmentation and Disease Detection", IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 40-50, Jan. 2017, hereinafter referred to as Allan.
Regarding claim 1, and similarly for claim 8, Allan teaches a system for detecting at least one cardiac anomaly (Fig. 3), the system comprising:
a specifically configured computer hardware arrangement (see pg. 42, col. 2, para. 3 "We use a MATLAB implementation of jICA available online.1 A schematic of the jICA approach is shown in Fig. 4." where MATLAB implantation is performed on a computer hardware arrangement, where a computer hardware arrangement performing ultrasound imaging diagnostics is inherent and known in the art) configured to:
receive ultrasound imaging information related to a heart of a patient (see Fig. 3- "During testing, the jICA mixing coefficients from the reconstruction of image intensity maps [ultrasound imaging information] in apical views are used to determine LA [left atrium of heart] segmentation contours, compute LA volume, and assign the disease label for an unseen image."); and
use at least one neural network trained on multiple recognition and analysis procedures to detect at least one anomaly and/or to classify a severity of at least one anomaly (see Fig. 3 "During testing [of a trained neural network], the jICA mixing coefficients from the reconstruction of image intensity maps in apical views are used to determine LA [left atrium of heart] segmentation contours, compute LA volume, and assign the disease label [anomaly] for an unseen image."),
wherein the multiple recognition and analysis procedures comprise at least two procedures selected from the group consisting of: view detection, systole/diastole detection, part segmentation, and valve localization (Fig. 2; see pg. 41, col. 2, para. 4 – “…LA segmentation contours are then used in the testing step [of a trained neural network] to perform apical view classification [view detection] and ROI selection on a new echo image.”; see Fig. 3 – “During testing [of a trained neural network], the jICA mixing coefficients from the reconstruction of image intensity maps in apical views are used to determine LA segmentation contours [part segmentation]…”).
Furthermore, regarding claims 2 and 9, Allan further teaches herein the anomaly is one of: left ventricle LV) ejection fraction (EF), LV Volume, right ventricle/left ventricle (RV/LV) Ratio, aortic(AO),mitral valve (MV),pulmonic valve (PV),tricuspid valve (TV), pericardial effusion, segmental abnormality, aortic measurements, and inferior vena cava (IVC) size (see Abstract "These models are then both used for segmentation and volume estimation of cardiac chambers such as the left atrium and for detecting pathological abnormalities such as mitral regurgitation." where measuring cardiac parameters with ultrasound imaging is inherent and known in the art).
Furthermore, regarding claims 5 and 12, Allan further teaches wherein said at least one neural network is also configured to make at least one cardiac measurement (see Fig. 3 "computer LA volume" as cardiac measurement, where measuring cardiac parameters with ultrasound imaging is inherent and known in the art).
Furthermore, regarding claims 6 and 13, Allan further teaches wherein said at least one cardiac measurement is one of: dimension of a left ventricle in systole and diastole, right ventricular assessment, left atrium (LA) size, measurement of an aortic valve annulus, an aortic sinus, an ascending aorta, a pulmonary valve, a mitral valve annulus and a tricuspid valve annulus (see Fig. 3 " compute LA volume [LA size]..." where measuring cardiac parameters with ultrasound imaging is inherent and known in the art).
Furthermore, regarding claims 7 and 14, Allan further teaches wherein said at least one cardiac measurement is ejection fraction (Fig. 3; see para. 0074 "Other extraction modules include a global parameter extraction module (32-2) for extracting global parameters from ultrasound image data, including for example, LVEF (left ventricular ejection fraction)..." where measuring cardiac parameters such as ejection fraction with ultrasound imaging is inherent and is known in the art).
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 3-4 and 10-11 are rejected under35 U.S.C. 103 as being unpatentable over Allan in view of Krishnan et al. (US 20050020903 A1, published January 27, 2005), from IDS, hereinafter referred to as Krishnan.
Regarding claims 3 and 10, Allan teaches all of the elements disclosed in claims 1 and 8 above.
Allan teaches detecting abnormalities in an ultrasound heart image, but does not explicitly teach wherein the anomaly is an abnormality in wall motion.
Whereas, Krishnan, in the same field of endeavor, teaches wherein the anomaly is an abnormality in wall motion (see para. 0045 "In one exemplary embodiment of the invention, the assessment or classification results output from the classification module (24) include a wall motion "score" for one or more regions of the heart wall.").
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 detecting abnormalities in an ultrasound heart image, as disclosed in Allan, by also detecting wall motion abnormalities in the ultrasound heart image, as disclosed in Krishnan. One of ordinary skill in the art would have been motivated to make this modification in order to assess the condition of the heart wall on a regional basis, as taught in Krishnan (see para. 0045).
Furthermore, regarding claims 4 and 11, Krishnan further teaches wherein the anomaly is one of: hypokynesis, dyskynesia or paradoxical motion of any part of a left ventricular wall and/or a septum (see para. 0045 "In particular, in one exemplary embodiment of the invention, the classification results will be presented to the user as a wall motion "score" for various segments of the left ventricle of the heart in accordance with a recommended standard of the American Society of Echocardiography (ASE)...analyzing such image data to assign each segment a wall motion score as follows: 1=normal; 2=hypokinesis; 3=akinesis; 4=dyskinesis; and 5=aneurysmal.").
The motivation for claims 4 and 11 was shown previously in claims 3 and 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ionasec et al. (US 20140052001 A1, published February 20, 2014) discloses a series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier.
Lu et al. (US 20090074280 A1, published March 19, 2009) discloses one or more standard view planes of a heart volume are detected as a function of the output of the classifiers.
Abolmaesumi et al. (US 20190125298 A1, published May 2, 2019 with a priority date of April 21, 2016) discloses each neural networks associated with one of the plurality of predetermined echocardiographic image view categories.
Voigt et al. (US 20150366532 A1, published December 24, 2015) discloses a model trained to detect the valve and/or regurgitant orifice from ultrasound data.
Hope Simpson et al. (US 20190336107 A1, published November 7, 2019 with a priority date of January 5, 2017) discloses trained neural network may be used to segment and identify cardiac chambers directly from the raw or beamformed data.
Pagoulatos et al. (US 20170262982 A1, published September 14, 2017 with a priority date of March 9, 2016) discloses based on acquired ultrasound images determined to substantially accurately depict or represent a particular view of a heart, the artificial intelligence approaches may further indicate a particular problem with the mitral valves in the heart.
Rothberg et al. (US 20170360403 A1, published December 21, 2017 with a priority date of June 20, 2016) discloses a neural network that is trained to identify an anatomical view contained in the ultrasound image, and another neural network trained to identify an anatomical feature in an ultrasound image.
Avendi et al. (US 20190311478 A1, published October 10, 2019 with a priority date of July 8, 2016) discloses convolutional neural networks to automatically detect the anatomical object and the surrounding tissue of the parameter space of the image, and automatically locating and segmenting, via additional convolutional neural networks, the anatomical object and surrounding tissue of the parameter space of the image
S. Snare et al, “Automatic Real-time View Detection”, 2009 IEEE International Ultrasonics Symposium, pp. 1-4, Sept. 2009 discloses classifying an echocardiographic view as either an apical two chamber view, four chamber view or long axis view.
A. Roy et al, “Data Model of Echocardiogram Video for Content Based Retrieval”, Proc. of Med. Informatics and Telemed. (ICMIT), pp. 1-5, Dec. 2006 discloses detecting and classifying views of echocardiogram, and classify the echo video frames into two classes, namely, systole and diastole.
A. Roy et al, “State-Based Modeling and Object Extraction From Echocardiogram Video”, IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 3, pp. 366-376, May 2008 discloses for view classification, an artificial neural network is trained with the histogram of a region of interest of each video frame.
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/N.C./Examiner, Art Unit 3798
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