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 .
Response to Arguments
Applicant's arguments filed 13 February 2026 have been fully considered but they are not persuasive. An Applicant interview is suggested in this case.
Claims 1-10 are pending in this application and have been considered below.
Argument:
Applicant notes that Claims 1, 4, 6, and 9 are interpreted under 35 U.S.C. §112(f) as including means- plus-function language. Applicant then argues that the above claim limitations should not be interpreted under 35 U.S.C. 112(f) because the claim language, when read in light of the specification, recites sufficiently definite structure for performing the recited functions. In particular, applicant notes that the specification includes appropriate descriptions of structure, material or act corresponding to the above claim limitations.
In the alternative, Applicant argues that, to the extent the Examiner maintains that any of these limitations are governed by §112(f), then, under MPEP §2181, each such limitation must be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof, and the patentability analysis must take these limitations into consideration.
Response:
If Applicant intends to claim a system that is not interpreted under 35 USC 112(f), or rejected under 35 USC 101, Applicant should include claim limitations that recite enough structure that a person of ordinary skill would have all the parts (Such as non-transitory memory, a computer processor, communications circuitry, nuclear medicine imaging emitter, x-ray collector, etc.) to practice the claimed invention.
As currently structured, the claims include nonce words (Modules for…) that require interpretation under 35 USC 112(f), so that definitions within the specification can be used to interpret the claims.
Argument:
Applicant then states that, in Min, the key steps involve analyzing the regions, positions and shapes of plaque in the vessel. In contrast, applicant argues that the system and method of the present application are fundamentally different, as they operate by analyzing the left ventricular myocardium images and the flattened images in both the post-stress form and the rest form.
Response:
Applicants should note that the Examiner is not stating that the applied art anticipates the claimed invention (Thus the rejection is not under 102). The examiner is asserting that the claims, when given their broadest reasonable interpretation, are taught by US Patent Publication 2023 0394663 A1, (Min et al.) as a whole. Min et al. teaches to analyze the left ventricular myocardium image ("In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to identify and/or analyze vessels or plaque, derive one or more quantification metrics and/or classifications, and/or generate a treatment plan," paragraph [0181]).
The Examiner states that in light of MPEP 2111, during patent prosecution, the pending claims must be “given their broadest reasonable interpretation assistant with the specification.” The Examiner has interpreted the claim language in reference to the specification. Because applicant has the opportunity to amend the claims during prosecution, given a claim in its broadest reasonable interpretation will reduce the possibility that the claim, once issued will be interpreted more or broadly than is justified. Although the cited reference is different from the invention disclosed, the language of Applicant's claims is sufficiently broad to reasonably read on the cited reference.
Further, it has been held that nonpreferred embodiments failing to assert discovery beyond that known in the art does not constitute a “teaching away” unless such disclosure criticizes, discredits, or otherwise discourages the solution claimed. In re Susi, 440 F.2d 442, 169 USPQ 423 (CCPA 1971), In re Gurley, 27 F.3d 551, 554, 31 USPQ2d 1130, 1132 (Fed. Cir. 1994), In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004), (see MPEP §2124).
Argument:
Applicant then argues that Min fails to disclose the specific methods and algorithms used to import 3D images of the left ventricular myocardium into spherical coordinate transformation for flattening.
Response:
If applicant wishes to distinguish over US Patent Publication 2023 0394663 A1, (Min et al.) by the specific methods and algorithms used in applicants invention, applicant should include the specific methods and algorithms in the claim language. As currently claimed, US Patent Publication 2023 0394663 A1, (Min et al.) teaches wherein the flattened images comprises data in a 3D spherical coordinate system ("in some embodiments, the system can be configured to utilize a cartesian coordinate system, polar coordinate system, cylindrical coordinate system, spherical coordinate system, homogeneous coordinate system, curvilinear coordinate system," paragraph [0370]).
The Examiner states that in light of MPEP 2111, during patent prosecution, the pending claims must be “given their broadest reasonable interpretation assistant with the specification.” The Examiner has interpreted the claim language in reference to the specification. Because applicant has the opportunity to amend the claims during prosecution, given a claim in its broadest reasonable interpretation will reduce the possibility that the claim, once issued will be interpreted more or broadly than is justified. Although the cited reference is different from the invention disclosed, the language of Applicant's claims is sufficiently broad to reasonably read on the cited reference.
Argument:
Applicant then argues that since Min focuses on analyzing the regions, positions and shapes of plaque in the vessel, that a person skilled in the art would not be motivated to analyze left ventricular myocardial images and flattened images in the post-stress form and rest form as described in the present application, let alone attempt to complete the claimed invention by selecting specific steps/devices from the many different embodiments mentioned in Min and using algorithms not mentioned in Min without any relevant teaching.
Response:
Disclosed examples and preferred embodiments do not constitute a teaching away from a broader disclosure or nonpreferred embodiments. In re Susi, 440 F.2d 442, 169 USPQ 423 (CCPA 1971). “A known or obvious composition does not become patentable simply because it has been described as somewhat inferior to some other product for the same use.” In re Gurley, 27 F.3d 551, 554, 31 USPQ2d 1130, 1132 (Fed. Cir. 1994) (The invention was directed to an epoxy impregnated fiber-reinforced printed circuit material. The applied prior art reference broadly discloses the claimed invention, in accordance with the broadly claimed invention.
There is no requirement that a motivation to make the modification be expressly articulated in the prior art. The test for combining references and embodiments within a reference is what the combination of disclosures taken as a whole would suggest to one of ordinary skill in the art. In re McLaughlin, 170 USPQ 209 (CCPA 1971). References are evaluated by what they suggest to one versed in the art, rather than by their specified disclosures. In re Bozek, 163 USPQ 545 (CCPA 1969). In re Hoeschele, 406 F.2d 1403, 1406-07, 160 USPQ 809, 811-812 (CCPA 1969) (“[I]t is proper to take into account not only specific teachings of the references but also the inferences which one skilled in the art would reasonably be expected to draw therefrom...”).
Argument:
Last, applicant argues that Min does not provide any teaching or suggestion regarding the use of left ventricular myocardium images and the flattened images as input in the deep learning analyzation. Thus applicant concludes a person having ordinary skill in the art would not have a reasonable motivation to replace the information employed by Min (i.e., regions, positions and shapes of plaque) with the left ventricular myocardium images and the flattened images as used in the present application.
In addition, applicants argue that as discussed in the "Discussion" section of the specification of the present application, the system of the present disclosure does not require manual correction of LV contour, the traditional process of PM generation, and quantitative comparison with NDB. Applicants thus point to the prediction result from the enrolled 1861 subjects with reference standard of their ICA results, noting that the technique of the present application outperformed TPD3D in both patient-based analysis and vessel-based analysis.
Response:
US Patent Publication 2023 0394663 A1, (Min et al.) shows wherein the MPI image set comprises 3D images of the post-stress form and the rest form ("the image information 135 may include 2D or 3D image data of a patient, scan information related to the image data, patient information, and other imagery or image related information that relates to a patient," paragraph [0124])
Concerning the applicant’s arguments that the system of the present disclosure does not require manual correction of LV contour, the traditional process of PM generation, and quantitative comparison with NDB. That using left ventricular myocardium images and the flattened images has unique advantages not realized or suggested in US Patent Publication 2023 0394663 A1, (Min et al.), that the reference fails to show certain features of applicant’s invention, it is noted that the features upon which the applicant relies (i.e., PM generation, and quantitative comparison with NDB) 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. In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Information Disclosure Statement
The IDS dated 26 June 2023 that has been previously considered remains placed in the application file.
Specification - Drawings
Acknowledgement is made of the successful petition dated 15 August 2023 to accept color drawings filed 26 June 2023 in this application.
1st Claim Interpretation
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation.
Under SuperGuide Corp. v. DirecTV Enters., Inc., 358 F.3d 870 (Fed. Cir. 2004), “the phrase ‘at least one of’ precedes a series of categories of criteria, and the patentee used the term ‘and’ to separate the categories of criteria, which connotes a conjunctive list. The district court correctly interpreted this phrase as requiring that the user select at least one value for each category; that is, at least one of a desired program start time, a desired program end time, a desired program service, and a desired program type.”, SuperGuide, 358 F.3d at 886.
In this case, in claims 1, 4, 6 and 9, applicant has presented the following categorical list, all of which must be present in order to reject the claim:
“post-stress form and rest form.”
In this case, in claims 1, 4, 6 and 9, applicant has presented the following categorical list, all of which must be present in order to reject the claim:
“left ventricular myocardium images and the flattened images.”
In this case, in claims 3 and 8, applicant has presented the following categorical list, all of which must be present in order to reject the claim:
“left anterior descending, left circumflex and right coronary artery, respectively.”
2nd Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a pre-processing module, for pre-processing in claim 1;
a flattening module, for resampling in claim 1; and
a deep learning module, for taking in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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.
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.
Claims 1-10 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2023 0394663 A1, (Min et al.).
Claim 1
Regarding Claim 1, Min et al. teach a system for predicting obstructive coronary artery disease ("methods described herein are configured to determine a risk of coronary artery disease (CAD)," paragraph [0004]) of a subject in need thereof, comprising:
a pre-processing module, for pre-processing a myocardial perfusion imaging (MPI) image ("the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS)," paragraph [0006]) set of the subject into left ventricular myocardium images ("wherein the left ventricular mass of the subject is determined based at least in part on the medical image of the subject," paragraph [0758]) of post-stress form and rest form, wherein the MPI image set comprises 3D images of the post-stress form and the rest form ("the image information 135 may include 2D or 3D image data of a patient, scan information related to the image data, patient information, and other imagery or image related information that relates to a patient," paragraph [0124]);
a flattening module, for resampling the left ventricular myocardium images into flattened images of the post-stress form and the rest form, wherein the flattened images comprises data in a 3D spherical coordinate system ("in some embodiments, the system can be configured to utilize a cartesian coordinate system, polar coordinate system, cylindrical coordinate system, spherical coordinate system, homogeneous coordinate system, curvilinear coordinate system," paragraph [0370]); and
a deep learning module, for taking the left ventricular myocardium images and the flattened images as input or predicting of the obstructive coronary artery disease of the subject ("In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to identify and/or analyze vessels or plaque, derive one or more quantification metrics and/or classifications, and/or generate a treatment plan," paragraph [0181]).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Systems, Devices, and Methods for Non-invasive Image-based Plaque Analysis and Risk Determination” to create the invention as claimed.
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Min et al. explicitly motivates doing so at least in paragraphs [0006], [0012] and [0876] including “It should be understood, however, that the invention is not to be limited to the particular forms or methods disclosed, but, to the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the various implementations described and the appended claims. Further, the disclosure herein of any particular feature, aspect, method, property, characteristic, quality, attribute, element, or the like in connection with an implementation or embodiment can be used in all other implementations or embodiments set forth herein.” and otherwise motivating experimentation and optimization.
Claim 2
Regarding claim 2, Min et al. teach the system of claim 1, wherein the pre-processing of the pre-processing module comprises steps of: performing a left ventricular myocardium segmentation on the 3D images via a U-net model to obtain the left ventricular myocardium images ("In an example, this analysis can comprise image segmentation, feature extraction, and classification. In some embodiments, ML methods can comprise image feature extraction and image-based learning from raw data," paragraph [0179]); and
performing a rigid registration to align each of the left ventricular myocardium images to a myocardium template according to a left ventricular myocardium segmented therefrom ("automatically and/or dynamically identify from raw medical images the presence and/or parameters of vessels, coronary arteries, and/or plaque," paragraph [0181]).
Claim 3
Regarding claim 3, Min et al. teach the system of claim 1, wherein the deep learning module comprises a disease prediction network and a patent prediction network, wherein the disease prediction network predicts probabilities of obstructive coronary artery disease in left anterior descending, left circumflex and right coronary artery, respectively, for the subject ("the described examples of methods and systems may be used to determine various information relating to one or more portions of a coronary artery where plaque has formed which is then used to determine risks associated with such plaque, for example, whether a plaque formation is a risk to cause an adverse event to a patient," paragraph [0126]), and wherein the patent prediction network predicts a probability of patent coronary arteries for the subject ("performing one or more image analysis techniques to assess a subject, including for example assessing cardiovascular disease risk," paragraph [0437]).
Claim 4
Regarding claim 4, Min et al. teach the system of claim 3, wherein the disease prediction network is takes the left ventricular myocardium images and the flattened images in both the post-stress form and the rest form as input for prediction ("determining, by the computer system, an assessment of a state of cardiovascular disease of the subject based at least in part on analysis of the weighted measure," paragraph [0781] where the analysis teaches input for prediction), and wherein the patent prediction network is takes the left ventricular myocardium image in the post-stress form as input for prediction ("performing one or more image analysis techniques to assess a subject, including for example assessing cardiovascular disease risk," paragraph [0437]).
Claim 5
Regarding claim 5, Min et al. teach the system of claim 3, wherein the disease prediction network and the patent prediction network are convolution neural networks ("the one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which arteries or coronary arteries have been identified," paragraph [0821]).
Claim 6
Regarding claim 6, Min et al. teach a method for predicting obstructive coronary artery disease of a subject in need thereof("methods described herein are configured to determine a risk of coronary artery disease (CAD)," paragraph [0004]), comprising:
having a pre-processing module pre-process a myocardial perfusion imaging (MPI) image set of the subject ("the medical image is obtained using an imaging technique comprising one or more of CT, x-ray, ultrasound, echocardiography, MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS)," paragraph [0006]) into left ventricular myocardium images ("wherein the left ventricular mass of the subject is determined based at least in part on the medical image of the subject," paragraph [0758]) of post-stress form and rest form, wherein the MPI image set comprises 3D images of the post-stress form and the rest form ("the image information 135 may include 2D or 3D image data of a patient, scan information related to the image data, patient information, and other imagery or image related information that relates to a patient," paragraph [0124]);
having a flattening module resample the left ventricular myocardium images into flattened images of the post-stress form and the rest form, wherein the flattened images comprises data in 3D spherical coordinate system ("in some embodiments, the system can be configured to utilize a cartesian coordinate system, polar coordinate system, cylindrical coordinate system, spherical coordinate system, homogeneous coordinate system, curvilinear coordinate system," paragraph [0370]); and
having a deep learning module take the left ventricular myocardium images and the flattened images as input for predicting the obstructive coronary artery disease of the subject ("In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to identify and/or analyze vessels or plaque, derive one or more quantification metrics and/or classifications, and/or generate a treatment plan," paragraph [0181]).
Claim 7
Regarding claim 7, Min et al. teach the method of claim 6, wherein the pre-process of the pre-processing module comprises: performing a left ventricular myocardium segmentation on the 3D images via a U-net model to obtain the left ventricular myocardium images ("In an example, this analysis can comprise image segmentation, feature extraction, and classification. In some embodiments, ML methods can comprise image feature extraction and image-based learning from raw data," paragraph [0179]); and
performing a rigid registration to align each of the left ventricular myocardium images to a myocardium template according to a left ventricular myocardium segmented therefrom ("automatically and/or dynamically identify from raw medical images the presence and/or parameters of vessels, coronary arteries, and/or plaque," paragraph [0181]).
Claim 8
Regarding claim 8, Min et al. teach the method of claim 6, wherein the deep learning module comprises a disease prediction network and a patent prediction network, wherein the disease prediction network predicts probabilities of obstructive coronary artery disease in left anterior descending, left circumflex and right coronary artery, respectively, for the subject ("the described examples of methods and systems may be used to determine various information relating to one or more portions of a coronary artery where plaque has formed which is then used to determine risks associated with such plaque, for example, whether a plaque formation is a risk to cause an adverse event to a patient," paragraph [0126]), and wherein the patent prediction network is predicts a probability of patent coronary arteries for the subject ("performing one or more image analysis techniques to assess a subject, including for example assessing cardiovascular disease risk," paragraph [0437]).
Claim 9
Regarding claim 9, Min et al. teach the method of claim 8, wherein the disease prediction network takes the left ventricular myocardium images and the flattened images in both the post-stress form and the rest form as input for prediction ("determining, by the computer system, an assessment of a state of cardiovascular disease of the subject based at least in part on analysis of the weighted measure," paragraph [0781] where the analysis teaches input for prediction), and wherein the patent prediction network takes the left ventricular myocardium image in the post-stress form as input for prediction ("performing one or more image analysis techniques to assess a subject, including for example assessing cardiovascular disease risk," paragraph [0437]).
Claim 10
Regarding claim 10, Min et al. teach the method of claim 8, wherein the disease prediction network and the patent prediction network are convolution neural networks ("the one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which arteries or coronary arteries have been identified," paragraph [0821]).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2013 0278776 A1 to Guterman et al. discloses automatic left ventricular (LV) inner border detection, the method comprising: performing image mapping on an echocardiogram, to produce a multi-level image map; converting the image map into a binary image, by attributing pixels of one or more darker levels of the image map to the LV cavity and pixels of one or more lighter levels of the image map to the myocardium; applying a radial filter to contours of the myocardium in the binary image, to extract an approximate inner border of the LV; and performing shape modeling on the approximate inner border, to determine the LV inner border.
Non Patent Publication “Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies,” by Apostolopoulos, Ioannis D. et al. discloses reviews of recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed.
US Patent Publication 2024 0193767 A1 to Moulton et al. discloses automatically computing an arterial input function from one or more regions of interest, the method comprising: a. obtaining a plurality of dynamic image data sets comprising volumetric image data from the regions of interest over multiple scanning intervals; b. utilizing an artificial neural network to segment the plurality of dynamic image data sets displaying one or more arterial input function(s) (AIF) in the region(s) of interest; c. automatically estimating, using artificial intelligence, an arterial input function based on plurality of dynamic image data sets combined with one or more time activity curves (TAC) in the region( s) of interest in target organ(s); and d. computing a pre-trained predictive pharmacokinetic AI model arterial input function using time activity curve input associated with region(s) of interest of target organ(s).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEATH E WELLS whose telephone number is (703)756-4696. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/Heath E. Wells/Examiner, Art Unit 2664
Date: 5 March 2026