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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10 March 2026 has been entered.
Response to Amendment
In response to the amendment of 10 March 2025, the rejection of claims 1-6, 8-17, and 19-20 are withdrawn. Claims 7 and 18 have been canceled. Claim 21, previously presented as a part of the after-final amendment, has been entered and is now under examination. However, upon further consideration, a new ground of rejection is made in view of Freiman and in further view of van Assen et al. (full citations below and in the PTO-892 form).
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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: “evaluation unit” in claim 12; and “imaging device”, “control device”, and “evaluation device” within claims 13 and 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-6, 8-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Freiman et al. (US PG Pub 20200375564, hereinafter “Freiman”) in view of van Assen et al. (“Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study”, European journal of radiology, 134, 109428, hereinafter “van Assen”).
Regarding claim 1, Freiman discloses a computer-implemented method for evaluating an image data set of an imaged region (para. 0001, “The invention relates to the field of digital image processing. More specifically it relates to myocardial computed tomography (CT) perfusion image synthesis based on spectral CT data, e.g. coronary CT angiography data.”), wherein, from the image data set, different processed data sets having different image data content are determinable by image processing (para. 0108, “For example, the obtaining 301 the spectral CT volumetric image data may comprise applying spectral processing techniques 302 as known in the art, to provide 303 the contrast-enhanced volumetric image and the baseline volumetric image, e.g. a virtual non-contrast-enhanced volumetric image and an iodine volumetric image”), and quantitative evaluation result data describing at least one of at least one dynamic feature or at least one static feature of the imaged region is determined by applying an evaluation algorithm (para. 0034, “The method comprises simulating a coronary flow based on the three-dimensional coronary tree model. The method comprises generating a perfusion image representative of a blood distribution in tissue at…least one instant in time taking at least the baseline volumetric image and the coronary flow simulation into account.”), the method comprising:
determining, from the image data set, at least two processed data sets having different image data content, the at least two processed data sets including at least one of a functional image, a virtual non-calcium image, or a virtual non-iodine image (para. 0108, “For example, the obtaining 301 the spectral CT volumetric image data may comprise applying spectral processing techniques 302 as known in the art, to provide 303 the contrast-enhanced volumetric image and the baseline volumetric image, e.g. a virtual non-contrast-enhanced volumetric image and an iodine volumetric image”);
applying a first sub-algorithm, of the evaluation algorithm, to a first of the at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets, the first intermediate result including a segmented vessel tree (para. 0069, “The flow simulator 12 may comprise a coronary tree segmentation unit 14 for generating the three-dimensional coronary tree model based on the volumetric image data, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”);
and determining the quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, the third sub-algorithm using both the first intermediate result as input data and performing at least one fluid flow simulation in the segmented vessel tree to determine at least one fluid flow parameter as an evaluation result (paras. 0067-0075, with specific reference to para. 0067, “The image processing device 10 also comprises a flow simulator 12 for generating or receiving, as input, a three-dimensional coronary tree model based on the volumetric image data and for simulating a coronary flow, e.g. generating a coronary flow simulation, based on the three-dimensional coronary tree model, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”; and para. 0075, “The flow simulation may be performed in accordance with methods known in the art, e.g. using a 3D computational fluid dynamics (CFD) approach, or a reduced-order approach, e.g. as known in the art for FFR-CT analysis.”).
Specifically, Freiman discloses a computed tomography-based method for exploration and quantification of coronary artery calcification. Although Freiman discloses different potential parameters for the boundary condition of coronary flow model, Freiman does not explicitly disclose:
applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets, wherein the second sub-algorithm determines the second intermediate result without using the first of the at least two processed data sets or the first intermediate result; or
wherein the at least one fluid flow simulation is at least partly parametrized using the second intermediate result within the quantitative evaluation result data of the third sub-algorithm of the evaluation algorithm.
However, van Assen explicitly discloses applying an evaluation algorithm to a data set to determine a result relating to image data content of the data set, wherein the quantitative evaluation result is the result of only the data set image data and not using the result of another algorithm, and wherein the determined quantitative evaluation result data is known in the art to be applicable to FFR-CT and general coronary flow simulation (pg. 2, Materials and Methods, section 2.3.2, “The coronary calcium volume was obtained using a deep-learning based algorithm”; and section 2.4, “In this study, the fully automated measurements on the non- triggered acquisitions were compared with…the manual measurements on the same acquisitions…and…the Agatston score of the corresponding ECG-triggered cardiac acquisitions…and…with manual qualitative calcium scoring” for validation of the determined calcification). The disclosure of van Assen does not use contrast-enhanced CT data, thus satisfying the limitation of independent claim 1 wherein the second intermediate result is determined without using another processed data set or the aforementioned coronary tree.
Specifically, van Assen discloses a deep learning algorithm for application on non-contrast chest or cardiac CT data sets to determine a calcification level or total calcification amount from the image data. Calcification is a widely-recognized metric for parametrizing and classifying FFR-CT (see, at least, instant application para. 0020, “The second intermediate result may, for example, relate to a quantitative concentration of material, for example calcium, such that it can be determined how much of the material is present in different segmented features, such that quantitative evaluation result data may be provided as amounts or concentrations of material in certain segmented features.”). Both Freiman and van Assen disclose coronary CT analysis methods of vascular flow, specifically directed to measuring atherosclerotic extent. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention that the disclosure of van Assen directed to calcification detection in non-contrast chest and cardiac CT could be integrated within the method and system of Freiman as the application of a known technique to a known system ready for improvement; specifically, the additional parameter of calcification disclosed by van Assen would enable increased accuracy of the FFR simulation, enabling more precise flow rate calculation.
Regarding Claims 12, 13, and 14, Examiner is interpreting the “evaluation unit” under 35 U.S.C. 112(f) as a combination of the CPU or ALU as described in para. 0069, with the RAM or ROM described as in para. 0074 the algorithm described in the Specification (for example, the algorithm to relate evaluation results with segmented features and equivalents) that causes the CPU to perform the claimed function. Additionally, the “imaging device” is interpreted as the computed tomography device described in para. 0034, and the “evaluation device” of claim 14 is the combination of the CPU/ALU of para. 0069 with the RAM or ROM of para. 0074 containing the algorithm of the Specification.
Claims 12 and 15 are rejected, mutatis mutandis, for reasons similar to claim 1. Regarding both claims 12 and 15, Freiman further discloses an (image) processor (para. 0098, “ The computing system 116 may also comprise at least one processor 124, such as a central processing unit (CPU), a microprocessor, a dedicated application-specific integrated circuit (ASIC) for processing and/or an appropriately configured programmable hardware processor such as a field-programmable gate array. ”), and a memory storing computer-executable instructions (para. 0098, “The computing system may comprise a computer readable storage medium 126, e.g. a non-transitory memory such as a physical digital memory. The computer readable storage medium 126 may store computer readable instructions 128 and data 130.”).
Regarding claim 2, Freiman and van Assen disclose all limitations of claim 1. Freiman further discloses that the image data set is a multi-energy computed tomography data set (Paras. 0060 and 106, Figure 2 element 110, and figure 4 element 301), and at least one of the at least two processed data sets is determined at least one of based on a material decomposition or as a monoenergetic image (Paras. 0059, 0063, and 0095, figure 2 element 112).
Regarding claim 3, Freiman and van Assen disclose all limitations of claim 2. Freiman further discloses acquiring the image data set using at least one of a source-based or a detector-based multi-energy computed tomography (paras. 0059 and 0060, element 110 fig 2).
Regarding claim 4, Freiman and van Assen disclose all limitations of claim 1. Freiman further discloses wherein the image data set is an angiography data set (para. 0061, “The spectral CT volumetric image data may for example comprise, or consist of, computed tomography cardiac angiography data, e.g. in accordance with a standard spectral CT acquisition protocol as known in the art for cardiac angiography.”).
Regarding claim 5, Freiman and van Assen disclose all limitations of claim 1. Freiman further discloses wherein the first intermediate result describes a segmentation result regarding multiple segmented features (para. 0069, “The flow simulator 12 may comprise a coronary tree segmentation unit 14 for generating the three-dimensional coronary tree model based on the volumetric image data, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”).
Furthermore, the combination of Freiman and van Assen according to the rationale of claim 1 discloses wherein the third sub-algorithm (the fractional flow reserve simulation of Freiman, disclosed in at least paras. 0067-0075, with specific reference to para. 0075, “The flow simulation may be performed in accordance with methods known in the art, e.g. using a 3D computational fluid dynamics (CFD) approach, or a reduced-order approach, e.g. as known in the art for FFR-CT analysis.”) assigns data of the second intermediate result (the calcification score of van Assen, disclosed at least within pg. 2, sections 2.3.2 and 2.4), to segmented features to yield quantitative segmented feature-specific evaluation results (Freiman paras. 0080 and 0109-0111, wherein the segments, registered to fit on top of the American Heart Association’s 17 segment model, is then used within a flow simulation delivering branch-specific fractional flow rates).
Regarding claim 6, Freiman in view of van Assen discloses all limitations of claim 1. Freiman further discloses wherein the segmented features are anatomical features including at least one of vessels or vessel segments of a vessel tree (para. 0069, “The flow simulator 12 may comprise a coronary tree segmentation unit 14 for generating the three-dimensional coronary tree model based on the volumetric image data, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”, wherein the coronary tree comprises a plurality of blood vessels).
Regarding claims 8 and 19, Freiman in view of van Assen discloses all limitations of claims 1 and 5, respectively. Freiman further discloses wherein the segmented vessel tree is a blood vessel tree, the fluid is blood, and the at least one fluid flow includes a fractional flow reserve (para. 0069, “The flow simulator 12 may comprise a coronary tree segmentation unit 14 for generating the three-dimensional coronary tree model based on the volumetric image data, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”, wherein the coronary tree comprises a plurality of blood vessels containing blood; and para. 0075 explicitly disclosing FFR-CT).
Regarding claims 9 and 20, Freiman in view of van Assen discloses all limitations of claims 1 and 5, respectively. Freiman further discloses wherein at least a part of the first intermediate result is used as a quantitative input data to at least one disease value estimation of the third sub-algorithm (paras. 0006-0008 for the disclosure of FFR-CT’s usage as a disease value metric for coronary artery disease and CAD-adjacent ailments; and para. 0069 for the first intermediate result’s application to the third sub-algorithm, “The flow simulator 12 may comprise a coronary tree segmentation unit 14 for generating the three-dimensional coronary tree model based on the volumetric image data, e.g. by taking the contrast-enhanced volumetric image into account, e.g. based on the contrast-enhanced volumetric image.”).
Furthermore, the combination of Freiman and van Assen according to the rationale of claim 1 discloses wherein the third sub-algorithm (the fractional flow reserve simulation of Freiman, disclosed in at least paras. 0067-0075, with specific reference to para. 0075, “The flow simulation may be performed in accordance with methods known in the art, e.g. using a 3D computational fluid dynamics (CFD) approach, or a reduced-order approach, e.g. as known in the art for FFR-CT analysis.”) may also be parametrized by the data of the second intermediate result (the calcification score of van Assen, disclosed at least within pg. 2, sections 2.3.2 and 2.4).
Regarding claim 10, Freiman in view of van Assen discloses all limitations of claim 1. Freiman further discloses wherein the third sub-algorithm determines at least one two-dimensional output image visualizing the quantitative evaluation result data (paras. 0110-0114, wherein a perfusion image is generated as a result of the coronary flow (FFR-CT) simulation, wherein multiple 2D perfusion images are determinable based on the sequence of CT images).
Regarding claim 11, Freiman in view of van Assen discloses all limitations of claim 10. Freiman further discloses wherein at least one of an orientation, a viewpoint, a shown imaged region portion of the at least one two-dimensional output image based on the second of the at least two processed data sets, or the second intermediate result is chosen based on the first intermediate result (paras. 0110-0114, wherein a perfusion image is generated as a result of the coronary flow (FFR-CT) simulation, wherein multiple 2D perfusion images are determinable based on the sequence of CT images; specifically, the at least one shown imaged region portion of the at least one 2D perfusion image is chosen and determined based on the first intermediate result).
Regarding claim 13, Freiman in view of van Assen discloses all limitations of claim 12. Freiman further discloses an imaging device with a control device comprising the evaluation device of claim 12 (paras. 0097, “The imaging system 100 may be operably connected to a workstation, e.g. computing system 116, such as a computer, that may comprise an input/output (I/O) interface 118 for facilitating communication with the spectral CT scanner.”).
Regarding claim 14, Freiman in view of van Assen discloses all limitations of claim 1. Freiman further discloses a non-transitory computer-readable storage medium storing a computer program that, when executed by at least one processor at an evaluation device, causes the evaluation device to perform the method of claim 1 (para. 0098, “The computing system may comprise a computer readable storage medium 126, e.g. a non-transitory memory such as a physical digital memory. The computer readable storage medium 126 may store computer readable instructions 128 and data 130.”).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Freiman in view of van Assen and in further view of Min (US Pat. No. 11,094,061).
Regarding claim 16, Freiman and van Assen teach all limitations of claim 3. Freiman and van Assen do not teach wherein the acquiring acquires the image data set using a counting x-ray detector.
However, Min discloses wherein the acquiring acquires the image data set using a counting x-ray detector. Specifically, Min discloses a method in the same field of endeavor Freiman and van Assen, wherein calcification is detected, quantified, and labeled within a medical imaging dataset and subsequent treatment is suggested. Min discloses dual-energy computed tomography (DECT, as disclosed in the method of Freiman and van Assen) and a counting detector as embodiments of the acquisition method of the medical image dataset used within the workflow of the method of calcification quantification in coronary artery images (Column 16, lines 25-56 and fig. 1 image element 104). One having obvious skill in the art would have recognized that DECT and counting detector acquisition would both produce the image dataset used in the evaluation workflow. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to use a counting detector rather than a dual-energy CT detector as a simple substitution of known elements (in this case, image acquisition modalities) to obtain predictable results.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Freiman in view of van Assen and in further view of Li et al. (“Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve, Radiology 293 (2019)).
Regarding claim 17, Freiman in view of van Assen discloses all limitations of claim 1. The combination of Freiman and van Assen does not explicitly disclose wherein the at least two processed data sets include the functional image, and the functional image is a perfusion image.
However, Li discloses wherein the at least two processed data sets include the functional image, and the functional image is a perfusion image (Page 305, para. 2 and page 306, paras. 4-5). Specifically, Li discloses methods and systems for flow simulation and perfusion imaging evaluation, comparing myocardial perfusion imaging and FFR-CT to determine the more efficient, accurate, and robust method of disease feature evaluation and identification. As a result, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to use the CT perfusion image dataset method of Li as one of the at least two processed datasets within the method of Freiman as modified by van Assen as a simple substitution in the art, as perfusion images are one of many processed image types used for detecting and diagnosing ischemic myocardium (Li page 305 paras. 1-3), as are non-contrast and iodine contrast images. It is predictable that perfusion imaging would have increased speed of acquisition.
Allowable Subject Matter
Claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/ROHAN TEJAS MUKUNDHAN/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698