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 .
Specification
The disclosure is objected to because of the following informalities: typographical and grammatical errors include (i) “attention coefficient / values” which should apparently read “attenuation coefficient / values”; (ii) “photelectric” which should read “photoelectric”; (iii) “medial field” which should read “medical field”; (iv) “system S-SYswitch S” which should read “system S-SYS”; and (v) “caid coefficients” which should apparently read “said coefficients” or “coefficients”.
Appropriate correction is required.
Claim Objections
Claims 3, 6, and 7 are objected to because of the following informalities: the phrase “orientation” should be amended to “an orientation” or “the orientation”, as appropriate, for grammatical clarity. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: informal punctuation in the phrase “computed tomography, scanner”. This should likely be “computed tomography scanner” or “computed tomography, CT, scanner”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 recites determining at least one material cluster and an orientation of the material cluster using a trained machine learning model. However, the specification does not sufficiently describe the trained model architecture, training data, labels, training objective, input features, output representation, or training procedure for determining the claimed material cluster and orientation from spectral data. The specification merely lists ML as an option alongside PCA and segmentation, with no further description of the ML model architecture, training data, training procedure, or how it would output cluster orientation in A-space. Therefore, the disclosure does not reasonably convey possession of, or enable the full scope of, the claimed trained machine learning model functionality.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9, 16, 18-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites "determine an orientation of the target material cluster is based on a value in the at least one auxiliary material cluster". The phrase "a value" is ambiguous. It is unclear if this refers to an attenuation value (HU), a geometric coordinate, a barycenter, or a statistical parameter derived from the cluster. The spec at [0041] and [0136] describes using the “long axis” orientation of the GM/ WM cluster ellipsoid, not merely "a value". A value in a cluster is broader than what the spec supports and lacks reasonable certainty as to its metes and bounds.
Claim 16 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: the phrase “relative to at least other of the material clusters” is grammatically incomplete. It is unclear whether the claim requires orientation relative to one other material cluster, more than one other material cluster, or all other material clusters. This should likely read "relative to at least one other of the material clusters".
Claim 18 recites “projecting the at least one auxiliary material cluster on a subspace defined by the target material cluster”. The disclosure describe such projection in A-space as a dimensionality reduction operation , which is separately claimed in Claim 17 as “dimensionally reducing the spectral data”. Since Claim 18 depends directly from Claim 13 rather than from Claim 17, and does not recite dimensional reduction, it is unclear whether Claim 18 is intended to encompass projection without dimensional reduction or is merely a subset of Claim 17. This internal inconsistency renders the scope of Claim 18 unclear, raising an issue under 35 U.S.C. § 112(b).
Claims 19-21 recite the limitation "when executed by the process" in claims. There is insufficient antecedent basis for this limitation in the claim. Claims 19-21 depend from claim 15 and claim 15 recites “executed by a processor” and does not provide antecedent basis for “the process”.
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 1-13 and 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
This rejection has been made in accordance with the current USPTO subject matter eligibility framework, including MPEP §§ 2103–2106.07, the 2019 Revised Patent Subject Matter Eligibility Guidance, the October 2019 Patent Eligibility Guidance Update, the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the July 2024 AI Subject Matter Eligibility Examples, the August 4, 2025 USPTO memorandum titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101", and the USPTO's guidance concerning Ex parte Desjardins, Appeal No. 2024-000567. The claims have been evaluated under the broadest reasonable interpretation, and the claims have been considered as a whole.
Step 1: Statutory category
Independent claim 1 is directed to a system comprising a processor in communication with memory and therefore falls within the statutory category of a machine. Independent claim 13 is directed to a computer-implemented method for determining a material density map and therefore falls within the statutory category of a process. Independent claim 15 is directed to a non-transitory computer readable storage medium having stored a computer program and therefore falls within the statutory category of a manufacture. Accordingly, the analysis proceeds to Step 2A.
Step 2A, Prong One (Judicial exception)
Independent claim 1 recites: "receive spectral data representable in an at least two-dimensional data space, the spectral data including, or based on, one or more measurements acquired by a spectral imaging apparatus of at least a part of an object in a three-dimensional image domain of the spectral imaging apparatus"; "determine a plurality of material clusters in the at least two-dimensional data space, including a target material cluster indicative of the target material, and at least one auxiliary material cluster indicative of at least one auxiliary material"; "analyze the plurality of material clusters so as to determine a mutual geometrical constellation of the material clusters"; and "determine the material density map based on the mutual geometrical constellation". These limitations recite an abstract idea, namely collecting spectral imaging data, organizing data points into mathematical groupings in a multi-dimensional data space, performing geometric and statistical analysis on those groupings to derive a spatial relationship among them, and calculating a quantitative output map based on that mathematical relationship.
The claim recites data collection, mathematical clustering, geometric analysis, and derivation of a numerical output. The "spectral data", "two-dimensional data space", "material clusters", "mutual geometrical constellation", and "material density map" are used as mathematical constructs and data representations in a data analysis and quantification process. The claim does NOT recite an improvement to the way X-ray or CT imaging data is physically acquired, detected, reconstructed, transmitted, or stored. Rather, the claim uses a generic processor to receive imaging data that has already been acquired, mathematically organize it into clusters in an abstract data space, compute geometric relationships among those clusters, and derive a numerical density map therefrom.
The claim is similar in character to claims that courts have found abstract where the focus is collecting information, mathematically analyzing the information, and presenting or acting on the results of the analysis. In Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), the Federal Circuit recognized that claims directed to collecting data from physical systems, mathematically analyzing that data, and generating output results are abstract. The present claims similarly receive spectral imaging data from a physical system, mathematically organize and analyze the data through clustering and geometric constellation determination, and generate a density map as output. Furthermore, grouping measured attenuation values into categories, identifying the geometric orientation and spatial relationships of those categories in a data space, and using those relationships to compute a proportional output map are processes of mathematical reasoning that can be performed mentally or with pen and paper by a skilled analyst.
Claims 3 and 16 further limit the system and method to determining an orientation of at least one of the plurality of material clusters relative to at least one other of the plurality of material clusters. This limitation recites a geometric or directional mathematical relationship, the angular or directional orientation of one data cluster relative to another within an abstract multi-dimensional data space. This is a mathematical concept that does not transform the underlying abstract character of the claims.
Claims 4, 17, and 20 recite that determining the material density map includes dimensionally reducing the spectral data. Dimensional reduction is a well-known mathematical technique, including but not limited to principal component analysis and linear projection, used to reduce the number of variables in a dataset. This limitation recites a pure mathematical operation applied to abstract data representations and does not alter the abstract character of the claims.
Claims 5, 18, and 21 recite projecting the at least one auxiliary material cluster onto a subspace defined by the target material cluster. Projection onto a subspace is an explicit mathematical operation, a linear algebraic transformation of abstract data points, applied to cluster representations in a multi-dimensional data space. This limitation recites a mathematical formula or calculation.
Claim 6 recites determining at least one of the plurality of material clusters and their orientation by one or more of principal component analysis, a trained machine learning model, or a segmentation operation. These limitations recite the use of mathematical models and statistical algorithms, specifically PCA, machine learning inference, and segmentation as tools for performing the abstract clustering and orientation-determination task. The claim does not recite any specific unconventional hardware architecture or technical improvement to machine learning technology itself.
Claim 7 recites a user interface configured to allow a user to define the plurality of material clusters and orientation of the plurality of material clusters based on visual representation of the spectral data on a display. This limitation recites data collection and presentation, specifically the gathering of user-defined parameters through a generic graphical interface, which is a conventional computer-implemented externalization of a mental process.
Claims 9 and 10 recite that the orientation of the target material cluster is based on a value in the at least one auxiliary material cluster, and that the material density map is determined separately for different parts of the three-dimensional image domain, respectively. These limitations recite mathematical dependencies between numerical values in data clusters and iterative application of the same abstract process to partitioned subsets of data, mathematical operations that do not alter the abstract character of the claims.
Claim 11 recites that the at least one auxiliary material includes one or more of grey matter and white matter, and that the target material includes water or cerebrospinal fluid. This limitation specifies the identity of the physical materials whose abstract data representations are being processed. Limiting the abstract idea to the particular field-of-use of brain imaging for stroke or edema diagnosis does not integrate the abstract idea into a practical application. See Recentive Analytics, Inc. v. Fox Corp., 97 F.4th 1307 (Fed. Cir. 2024).
The character of the elected claims as a whole is spectral imaging data collection, mathematical cluster determination in an abstract data space, geometric constellation analysis, dimensional reduction and projection operations, and numerical density map derivation — not an improvement to CT scanner hardware, X-ray detector technology, tomographic reconstruction algorithms, computer memory architecture, or machine learning model design itself. Independent claims 13 and 15 recite substantially the same abstract idea in method and computer-program-product form, respectively. Merely implementing the same mathematical clustering, geometric analysis, and density map derivation process using generic computer-implemented steps or a generic storage medium does not avoid the judicial exception.
Accordingly, claims 1–13 and 15–21 recite an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two (Practical Application)
The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application.
The recited "processor", "memory", "spectral data", "two-dimensional data space", "material clusters", "mutual geometrical constellation", "material density map", "principal component analysis", "trained machine learning model", "user interface", and "non-transitory computer readable storage medium" amount to data objects, mathematical constructs, generic computer components, and conventional implementation tools for the abstract clustering, geometric analysis, and density map derivation concept.
The claims do NOT recite a particular improvement to spectral imaging or computed tomography technology. They do not improve how X-ray photons are detected, how raw projection data is acquired, how spectral decomposition is physically performed at the detector level, or how tomographic reconstruction is computed. Critically, the spectral imaging apparatus is NOT a structural element of independent claims 1, 13, or 15. The apparatus is referenced only as the origin of the input data, the claims recite "spectral data including, or based on, measurements acquired by a spectral imaging apparatus", but impose no structural or operational requirements on that apparatus. Merely referencing a real-world data source does not ground an otherwise abstract claim in a practical application. See Two-Way Media Ltd. v. Comcast Cable Communications, 874 F.3d 1329 (Fed. Cir. 2017).
The claims further do not recite a particular improvement to artificial intelligence or machine learning technology. Claim 6 recites PCA, a trained machine learning model, and segmentation as optional tools for performing the abstract clustering task. The claims do not train a model in a novel way that improves computational operation, modify model architecture to reduce processing burden, reduce storage requirements, preserve prior model knowledge, or improve inference speed by any specific claimed technical mechanism. The machine learning model is recited purely functionally as a mathematical tool for identifying cluster geometry in an abstract data space.
This analysis is consistent with the USPTO's 2024 AI subject matter eligibility guidance and AI examples, which emphasize that AI-related claims may be eligible when they recite a specific technological improvement or otherwise integrate a judicial exception into a practical application. The present claims do NOT recite such a specific technological improvement. Instead, the claims use a generic processor to receive spectral imaging data, apply mathematical clustering and geometric analysis in an abstract data space, and output a numerical density map.
This case is distinguishable from Ex parte Desjardins. In Desjardins, the claims were found to reflect an improvement in machine learning technology itself, including training a machine learning model on a series of tasks while preserving prior knowledge and reducing complexity and storage burdens. Here, the claims do NOT recite a particular parameter-update mechanism, memory-saving arrangement, or data structure that improves the physical or computational operation of a machine learning model or of the spectral imaging apparatus. The claimed clustering, geometric constellation analysis, and projection operations merely automate the mathematical task of deriving a material density map from spectral data in an abstract data space. Nor does limiting the abstract idea to the environment of medical brain imaging for stroke or edema diagnosis make the claims eligible. In Recentive Analytics, Inc. v. Fox Corp., 97 F.4th 1307 (Fed. Cir. 2024), the Federal Circuit rejected the argument that applying an abstract mathematical process to a new field of use was sufficient for eligibility where the claims did not recite a technical improvement to the underlying process itself. Similarly here, applying mathematical clustering and geometric analysis to spectral CT attenuation data for water map generation is a field-of-use limitation, not an integration of the abstract idea into a practical application.
Accordingly, the claims do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Step 2B: (Inventive Concept)
The additional elements, considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The claims use generic computer components to perform ordinary computer functions, including receiving spectral imaging data, performing mathematical clustering operations in an abstract data space, computing geometric relationships among data clusters, applying dimensional reduction and linear projection, and outputting a numerical density map. These are conventional data processing and mathematical operations performed using generic computer technology and do not add significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014).
The ordered combination also does not provide an inventive concept. The ordered combination follows the abstract idea itself: receive spectral imaging data, mathematically cluster the data in an abstract attenuation space, determine the geometric orientation and spatial relationships of the clusters, and compute a density map by mathematical projection and dimensional reduction. This is no more than the abstract mathematical concept implemented on generic computer components.
Dependent claims 2–12, 16–21 recite additional limitations including visualizing the map on a display, determining cluster orientation, performing dimensional reduction, projecting onto a subspace, using PCA or a trained machine learning model or segmentation, providing a graphical user interface, specifying CT or X-ray or tomosynthesis apparatus as a data source, determining map values based on auxiliary cluster values, computing the map for different spatial regions, and specifying grey matter, white matter, water, and cerebrospinal fluid as the relevant materials. These limitations merely specify generic display components, well-known mathematical and statistical techniques, conventional imaging apparatus types as data sources, and field-of-use restrictions on the abstract data content. None of these limitations, individually or in ordered combination, transform the abstract clustering, geometric analysis, and density map derivation concept into patent-eligible subject matter.
Accordingly, claims 1–13 and 15–21 are directed to a judicial exception without significantly more and are therefore rejected under 35 U.S.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 1–4, 6, 8, 10, 12–13, 15–17, and 19–20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Zamyatin (Zamyatin et al, US 2010/0328313 A1, 2010).
Regarding claim 1, Zamyatin teaches a system for determining a material density map for a target material, the system comprising:
a processor in communication with memory, the processor configured to:
( [0052-0054], [0059-0060], [Fig. 3]: Zamyatin discloses a dual-energy X-ray CT apparatus/ scanner system, including a reconstruction device 114 with various software and hardware components [processor] connected to a storage device 112 [memory] through a data/control bus. )
receive spectral data representable in an at least two-dimensional data space, the spectral data including, or based on, one or more measurements acquired by a spectral imaging apparatus of at least a part of an object in a three-dimensional image domain of the spectral imaging apparatus;
( [0012-0015], [0053-0054], [0078-0079], [Fig. 3-5], [Fig. 9, Step S100]: Zamyatin discloses acquiring dual-energy CT data as a subject portion is scanned by a dual-energy CT apparatus/scanner. Zamyatin further discloses plotting the acquired image data points in a vector plot based on the dual-energy data, wherein the vector plot is a two-dimensional histogram having HU values in both axes, such as low-energy and high-energy HU values corresponding to each pixel or detector element. )
determine a plurality of material clusters in the at least two-dimensional data space, including a target material cluster indicative of the target material, and at least one auxiliary material cluster indicative of at least one auxiliary material;
( [0012-0015], [0067-0072], [0074], [0082], [Figs. 6A-7C], [Figs. 10A, 11A, 16A, 18A, 21]: Zamyatin discloses that image data points corresponding to a common material tend to cluster together in the vector/ density plot, that density plots show good separation of clusters that represent different materials, and that clustering of a certain material is visualized by a predetermined visualization scheme. Zamyatin further discloses identifying/ separating clusters for material separation, wherein each cluster represents a clinically significant material. Zamyatin gives examples of material clusters including water, bone, fat, blood, calcium, and iodine contrast agent. )
analyze the plurality of material clusters so as to determine a mutual geometrical constellation of the material clusters; and
( [0068-0069], [0074-0076], [0089], [Figs. 8, 21]: Zamyatin discloses analyzing material clusters in the density/ vector plot by determining cluster boundaries, distances/ probabilities relative to material clusters, lines connecting clusters, and Gaussian cluster parameters. Zamyatin further discloses that each cluster in the density plot may be represented by a 2D Gaussian function having widths and rotation, with a rotation angle estimated for the 2D normal distribution. Zamyatin also discloses vector arithmetic for estimating iodine contrast agent concentration in each cluster. Therefore, Zamyatin teaches determining the relative geometric relationship, including position, boundary, direction/ orientation, and cluster relationship, of the material clusters. )
determine the material density map based on the mutual geometrical constellation.
( [0064-0067], [0081-0082], [Figs. 4B-4C, 16A-16C, 18A-18B]: Zamyatin discloses generating a density plot, also called a density map, from the vector plot based on density values representing surrounding image data points. Zamyatin further discloses that clustering of material is visualized in the density plot and that a material separation map is formed based on the separated clusters in the density plot, with each material visualized by a distinct cue such as an assigned color. )
Regarding claim 2, Zamyatin teaches the system of claim 1, wherein the processor is further configured to visualize the material density map in the three-dimensional imaging domain on a display.
( [0052], [0058-0060], [0067-0069], [0081-0082], [Figs. 3, 16A-16C, 18A-18B]: Zamyatin discloses a multi-slicedual-energy X-ray CT apparatus/scanner including a gantry, X-ray tube, X-ray detector, data acquisition system, reconstruction device, storage device, display device, and input device. Zamyatin further discloses that the subject is moved along the rotation axis during scanning, thereby acquiring CT data for reconstruction in a CT imaging domain [corresponding to a three-dimensional image domain]. Zamyatin teaches that image data points from image space, corresponding to pixels or detector elements, are mapped into a vector plot having low- and high-energy HU-value axes, and that a density plot/ density map is generated from the vector plot. Zamyatin further teaches visualizing material clusters using visual cues such as grayscale, RGB, HSL, and color-coding schemes, and forming a material separation map based on separated clusters in the density plot, wherein each material is visualized by a distinct cue in a reconstructed/ 3D domain bridge image. Zamyatin describes taking 3D image data, using a 2D density plot for material classification, and re-mapping these classified materials back onto the spatial image data for display [same Applicant's overall pipeline described in disclosure]. )
Regarding claim 3, Zamyatin teaches the system of claim 1, wherein, to determine the mutual geometrical constellation, the processor is further configured to determine orientation of at least one of the plurality of material clusters relative to at least one other of the plurality of material clusters.
( [0068-0069], [0074-0076], [Figs. 8, 21]: Zamyatin discloses determining cluster boundaries and using Gaussian decomposition when clusters overlap. Zamyatin further discloses that each cluster in the density plot is represented by a 2D Gaussian function that varies in two widths and rotation, and that the rotation angle of the 2D normal distribution is estimated; wherein tan 2θ is a function of CoVar(H₁, H₂), the covariance between the HU distributions of the clusters, expressly computing the rotational orientation of one cluster relative to the other clusters in the 2D HU space. Zamyatin also discloses cluster relationships using distances/ probabilities to material clusters and lines connecting clusters, thereby determining orientation/ mutual geometric relationship of material clusters in the data space. )
Regarding claim 4, Zamyatin teaches the system of claim 1, wherein, to determine the material density map, the processor is further configured to dimensionally reduce the spectral data.
( [0013], [0015], [0077-0079], [Fig. 9]: Zamyatin discloses performing data-domain dual-energy decomposition on the dual-energy data to generate two basis images having two basis image values. Zamyatin further discloses that the data-domain decomposition may generate two basis images including water/ bone images, Z-number/ density images, and Compton/ photoelectric-effect images, and that the resulting two basis image data are used to construct a two-dimensional vector plot/ histogram; thereby reducing spectral CT data to a lower-dimensional two-basis/ vector-plot representation for generating the density/ material map. )
Regarding claim 6, Zamyatin teaches the system of claim 1, wherein the processor is configured to determine at least one of the plurality of material clusters and orientation of the at least one of the plurality of material clusters by one or more of: i) principal component analysis, PCA, ii) a trained machine learning model, and iii) segmentation operation.
( [0070], [0074-0076], [0081-0082]: Zamyatin discloses determining cluster boundaries and separating clusters using clustering image-processing operations including thresholding, region growing, and Gaussian decomposition. Zamyatin further discloses applying the separated clusters to image data to generate a material separation map. The region-growing and cluster-boundary determination operations teach a segmentation operation for determining material clusters and/or their orientation/ geometric parameters.)
Regarding claim 8, Zamyatin teaches the system of claim 1, wherein the spectral imaging apparatus is one of: i) an X-ray imager, ii) a computed tomography, scanner, or iii) a tomosynthesis scanner.
( [0052-0058], [Fig. 3]: Zamyatin discloses a multi-slice dual-energy X-ray CT apparatus or scanner having an X-ray tube, X-ray detector, data acquisition system, preprocessing device, reconstruction device, storage device, display device, and input device. Therefore, Zamyatin teaches that the spectral imaging apparatus is a computed tomography scanner and X-ray imaging apparatus. )
Regarding claim 10, Zamyatin teaches the system of claim 1, wherein the processor is configured to determine the material density map separately for different parts of the three-dimensional image domain.
( [0062-0065], [0079-0082], [Figs. 4A-4C, 16A-16C, 18A-18B]: Zamyatin discloses that each spatial data point represents a corresponding pixel or detector element in image space, and that each pixel/ data point has corresponding low/ high HU values mapped into the vector plot. Zamyatin determines a density value for each image data point, generates the density plot based on those density values, and forms a material separation map based on the separated clusters; the cluster separation result is applied per-pixel/ per-voxel across the full reconstructed image [ reconstruction in a CT imaging domain corresponding to a three-dimensional image domain ]. Thus, Zamyatin determines the material density/ material separation map separately for different image points or regions of the reconstructed CT [three-dimensional] image domain. )
Regarding claim 12, Zamyatin teaches the system of claim 1, further comprising the spectral imaging apparatus.
( [0052-0058], [Fig. 3]: Zamyatin discloses a dual-energy X-ray CT apparatus/ scanner including the gantry, X-ray tube, X-ray detector, data acquisition system, preprocessing device, reconstruction device, storage device, display device, and input device; all within a single complete integrated dual energy CT system. In medical imaging and radiology, the terms "dual-energy CT" (DECT) and "spectral CT" are frequently used interchangeably. )
Regarding claims 13, 15–17 and 19–20, the rationale provided in the rejection of claims 1–4, 6, 8, 10, and 12 is incorporated herein. In addition, Zamyatin teaches a dual energy CT system including a reconstruction device 114 having “various software and hardware components”, as well as a storage device 112, system controller 110, display device 116, and input device 115 connected through a data/ control bus ( [0052-0060], [Fig. 3] ). Accordingly, the system of claims 1–4, 6, 8, 10, 12 corresponds to the method of claims 13 and 16–17, as well as the non-transitory computer readable storage medium of claims 15 and 19–20 and performs the steps disclosed herein. Therefore, the claims are all rejected.
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.
Claims 5, 7, 9, 11, 18 and 21 are rejected under 35 U.S.C. §103 as being unpatentable over Zamyatin in view of Naruto (Naruto, et al. “Dual Energy Computed Tomography for the Head.” Japanese Journal of Radiology, vol. 36, no. 2, 9 Nov. 2017, pp. 69–80, 2017), as provided by Applicant’s disclosure filed on 06/12/2024.
Regarding claim 5, Zamyatin teaches the system of claim 4, wherein, to determine the material density map, the processor is further configured to
Zamyatin discloses material cluster separation in a density plot using Gaussian decomposition and region growing to separate material clusters, but does not expressly disclose where Naruto teaches:
project the at least one auxiliary material cluster on a subspace defined by the target material cluster.
( [Pages. 76–77], [Fig. 9]: Naruto teaches dual-energy CT material decomposition in a high-kV/ low-kV orthogonal diagram, where voxel coordinates are represented in terms of material-specific vectors and the vector length represents the amount of each material in the voxel. Naruto further teaches that, in a three-material decomposition algorithm, all voxels are projected along a material-specific slope to a line connecting two materials. Naruto also teaches the X-Map algorithm, in which gray matter, white matter, and water are represented in the dual-energy CT diagram, and projecting all voxels of the auxiliary material cluster (white matter/ grey matter) along a directional slope onto a baseline subspace defined by the target material cluster(water/ grey matter). )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Zamyatin’s dual-energy CT material-cluster/ density-plot material-separation system by applying Naruto’s known DECT projection technique, in which voxel/ material data are projected along a material-specific slope onto a material-defined baseline or subspace, because both references address dual-energy CT material decomposition/ material separation using high/ low energy CT values in a data space. The modification would have predictably allowed Zamyatin’s separated material clusters to be used to generate a quantitative target-material map, such as Naruto’s X-Map/ water-content map, based on the geometric relationship between target and auxiliary material data.
Regarding claim 7, Zamyatin teaches the system of claim 1, Zamyatin discloses a display device 116 and input device 115 connected to the system controller and reconstruction device [Fig. 3], and expressly discloses that the density plot and material separation map are visualized on a display [Figs. 16A–16C, 17, 18A–18B]. However, Zamyatin does not expressly disclose a graphical user interface through which a user can interactively define the material clusters and their orientations based on visual representation of the spectral data where Naruto teaches:
further comprising a user interface configured to allow a user to define the plurality of material clusters and orientation of the plurality of material clusters based on visual representation of the spectral data on a display, wherein the user interface is a graphical user interface.
( [Pages 76-78], [Fig. 9]: Naruto teaches visually representing material-decomposition geometry in a dual-energy CT diagram, including gray matter, white matter, water, a baseline, and a lipid-specific slope used to determine the X-Map projection. Naruto further teaches that the X-Map is generated using nominal gray-matter/ white-matter values and a lipid-specific slope in the high-kV/ low-kV material-decomposition diagram, constitutes a visual representation of the spectral data from which cluster geometry and orientation are defined. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to implement Zamyatin’s display/ input-device arrangement as a graphical user interface that allows a user to define or adjust material clusters and their orientation/ slope based on the displayed density/ vector plot disclosed in both Zamyatin and Naruto, especially in view of Naruto’s teaching that clinically meaningful material-decomposition geometry, including gray matter, white matter, water, baseline, and lipid-specific slope, is visually defined in the dual-energy CT diagram. The modification would have been the predictable use of a graphical user interface, providing user-adjustable parameters for material-specific vectors and cluster orientations in an interactive GUI is a well-known and routine design choice in medical imaging workstations.
Regarding claim 9, Zamyatin teaches the system of claim 3, wherein the processor is configured to
Zamyatin discloses determining the rotation angle θ of a 2D Gaussian from the covariance CoVar(H₁, H₂) between the HU distributions of material clusters in the density plot [Fig. 8], but does not expressly disclose where Naruto teaches:
determine an orientation of the target material cluster is based on a value in the at least one auxiliary material cluster.
( [Pages 76-78], [Fig. 9]: Naruto teaches that the X-Map projection geometry is based on material values in the dual-energy CT diagram. In particular, Naruto teaches nominal gray-matter and white-matter values at Sn150 kV and 80 kV, and teaches that the lipid-specific slope between the nominal point of gray matter and nominal point of white matter is 2.0. Naruto further teaches projecting all voxels along that lipid-specific slope onto a baseline connected between the nominal point of water and the nominal point of gray matter to generate the X-Map. The projection direction/ orientation of the target material cluster (water/GM baseline) is directly determined from the values (nominal HU positions) of the auxiliary material clusters (grey matter and white matter). )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to determine the orientation/ slope used in Zamyatin’s material-cluster analysis based on values of an auxiliary material cluster, as taught by Naruto, because Naruto expressly uses auxiliary brain-material values, including gray-matter and white-matter values, to define the lipid-specific slope/ orientation for projecting voxel data to obtain a water-content/ virtual gray-matter map. The modification would have predictably improved material-specific map generation in dual-energy CT by defining the projection orientation from clinically relevant auxiliary material values, yields the predictable result of a more robust and patient-specific determination of target cluster orientation.
Regarding claim 11, Zamyatin teaches the system of claim 1,
Zamyatin discloses material separation for clinically relevant materials including fat, water, blood, bone, and iodine contrast agent in the context of head phantom imaging [Figs. 10A, 11A, 12A–12D, 18A–18B], but does not expressly disclose where Naruto teaches:
wherein the at least one auxiliary material includes one or more of grey matter and white matter, and wherein the target material includes water or cerebrospinal fluid.
( [Pages. 71, 76], [Figs. 3, 9, 10-12]: Naruto et al. expressly discloses grey matter, white matter, and water/ cerebrospinal fluid as the specific material clusters in dual energy CT brain imaging. The X-Map algorithm expressly states: "the brain parenchyma mainly consists of three materials: gray matter, white matter and water"and "the X-Map emphasizes water content as a hypo-dense lesion in the brain parenchyma by suppressing a difference in the lipid content between the white matter and the gray matter". Additionally, Naruto et al. discloses the 3MD algorithm applied to "cerebrospinal fluid, hemorrhage, and iodine" for DE brain hemorrhage applications. Figs. 10–12 expressly show the X-Map applied to brain imaging with grey matter and white matter as auxiliary materials and water content (edema) as the target material visualized as a hypo-dense lesion. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply Zamyatin’s dual-energy CT material-cluster/ density-plot material-separation framework to Naruto’s head DECT/X-Map material set, including gray matter, white matter, water, and cerebrospinal fluid, because Naruto teaches that these materials are clinically relevant for brain DECT material decomposition and water/ edema visualization. The modification would have predictably allowed Zamyatin’s material-cluster processing to generate a clinically useful brain water/ CSF material map.
Regarding claims 18 and 21, the rationale provided in the rejection of claim 5 is incorporated herein. In addition, Zamyatin teaches a dual energy CT system including a reconstruction device 114 having “various software and hardware components”, as well as a storage device 112, system controller 110, display device 116, and input device 115 connected through a data/ control bus ( [0052-0060], [Fig. 3] ). Accordingly, the system of claim 5 corresponds to the method of claim 18, as well as the non-transitory computer readable storage medium of claim 21 and performs the steps disclosed herein. Therefore, the claims are all rejected.
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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