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 .
PRELIMINARY AMENDMENT
The Examiner acknowledges the preliminary amendments submitted on 06/09/2025 and examined the claims accordingly.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/23/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 15 is objected to because of the following informalities:
Remove “and” at the end of first limitation.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The claim(s) recite(s) a method, and computer-readable storage medium configured to detect a focus of attention. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 10 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES.
Claims 1, 13 and 20 are directed to a method, i.e. process, an apparatus and a method.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The methods in claim 1 and 20 (and apparatus in claim 13) comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding Claims 1 and 13: the method recites the steps (functions) of:
implementing a first level identification process to generate a first set of level identification outputs, the first level identification process including: determining geometrical parameters of the set of anatomical components based on the segmentation data (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method; determining geometrical parameters… based on the segmentation data);
grouping the set of anatomical components into separate levels based on geometrical parameters of the set of anatomical components (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method; grouping into separate levels based on geometrical parameters…)
implementing a second level identification process to generate a second set of level identification outputs, the second level identification process including processing the image data of the set of anatomical components using a machine learning model to generate probability maps for each class of a plurality of classes associated with a set of level types or the set of levels (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method; identification process to generate a second set of level identifications is a mental process that can be done by a human mind. The machine learning model can be a generic computer program that generates a probability map for each class of a plurality of classes associated with a set of level types. The probability map can be generated by a human using a paper and a pen after identifying the second set of levels…)
assigning a level identifier of a level from the set of levels to each anatomical component from the set of anatomical components based on the first set of level identification outputs and the second set of level identification outputs (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method; assigning a level identifier of a level from the set of levels can be performed by a human using a pen and a paper);
generating a visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level identifiers assigned to the set of anatomical components (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method; generating a visual representation of the anatomical structure can be performed by a huma using a pen and a paper…).
Regarding Claim 20: the method recites the additional steps (functions) of: for each anatomical component from the set of anatomical components:
assigning a level or a level type to the anatomical component based on the predicted level or level type for the anatomical component output by processing the subset of 2D images associated with the anatomical component (mental process including observation and evaluation, and can be done mentally in the human mind or generic computers or components configured to perform the method); and
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person could mentally analyze an image and determine a fill level, either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a device/in a device (e.g. processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 13 and 20 does/do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Claims 1 and 13 recites the further limitations of:
receiving image data of a set of anatomical components of an anatomical structure, the anatomical structure including a set of levels (insignificant pre/post-solution extra activity of generating data);
receiving segmentation data identifying the set of anatomical components in the image data (insignificant pre/post-solution extra activity of generating data);
Claim 20 recites the further limitations of:
processing, using a convolutional neural network (CNN) trained to identify the set of levels or level types of the set of levels, each 2D image from the subset of 2D images associated with the anatomical component to output a predicted level or level type for the anatomical component based on the 2D image (insignificant pre/post-solution extra activity of generating data; and the trained CNN is recited with a high level of generality without any specific details on the algorithm or any specific training process that improves the functioning of a computer or improves another technology or technical field, therefore the CNN is a generic computer component as recited. See claims 2 and 3 in example 47 of the AI-Related Subject Matter Eligibility Guidance);
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1, 13 and 20 does/do not recite any additional elements that are not well-understood, routine or conventional. The use of a computer to “receiving, determining, and assigning, etc., as claimed in Claim(s) 1, 13 and 20 is a routine, well-understood and conventional process that is performed by computers.
Thus, since Claims 1, 13 and 20 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claims 1, 13 and 20 are not eligible subject matter under 35 U.S.C 101.
Regarding claims 2 – 12 and 14 – 19: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitations merely recite that the functions are performed by a processing unit and does not demonstrate a technological improvement. The claims are functionally generic with no details about architecture, dataset specifics, or a novel arrangement of components. Since the claims are directed toward an abstract idea (mental process and data manipulation) using conventional tools in a generic way, without integration into a practical application or an inventive concept, they are ineligible 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(g)(1) during the course of an interference conducted under section 135 or section 291, another inventor involved therein establishes, to the extent permitted in section 104, that before such person’s invention thereof the invention was made by such other inventor and not abandoned, suppressed, or concealed, or (2) before such person’s invention thereof, the invention was made in this country by another inventor who had not abandoned, suppressed, or concealed it. In determining priority of invention under this subsection, there shall be considered not only the respective dates of conception and reduction to practice of the invention, but also the reasonable diligence of one who was first to conceive and last to reduce to practice, from a time prior to conception by the other.
A rejection on this statutory basis (35 U.S.C. 102(g) as in force on March 15, 2013) is appropriate in an application or patent that is examined under the first to file provisions of the AIA if it also contains or contained at any time (1) a claim to an invention having an effective filing date as defined in 35 U.S.C. 100(i) that is before March 16, 2013 or (2) a specific reference under 35 U.S.C. 120, 121, or 365(c) to any patent or application that contains or contained at any time such a claim.
Claims 1, 2, 4, 5, 12 – 14, and 16 – 17 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Siemionow et al. (US 20200327721 A1; hereafter referred to as Siemionow).
Regarding Claim 1, Siemionow teaches:
A method, comprising:
receiving image data of a set of anatomical components of an anatomical structure, the anatomical structure including a set of levels (Siemionow, [0037] “In the procedure of level identification, first, in step 101, a 3D scan volume is received, comprising a set of DICOM (Digital Imaging and Communications in Medicine) images of an anatomical bony structure, in this example—a spine, which is to be segmented, such as a set of CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans. The set represents consecutive slices of the anatomical structure (such as one slice shown in FIG. 2A)”);
receiving segmentation data identifying the set of anatomical components in the image data (Siemionow, [0038] “the received images are processed in step 102 to perform autonomous segmentation of the anatomical structure to determine plurality of anatomical components, such as vertebral body 16, pedicles 15, transverse processes 14, lamina 13, articular process 17, spinous process 11 and ribs 18, as shown in FIG. 2B”);
implementing a first level identification process to generate a first set of level identification outputs, the first level identification process including:
determining geometrical parameters of the set of anatomical components based on the segmentation data (Siemionow, [0040] Next, in step 104, geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”); and
grouping the set of anatomical components into separate levels based on geometrical parameters of the set of anatomical components (Siemionow, Fig. 1, step 105, “group anatomy parts to form separate levels based on the anatomy and geometrical properties”; [0041] “In step 105, each individual anatomical component is analyzed and assigned to a corresponding vertebral level by determining morphological and spatial relationships of the anatomical components”);
implementing a second level identification process to generate a second set of level identification outputs, the second level identification process including processing the image data of the set of anatomical components using a machine learning model to generate probability maps for each class of a plurality of classes associated with a set of level types or the set of levels (Siemionow, [0043] “ prediction by means of a neural network is performed in step 106, to assign an initial classification of vertebral level type. For example, the following level types can be assigned: C (Cervical), T (Thoracic), L (lumbar), S (Sacral). A Convolutional Neural Network (CNN) architecture 500, as shown in FIG. 5, can be used for this purpose. The network performs vertebral type prediction on each DICOM slice, as shown in FIG. 2C or 2D. The CNN 500 returns a probability map for each vertebral type class, for example, thoracic, sacrum or lumbar, in a way that the sum of individual probability components is always equal to one, and the biggest number explicitly suggests a predicted vertebral type”);
assigning a level identifier of a level from the set of levels to each anatomical component from the set of anatomical components based on the first set of level identification outputs and the second set of level identification outputs (Siemionow, [0050] “in step 108, final counting and identification of vertebral components take place. Based on the orientation of the patient anatomy in the volumetric scan data, neural network prediction, and overall distribution of levels' types, proper indices are assigned to form a full identification of levels with an ordinal identifier assigned. For example, counting of lumbar vertebrae may start from L5 (or L6) if sacrum is included in the scan set, or from L1 if thoracic spine is included in the scan set. Therefore, an ordinal identifier is assigned in step 108 to each group of anatomical components represented by the level (C, T, L, S) determined in step 107, based on the anatomical structure and distribution of all the other levels—for example, the following identifiers may be assigned: C1-S5 or C1-C7, T1-T12, L1-L5, S1-S5”); and
generating a visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level identifiers assigned to the set of anatomical components (Siemionow, [0051] Once the level information on the model is known, virtual 3D model output is provided in step 109, which can be used to conveniently display any configuration of identified levels (and other components of the patient anatomy, such as nerves or surgical instruments (such as implants 603, FIG. 6B)… the model is useful for providing an augmented reality image display in computer assisted surgery systems, wherein the virtual 3D model of the anatomy 600 is displayed over the real anatomy”).
Regarding Claim 2, Siemionow teaches the method of claim 1, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
the set of anatomical components includes a set of pedicle pairs and a set of vertebral bodies (Siemionow, [0011] “determining pairs of pedicles; determining a vertebral body closest to or intersecting with each pair of pedicles”; [036] “to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15”),
implementing the first level identification process further includes, for each pedicle pair from the set of pedicle pairs:
identifying, after determining the geometrical parameters of the set of anatomical components, a vertebral body from the set of vertebral bodies that intersects with or is nearest to the pedicle pair (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level”; Siemionow, [0040] “ geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”); and
subsequently identifying one or more additional anatomical components from the remaining anatomical components from the set of anatomical components that intersect with or are nearest to that pedicle pair or the vertebral body (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level; and repeating the previous steps for each level separately, excluding parts that were already assigned”), and
grouping the set of anatomical components into separate levels includes grouping, for each pedicle pair from the set of pedicle pairs, the pedicle pair and the respective vertebral body and additional anatomical components into a level from the set of levels (Siemionow, [0036] The aim of this procedure is to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15 transverse process 14, lamina 13, articular process 17, and spinous process 11”; Siemionow, [0042] “estimated which pedicles correspond to separate levels by their location in space and relation between all other anatomy components. Then, for each spinal level, the closest (or intersecting) vertebral body 16, determined in step 302 is assigned, that is touching at least one of the pedicles as shown in FIG. 4B”).
Regarding Claim 4, Siemionow teaches the method of claim 1, wherein:
implementing the first level identification process further includes:
iteratively performing until each anatomical component from the set of anatomical components has been grouped together with at least one other anatomical component from the set of anatomical components (Siemionow, [0042] “for each spinal level, the closest (or intersecting) vertebral body 16, determined in step 302 is assigned, that is touching at least one of the pedicles as shown in FIG. 4B. In step 303, further assignment of other anatomy components to levels, such as transverse process 14, articular process 17, spinous process 11 and/or lamina 13, can be conducted with the same approach based on anatomical knowledge and geometrical analysis, as shown in FIG. 4C. This process shall be repeated for each level separately in step 304, excluding anatomical components that were already picked by other levels”):
selecting, after determining the geometrical parameters of the set of anatomical components, an ungrouped anatomical component from the set of anatomical components (Siemionow, [0042] “assignment of other anatomy components to levels, such as transverse process 14, articular process 17, spinous process 11 and/or lamina 13, can be conducted with the same approach based on anatomical knowledge and geometrical analysis… This process shall be repeated for each level separately in step 304, excluding anatomical components that were already picked by other levels (ungrouped anatomical components)”); and
identifying additional ungrouped anatomical components from the set of anatomical components that intersect with or are nearest to the ungrouped anatomical component or subsequently identified ungrouped anatomical components (Siemionow, [0050] “final counting and identification of vertebral components take place. Based on the orientation of the patient anatomy in the volumetric scandata, neural network prediction, and overall distribution of levels' types, proper indices are assigned to form a full identification of levels with an ordinal identifier assigned. For example, counting of lumbar vertebrae may start from L5 (or L6) if sacrum is included in the scan set, or from L1 if thoracic spine is included in the scan set. Therefore, an ordinal identifier is assigned in step 108 to each group of anatomical components represented by the level (C, T, L, S) determined in step 107, based on the anatomical structure and distribution of all the other levels—for example, the following identifiers may be assigned: C1-S5 or C1-C7, T1-T12, L1-L5, S1-S5”), and
grouping the set of anatomical components into separate levels includes grouping, after each iteration of selecting the ungrouped anatomical component and identifying the additional ungrouped anatomical components, the ungrouped anatomical component and the additional ungrouped anatomical components into a level from the set of levels (Siemionow, Claim 1, “ grouping (105) the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing (106) the set of medical scan images using a convolutional neural network (500) to autonomously assign an initial level type; assigning (107) a level type to each group of anatomical components by combining the determined morphological and spatial relationships with the assigned initial level type; assigning (108) an ordinal identifier to each group of anatomical components to complement the assigned level type based on a relative distribution of the groups of anatomical components and their respective level types”).
Regarding Claim 5, Siemionow teaches the method of claim 4, wherein:
determining the geometrical parameters of the set of anatomical components includes determining a bounding volume that contains each anatomical component (Siemionow, [0009] “The determined morphological relationships of anatomical components may be their size and bounding box”; Siemionow, [0040] “geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”), and
identifying the additional ungrouped anatomical components that intersects with or is nearest to the unassigned anatomical component or subsequently identified ungrouped anatomical components includes identifying one or more anatomical components that have a bounding volume that intersects with a bounding volume of the unassigned anatomical component or a subsequently identified ungrouped anatomical component (Siemionow, Claim 1 “grouping (105) the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing (106) the set of medical scan images using a convolutional neural network (500) to autonomously assign an initial level type; assigning (107) a level type to each group of anatomical components by combining the determined morphological and spatial relationships with the assigned initial level type; assigning (108) an ordinal identifier to each group of anatomical components to complement the assigned level type based on a relative distribution of the groups of anatomical components and their respective level types”; claim 3, “wherein the determined (104) morphological relationships of anatomical components are their size and bounding box”).
Regarding Claim 12, Siemionow teaches the method of claim 1, wherein the set of level types includes two or more of: sacrum, thoracic, lumbar, or cervical (Siemionow, [0043] “prediction by means of a neural network is performed in step 106, to assign an initial classification of vertebral level type. For example, the following level types can be assigned: C (Cervical), T (Thoracic), L (lumbar), S (Sacral)”).
Regarding Claim 13, Siemionow teaches:
An apparatus, comprising:
a memory (Fig. 7, Siemionow, [0015] “at least one non-transitory processor-readable storage medium that stores at least one of processor-executable instructions or data”); and
a processor operatively coupled to the memory (Siemionow, [0015] “at least one processor communicably coupled to at least one non-transitory processor-readable storage medium”), the processor configured to:
receive image data of a set of anatomical components of an anatomical structure, the anatomical structure including a set of levels (Siemionow, [0037] “In the procedure of level identification, first, in step 101, a 3D scan volume is received, comprising a set of DICOM (Digital Imaging and Communications in Medicine) images of an anatomical bony structure, in this example—a spine, which is to be segmented, such as a set of CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scans. The set represents consecutive slices of the anatomical structure (such as one slice shown in FIG. 2A)”);
receive segmentation data identifying the set of anatomical components in the image data (Siemionow, [0038] “the received images are processed in step 102 to perform autonomous segmentation of the anatomical structure to determine plurality of anatomical components, such as vertebral body 16, pedicles 15, transverse processes 14, lamina 13, articular process 17, spinous process 11 and ribs 18, as shown in FIG. 2B”);
implement a first level identification process to generate a first set of level identification outputs, the first level identification process including determine geometrical parameters of the set of anatomical components based on the segmentation data (Siemionow, [0040] Next, in step 104, geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”); and grouping the set of anatomical components into separate levels based on geometrical parameters of the set of anatomical components (Siemionow, Fig. 1, step 105, “group anatomy parts to form separate levels based on the anatomy and geometrical properties”; [0041] “In step 105, each individual anatomical component is analyzed and assigned to a corresponding vertebral level by determining morphological and spatial relationships of the anatomical components”);
implementing a second level identification process to generate a second set of level identification outputs, the second level identification process including processing the image data of the set of anatomical components using a machine learning model to generate probability maps for each class of a plurality of classes associated with a set of level types or the set of levels (Siemionow, [0043] “ prediction by means of a neural network is performed in step 106, to assign an initial classification of vertebral level type. For example, the following level types can be assigned: C (Cervical), T (Thoracic), L (lumbar), S (Sacral). A Convolutional Neural Network (CNN) architecture 500, as shown in FIG. 5, can be used for this purpose. The network performs vertebral type prediction on each DICOM slice, as shown in FIG. 2C or 2D. The CNN 500 returns a probability map for each vertebral type class, for example, thoracic, sacrum or lumbar, in a way that the sum of individual probability components is always equal to one, and the biggest number explicitly suggests a predicted vertebral type”);
assign a level identifier of a level from the set of levels to each anatomical component from the set of anatomical components based on the first set of level identification outputs and the second set of level identification outputs (Siemionow, [0050] “in step 108, final counting and identification of vertebral components take place. Based on the orientation of the patient anatomy in the volumetric scan data, neural network prediction, and overall distribution of levels' types, proper indices are assigned to form a full identification of levels with an ordinal identifier assigned. For example, counting of lumbar vertebrae may start from L5 (or L6) if sacrum is included in the scan set, or from L1 if thoracic spine is included in the scan set. Therefore, an ordinal identifier is assigned in step 108 to each group of anatomical components represented by the level (C, T, L, S) determined in step 107, based on the anatomical structure and distribution of all the other levels—for example, the following identifiers may be assigned: C1-S5 or C1-C7, T1-T12, L1-L5, S1-S5”); and
generate a visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level identifiers assigned to the set of anatomical components (Siemionow, [0051] Once the level information on the model is known, virtual 3D model output is provided in step 109, which can be used to conveniently display any configuration of identified levels (and other components of the patient anatomy, such as nerves or surgical instruments (such as implants 603, FIG. 6B)… the model is useful for providing an augmented reality image display in computer assisted surgery systems, wherein the virtual 3D model of the anatomy 600 is displayed over the real anatomy”).
Regarding Claim 14, Siemionow teaches the apparatus of claim 13, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
the set of anatomical components includes a set of pedicle pairs and a set of vertebral bodies (Siemionow, [0011] “determining pairs of pedicles; determining a vertebral body closest to or intersecting with each pair of pedicles”; [036] “to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15”),
in order to implement the first level identification process, the processor is configured to, for each pedicle pair from the set of pedicle pairs:
identify, after determining the geometrical parameters of the set of anatomical components, a vertebral body from the set of vertebral bodies that intersects with or is nearest to the pedicle pair (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level”; Siemionow, [0040] “ geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”); and
subsequently identify one or more additional anatomical components from the remaining anatomical components from the set of anatomical components that intersect with or are nearest to that pedicle pair or the vertebral body (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level; and repeating the previous steps for each level separately, excluding parts that were already assigned”), and
in order to group the set of anatomical components into separate levels includes grouping, , the processor is configured to, for each pedicle pair from the set of pedicle pairs, group the pedicle pair and the respective vertebral body and additional anatomical components into a level from the set of levels (Siemionow, [0036] The aim of this procedure is to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15 transverse process 14, lamina 13, articular process 17, and spinous process 11”; Siemionow, [0042] “estimated which pedicles correspond to separate levels by their location in space and relation between all other anatomy components. Then, for each spinal level, the closest (or intersecting) vertebral body 16, determined in step 302 is assigned, that is touching at least one of the pedicles as shown in FIG. 4B”).
Regarding Claim 16, Siemionow teaches the apparatus of claim 13, wherein:
in order to implement the first level identification process, the processor is configured to:
iteratively performing until each anatomical component from the set of anatomical components has been grouped together with at least one other anatomical component from the set of anatomical components (Siemionow, [0042] “for each spinal level, the closest (or intersecting) vertebral body 16, determined in step 302 is assigned, that is touching at least one of the pedicles as shown in FIG. 4B. In step 303, further assignment of other anatomy components to levels, such as transverse process 14, articular process 17, spinous process 11 and/or lamina 13, can be conducted with the same approach based on anatomical knowledge and geometrical analysis, as shown in FIG. 4C. This process shall be repeated for each level separately in step 304, excluding anatomical components that were already picked by other levels”):
selecting, after determining the geometrical parameters of the set of anatomical components, an ungrouped anatomical component from the set of anatomical components (Siemionow, [0042] “assignment of other anatomy components to levels, such as transverse process 14, articular process 17, spinous process 11 and/or lamina 13, can be conducted with the same approach based on anatomical knowledge and geometrical analysis… This process shall be repeated for each level separately in step 304, excluding anatomical components that were already picked by other levels (ungrouped anatomical components)”); and
identifying additional ungrouped anatomical components from the set of anatomical components that intersect with or are nearest to the ungrouped anatomical component or subsequently identified ungrouped anatomical components (Siemionow, [0050] “final counting and identification of vertebral components take place. Based on the orientation of the patient anatomy in the volumetric scan data, neural network prediction, and overall distribution of levels' types, proper indices are assigned to form a full identification of levels with an ordinal identifier assigned. For example, counting of lumbar vertebrae may start from L5 (or L6) if sacrum is included in the scan set, or from L1 if thoracic spine is included in the scan set. Therefore, an ordinal identifier is assigned in step 108 to each group of anatomical components represented by the level (C, T, L, S) determined in step 107, based on the anatomical structure and distribution of all the other levels—for example, the following identifiers may be assigned: C1-S5 or C1-C7, T1-T12, L1-L5, S1-S5”), and
in order to group the set of anatomical components into separate levels, the processor is configured to, after each iteration of selecting the ungrouped anatomical component and identifying the additional ungrouped anatomical components, the ungrouped anatomical component and the additional ungrouped anatomical components into a level from the set of levels (Siemionow, Claim 1, “ grouping (105) the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing (106) the set of medical scan images using a convolutional neural network (500) to autonomously assign an initial level type; assigning (107) a level type to each group of anatomical components by combining the determined morphological and spatial relationships with the assigned initial level type; assigning (108) an ordinal identifier to each group of anatomical components to complement the assigned level type based on a relative distribution of the groups of anatomical components and their respective level types”).
Regarding Claim 17, Siemionow teaches the apparatus of claim 16, wherein:
in order to determine the geometrical parameters of the set of anatomical components, the processor is configured to determine a bounding volume that contains each anatomical component (Siemionow, [0009] “The determined morphological relationships of anatomical components may be their size and bounding box”; Siemionow, [0040] “geometrical parameters for each anatomical component are determined, including, at least, their relative positions, size, bounding box edges, and preferably also orientation based on semantic segmentation results and the moment of inertia”), and
in order to identify the additional ungrouped anatomical components that intersects with or is nearest to the unassigned anatomical component or subsequently identified ungrouped anatomical components, the processor is configured to by identifying one or more anatomical components that have a bounding volume that intersects with a bounding volume of the unassigned anatomical component or a subsequently identified ungrouped anatomical component (Siemionow, Claim 1 “grouping (105) the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing (106) the set of medical scan images using a convolutional neural network (500) to autonomously assign an initial level type; assigning (107) a level type to each group of anatomical components by combining the determined morphological and spatial relationships with the assigned initial level type; assigning (108) an ordinal identifier to each group of anatomical components to complement the assigned level type based on a relative distribution of the groups of anatomical components and their respective level types”; claim 3, “wherein the determined (104) morphological relationships of anatomical components are their size and bounding box”).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Siemionow et al. (US 20200327721 A1; hereafter referred to as Siemionow) in view of Jiang et al. (US 20220083821 A1; hereafter referred to as Jiang).
Regarding Claim 3, Siemionow teaches the method of claim 1, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
subsequently identifying one or more additional anatomical components from the remaining anatomical components from the set of anatomical components that intersect with or are nearest to the vertebral body (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level; and repeating the previous steps for each level separately, excluding parts that were already assigned”), and
grouping the set of anatomical components into separate levels includes grouping, for each level from the set of levels, the vertebral body and the respective additional anatomical components into the level (Siemionow, [0036] The aim of this procedure is to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15 transverse process 14, lamina 13, articular process 17, and spinous process 11”).
However, Siemionow fails to explicitly teach:
the set of anatomical components includes a set of intervertebral discs and a set of vertebral bodies,
implementing the first level identification process further includes, for each level from the set of levels:
identifying, after determining the geometrical parameters of the set of anatomical components, first and second intervertebral discs that are closest to the level;
identifying a vertebral body from the set of vertebral bodies that is disposed between the first and second intervertebral discs;
In the same field of endeavor, Jiang teaches:
identifying, after determining the geometrical parameters of the set of anatomical components, first and second intervertebral discs that are closest to the level (Jiang, [0037] the vertebral bodies and intervertebral discs included in the source spinal image are determined based at least in part on one or more characteristics associated with a vertebral body and/or intervertebral disc, such as in a mapping of characteristics to a vertebral body and/or intervertebral disc. The one or more characteristics may include one or more of shape, color, size, relative location, relative characteristics (geometric parameters) of the vertebral body and/or intervertebral disc (e.g., a relative characteristic of each vertebral body and/or intervertebral disc), etc.”);
identifying a vertebral body from the set of vertebral bodies that is disposed between the first and second intervertebral discs (Jiang, [0030] “determining vertebral body recognition results corresponding to the vertebral bodies and intervertebral disc recognition results corresponding to the intervertebral discs”);
Siemionow and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Jiang to make the invention that identifies the intervertebral discs that are closest to the level and identify a vertebral body from the set of vertebral bodies that is disposed between the intervertebral discs; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 15, Siemionow teaches the apparatus of claim 13, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
subsequently identify one or more additional anatomical components from the remaining anatomical components from the set of anatomical components that intersect with or are nearest to the vertebral body (Siemionow, [0011] “determining a vertebral body closest to or intersecting with each pair of pedicles; searching for anatomy parts intersecting with other components that were already assigned to the level; and repeating the previous steps for each level separately, excluding parts that were already assigned”), and
in order to group the set of anatomical components into separate levels includes grouping, the processor is configured to, for each level from the set of levels, group the vertebral body and the respective additional anatomical components into the level (Siemionow, [0036] The aim of this procedure is to identify levels of a bony structure, i.e. adjacent groups of similar components of the anatomical structure, for example the vertebrae. Each vertebra can be considered as a level that comprises a plurality of anatomical parts, such as vertebral body 16, pedicles 15 transverse process 14, lamina 13, articular process 17, and spinous process 11”).
However, Siemionow fails to explicitly teach:
the set of anatomical components includes a set of intervertebral discs and a set of vertebral bodies,
in order to implement the first level identification process, the processor is configured to, for each level from the set of levels:
identify, after determining the geometrical parameters of the set of anatomical components, first and second intervertebral discs that are closest to the level;
identify a vertebral body from the set of vertebral bodies that is disposed between the first and second intervertebral discs;
In the same field of endeavor, Jiang teaches:
identify, after determining the geometrical parameters of the set of anatomical components, first and second intervertebral discs that are closest to the level (Jiang, [0037] the vertebral bodies and intervertebral discs included in the source spinal image are determined based at least in part on one or more characteristics associated with a vertebral body and/or intervertebral disc, such as in a mapping of characteristics to a vertebral body and/or intervertebral disc. The one or more characteristics may include one or more of shape, color, size, relative location, relative characteristics (geometric parameters) of the vertebral body and/or intervertebral disc (e.g., a relative characteristic of each vertebral body and/or intervertebral disc), etc.”);
identify a vertebral body from the set of vertebral bodies that is disposed between the first and second intervertebral discs (Jiang, [0030] “determining vertebral body recognition results corresponding to the vertebral bodies and intervertebral disc recognition results corresponding to the intervertebral discs”);
Siemionow and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Jiang to make the invention that identifies the intervertebral discs that are closest to the level and identify a vertebral body from the set of vertebral bodies that is disposed between the intervertebral discs; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Claims 6 – 11 and 18 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Siemionow et al. (US 20200327721 A1; hereafter referred to as Siemionow) in view of Shah (US 20210287454 A1; hereafter referred to as Shah) further in view of Jiang et al. (US 20220083821 A1; hereafter referred to as Jiang).
Regarding Claim 6, Siemionow teaches the method of claim 1, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
While Siemionow teaches obtaining 3D -volume scans (Fig. 2A, Siemionow, [0037]), it fails to explicitly teach:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine,
the processing the image data using the machine learning model includes processing, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra, and
implementing the second level identification process further includes, for each vertebra from the set of vertebrae:
assigning, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image; and
determining, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra.
In the same field of endeavor, Shah teaches:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine (Shah, [0008] “generate a patient specific three-dimensional model of an anatomical area from two-dimensional data images of the anatomical area”; Shah, [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”),
Siemionow and Shah are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Shah to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; doing so can yield predictable results in identifying anatomical structures from the MRI scans of a patient (Shah [0006]); thus one of the ordinary skill in the art would have been motivated to combine the references.
While Siemionow in view of Shah teaches identifying any anatomical object that is defined statically or dynamically such as the disc between vertebra using machine learning (Shah, [0045]- [0047]), it fails to explicitly teach:
the processing the image data using the machine learning model includes processing, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra, and
implementing the second level identification process further includes, for each vertebra from the set of vertebrae:
assigning, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image; and
determining, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra.
In the same field of endeavor, Jiang teaches:
processing the image data using the machine learning model includes processing, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra (Jiang, [0039] “ pre-train a machine learning model for recognizing vertebral bodies and intervertebral discs included in a spinal image and subject the source spinal image to analytical processing by the machine learning model in order to recognize vertebral bodies and intervertebral discs included in the source spinal image”; Jiang, [0043] After a vertebral body is obtained (e.g., determined), analytical processing may be performed with respect to the vertebral body in connection with determining the corresponding vertebral body recognition result”; Jiang, [0049] “The probability map may include vertebral body recognition probabilities corresponding to the vertebral body recognition results and intervertebral disc recognition probabilities corresponding to the intervertebral disc recognition results”), and
implementing the second level identification process further includes, for each vertebra from the set of vertebrae:
assigning, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image (Jiang, [0097] “Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4; the preset intervertebral disc recognition types may include: type b1, type b2, type b3, and type b4. Then the second convolutional neural network may be used to perform analytical processing with respect to (e.g., on) the image features. According to various embodiments, third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image may be obtained”); and
determining, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra (Jiang, [0097] Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4”; Jiang, [0102] “After the third probabilities of vertebral body positions corresponding to a set of preset vertebral body recognition types are obtained (e.g., after probabilities of vertebral body positions corresponding to all of the set of the preset vertebral body recognition types are obtained), analytical processing may be performed with respect to the third probabilities of the vertebral body positions corresponding to the set of preset vertebral body recognition types (e.g., all of the set) to determine vertebral body recognition results corresponding to the vertebral bodies”).
Siemionow, Shah and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow in view of Shah with the invention of Jiang to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; processes the image data using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra; assigning at least one level type from the set of level types based on the probability map and determining the level types; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 7, Siemionow in view of Shah further in view of Jiang teaches the method of claim 6, wherein:
the 2D images include 2D axial scans of the set of vertebrae (Shah, [0081] “, two-dimensional MRI data may be loaded into the system.”; Shah, [0091] “In the above example of a spine, the three-dimensional anatomical model of the lumbar spine may be fitted to each of the patient's MRIs by a team of technicians. MRI T1, T2, sagittal, axial, and transverse reconstruction image series may be used to train segmentation models by the intersection of the 3D model through these image sequences”), and
determining the level type or the level identifier for each vertebra from the set of vertebrae includes determining which level type or level identifier has been assigned to a greatest number of the 2D images that contain at least a portion of the vertebra (Jiang, [0156] “After acquiring vertebral body regions, vertebral body pathologic types, intervertebral disc regions, and intervertebral disc pathologic types included in the training image, the vertebral body regions, vertebral body pathologic types, intervertebral disc regions, and intervertebral disc pathologic types may be input into a classifier for learning training and for performing computations using a cross entropy loss function so as to obtain loss values for model learning training”; Jiang, [0169] “distributed analysis subcomponents with smaller information loads may be allocated a greater number source spinal images for analytical processing and distributed analysis subcomponents with greater information loads may be allocated a smaller number of source spinal images for analytical processing”).
Regarding Claim 8, Siemionow teaches the method of claim 1, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
While Siemionow teaches obtaining 3D -volume scans (Fig. 2A, Siemionow, [0037]), it fails to explicitly teach:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine,
processing the image data using the machine learning model includes processing the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image, and
implementing the second level identification process further includes determining, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image.
In the same field of endeavor, Shah teaches:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine (Shah, [0008] “generate a patient specific three-dimensional model of an anatomical area from two-dimensional data images of the anatomical area”; Shah, [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”),
Siemionow and Shah are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Shah to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; doing so can yield predictable results in identifying anatomical structures from the MRI scans of a patient (Shah [0006]); thus one of the ordinary skill in the art would have been motivated to combine the references.
While Siemionow in view of Shah teaches identifying any anatomical object that is defined statically or dynamically such as the disc between vertebra using machine learning (Shah, [0045]- [0047]), it fails to explicitly teach:
processing the image data using the machine learning model includes processing the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image, and
implementing the second level identification process further includes determining, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image.
In the same field of endeavor, Jiang teaches:
processing the image data using the machine learning model includes processing the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image (Jiang, Fig. 2, 2D images, [0039] “ pre-train a machine learning model for recognizing vertebral bodies and intervertebral discs included in a spinal image and subject the source spinal image to analytical processing by the machine learning model in order to recognize vertebral bodies and intervertebral discs included in the source spinal image”; Jiang, [0043] After a vertebral body is obtained (e.g., determined), analytical processing may be performed with respect to the vertebral body in connection with determining the corresponding vertebral body recognition result”; Jiang, [0049] “The probability map may include vertebral body recognition probabilities corresponding to the vertebral body recognition results and intervertebral disc recognition probabilities corresponding to the intervertebral disc recognition results”), and
implementing the second level identification process further includes determining, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image (Jiang, [0097] Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4; the preset intervertebral disc recognition types may include: type b1, type b2, type b3, and type b4. Then the second convolutional neural network may be used to perform analytical processing with respect to (e.g., on) the image features. According to various embodiments, third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image may be obtained”).
Siemionow, Shah and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow in view of Shah with the invention of Jiang to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; processes the image data using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra; determining level identifiers from the set of levels based on the probability map; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 9, Siemionow in view of Shah further in view of Jiang teaches the method of claim 8, wherein the at least one 2D image includes a 2D sagittal scan or a 2D coronal scan of the set of vertebrae (Shah, [0097] “Segmented outputs similar to those in FIG. 13A-13B may be generated for each sagittal and axial slice in the MRI images”; [0081] “two-dimensional MRI data may be loaded into the system. The two-dimension data loaded may be multiple two-dimension MRI images”; [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”; Shah, [0055] “a spine may be seen in FIGS. 4A-4C, where the individual vertebrae model may be made up of several different components”).
Regarding Claim 10, Siemionow in view of Shah further in view of Jiang teaches the method of 6, wherein the machine learning model is a convolutional neural network trained to process 2D images of the set of vertebrae (Shah, “[0081] “two-dimensional MRI data may be loaded into the system. The two-dimension data loaded may be multiple two-dimension MRI images”; Shah, [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”; Shah, [0055] “a spine may be seen in FIGS. 4A-4C, where the individual vertebrae model may be made up of several different components”; [0101] Using a series of convolutional neural networks trained with gradient descent algorithms with dice loss coefficients and spatial dropout may prevent over-training to the dataset and enforces the network models to identify defining features that result in diagnosis and grading”).
Regarding Claim 11, Siemionow in view of Shah further in view of Jiang teaches the method of 6, wherein the set of 2D images includes at least one of computed tomography (CT) images or magnetic resonance imaging (MRI) images (Shah, Fig. 12, two-dimensional MRI scans images”; [0033] “The system 10 may read medical image data such as MRIs, CT and the like”).
Regarding Claim 18, Siemionow teaches the apparatus of claim 13, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
While Siemionow teaches obtaining 3D -volume scans (Fig. 2A, Siemionow, [0037]), it fails to explicitly teach:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine,
in order to process the image data using the machine learning model, the processor is configured to process, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra, and
in order to implement the second level identification process, the processor is configured to, for each vertebra from the set of vertebrae:
assign, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image; and
determine, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra.
In the same field of endeavor, Shah teaches:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine (Shah, [0008] “generate a patient specific three-dimensional model of an anatomical area from two-dimensional data images of the anatomical area”; Shah, [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”),
Siemionow and Shah are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Shah to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; doing so can yield predictable results in identifying anatomical structures from the MRI scans of a patient (Shah [0006]); thus one of the ordinary skill in the art would have been motivated to combine the references.
While Siemionow in view of Shah teaches identifying any anatomical object that is defined statically or dynamically such as the disc between vertebra using machine learning (Shah, [0045]- [0047]), it fails to explicitly teach:
in order to process the image data using the machine learning model, the processor is configured to process, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra, and
in order to implement the second level identification process, the processor is configured to, for each vertebra from the set of vertebrae:
assign, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image; and
determine, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra.
In the same field of endeavor, Jiang teaches:
in order to process the image data using the machine learning model, the processor is configured to process, for each vertebra from the set of vertebrae, 2D images from the set of 2D images that contain at least a portion of the vertebra using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra (Jiang, [0039] “ pre-train a machine learning model for recognizing vertebral bodies and intervertebral discs included in a spinal image and subject the source spinal image to analytical processing by the machine learning model in order to recognize vertebral bodies and intervertebral discs included in the source spinal image”; Jiang, [0043] After a vertebral body is obtained (e.g., determined), analytical processing may be performed with respect to the vertebral body in connection with determining the corresponding vertebral body recognition result”; Jiang, [0049] “The probability map may include vertebral body recognition probabilities corresponding to the vertebral body recognition results and intervertebral disc recognition probabilities corresponding to the intervertebral disc recognition results”), and
in order to implement the second level identification process, the processor is configured to, for each vertebra from the set of vertebrae:
assign, for each of the 2D images containing at least a portion of the vertebra, at least one of a level type from the set of level types or a level identifier of a level from the set of levels based on the probability maps for the 2D image (Jiang, [0097] “Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4; the preset intervertebral disc recognition types may include: type b1, type b2, type b3, and type b4. Then the second convolutional neural network may be used to perform analytical processing with respect to (e.g., on) the image features. According to various embodiments, third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image may be obtained”); and
determine, based on the level type or the level identifier assigned, a level type from the set of level types or a level identifier of a level from the set of levels for the vertebra (Jiang, [0097] Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4”; Jiang, [0102] “After the third probabilities of vertebral body positions corresponding to a set of preset vertebral body recognition types are obtained (e.g., after probabilities of vertebral body positions corresponding to all of the set of the preset vertebral body recognition types are obtained), analytical processing may be performed with respect to the third probabilities of the vertebral body positions corresponding to the set of preset vertebral body recognition types (e.g., all of the set) to determine vertebral body recognition results corresponding to the vertebral bodies”).
Siemionow, Shah and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow in view of Shah with the invention of Jiang to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; processes the image data using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra; assigning at least one level type from the set of level types based on the probability map and determining the level types; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 19, Siemionow teaches the apparatus of claim 13, wherein:
the anatomical structure is a spine (Siemionow, [0011] “The anatomical structure may be a spine”),
While Siemionow teaches obtaining 3D -volume scans (Fig. 2A, Siemionow, [0037]), it fails to explicitly teach:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine,
in order to process the image data using the machine learning model, the processor is configured to process the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image, and
in order to implement the second level identification process, the processor is configured to determine, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image.
In the same field of endeavor, Shah teaches:
the image data includes two-dimensional (2D) images of a three-dimensional volume containing a set of vertebrae of the spine (Shah, [0008] “generate a patient specific three-dimensional model of an anatomical area from two-dimensional data images of the anatomical area”; Shah, [0045] “method applies to any anatomical object that is defined statically or dynamically such as the disc between vertebra”),
Siemionow and Shah are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow with the invention of Shah to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; doing so can yield predictable results in identifying anatomical structures from the MRI scans of a patient (Shah [0006]); thus one of the ordinary skill in the art would have been motivated to combine the references.
While Siemionow in view of Shah teaches identifying any anatomical object that is defined statically or dynamically such as the disc between vertebra using machine learning (Shah, [0045]- [0047]), it fails to explicitly teach:
in order to process the image data using the machine learning model, the processor is configured to process the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image, and
in order to implement the second level identification process, the processor is configured to determine, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image.
In the same field of endeavor, Jiang teches:
in order to process the image data using the machine learning model, the processor is configured to process the at least one 2D image using the machine learning model to generate the probability maps for each of the at least one 2D image (Jiang, Fig. 2, 2D images, [0039] “ pre-train a machine learning model for recognizing vertebral bodies and intervertebral discs included in a spinal image and subject the source spinal image to analytical processing by the machine learning model in order to recognize vertebral bodies and intervertebral discs included in the source spinal image”; Jiang, [0043] After a vertebral body is obtained (e.g., determined), analytical processing may be performed with respect to the vertebral body in connection with determining the corresponding vertebral body recognition result”; Jiang, [0049] “The probability map may include vertebral body recognition probabilities corresponding to the vertebral body recognition results and intervertebral disc recognition probabilities corresponding to the intervertebral disc recognition results”), and
in order to implement the second level identification process, the processor is configured to determine, for each vertebra from the set of vertebrae, a level identifier of a level from the set of levels based on the probability maps for the at least one 2D image (Jiang, [0097] Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4; the preset intervertebral disc recognition types may include: type b1, type b2, type b3, and type b4. Then the second convolutional neural network may be used to perform analytical processing with respect to (e.g., on) the image features. According to various embodiments, third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image may be obtained”).
Siemionow, Shah and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Siemionow in view of Shah with the invention of Jiang to make the invention that receives two-dimensional images of a three-dimensional volume containing a set of vertebrae of the spine; processes the image data using the machine learning model to generate the probability maps for each of the 2D images that contain at least a portion of the vertebra; determining level identifiers from the set of levels based on the probability map; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Shah (US 20210287454 A1; hereafter referred to as Shah) in view of Jiang et al. (US 20220083821 A1; hereafter referred to as Jiang).
Regarding Claim 20, Shah teaches:
receiving a set of two-dimensional (2D) images of a three-dimensional volume containing a set of anatomical components of an anatomical structure, the anatomical structure including a set of levels, the set of 2D images including subsets of 2D images each associated with a different anatomical component from the set of anatomical components (Shah, [0081] “two-dimensional MRI data may be loaded into the system. The two-dimension data loaded may be multiple two-dimension MRI images. The MRI images can be acquired along multiple planes to create a stack of two-dimension MRI images which may resemble a pseudo 3D volume”; Shah, [0044] “Anatomical structure reduction. The anatomical structures/objects are broken down into properties and shape components (See FIG. 4B)”; Shah, [0055] “a spine may be seen in FIGS. 4A-4C, where the individual vertebrae model may be made up of several different components... shown in FIG. 4B will have properties that can be defined for each such as height radius, thickness, length, position, scale or a custom defined property such as additional injuries. The body 1 of the vertebra is the primary area of weight bearing and provides a resting place for the fibrous discs which separate each of the vertebrae. Additionally, the same applies for pedicle 2, transverse process 3, lamina 4, spinous process 5, and any additional shape components and/or properties. The shape mesh and shape definition can represent any anatomical object with assigned properties”; [0091] “the pathologies at each disc level and, when available, the grading of severity may be extracted”);
for each anatomical component from the set of anatomical components:
processing, using a convolutional neural network (CNN) trained to identify the set of levels or level types of the set of levels (Shah, [0092] “automated segmentation algorithms may be used to identify the location of each vertebra and disc in the patient's lumbar spine in order. Segmented regions may be used to fit a spine curve and localize the centers of each disc, and a series of sagittal and axial slices from the region were used for training and prediction”; Shah, [0099] “the first network may be used to detect and grade central canal stenosis, and the second may be used to detect foraminal stenosis on the left and right neural foramen”); and
However, Shah fails to explicitly teach:
processing, using a convolutional neural network (CNN) trained to identify the set of levels or level types of the set of levels, each 2D image from the subset of 2D images associated with the anatomical component to output a predicted level or level type for the anatomical component based on the 2D image;
assigning a level or a level type to the anatomical component based on the predicted level or level type for the anatomical component output by processing the subset of 2D images associated with the anatomical component; and
generating a visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level or level type assigned to each anatomical component of the set of anatomical components.
In the same field of endeavor, Jiang teaches:
processing, using a convolutional neural network (CNN) trained to identify the set of levels or level types of the set of levels, each 2D image from the subset of 2D images associated with the anatomical component to output a predicted level or level type for the anatomical component based on the 2D image (Jiang, [0039] “ pre-train a machine learning model for recognizing vertebral bodies and intervertebral discs included in a spinal image and subject the source spinal image to analytical processing by the machine learning model in order to recognize vertebral bodies and intervertebral discs included in the source spinal image”; Jiang, [0043] After a vertebral body is obtained (e.g., determined), analytical processing may be performed with respect to the vertebral body in connection with determining the corresponding vertebral body recognition result”; Shah, [0094] “a second convolutional neural network is used in connection with performing an analytical processing with respect to image features to obtain third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image”).
assigning a level or a level type to the anatomical component based on the predicted level or level type for the anatomical component output by processing the subset of 2D images associated with the anatomical component (Jiang, [0097] Example 1: When determining said vertebral body position to be a vertebral body included in the source spinal image, the preset vertebral body recognition types may include: type a1, type a2, type a3, and type a4; the preset intervertebral disc recognition types may include: type b1, type b2, type b3, and type b4. Then the second convolutional neural network may be used to perform analytical processing with respect to (e.g., on) the image features. According to various embodiments, third probabilities of vertebral body positions corresponding to different preset vertebral body recognition types and fourth probabilities of intervertebral disc positions corresponding to different preset intervertebral disc recognition types in the source spinal image may be obtained”); and
generating a visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level or level type assigned to each anatomical component of the set of anatomical components (Jiang, Fig. 9, [0111] “After target recognition results corresponding to the source spinal image are obtained, the source spinal image and the corresponding target recognition results may be displayed by a display device”).
Shah and Jiang are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Shah with the invention of Jiang to make the invention that processes using a convolutional neural network (CNN) trained to identify the set of levels or level types of the set of levels; assigns a level or a level type to the anatomical component based; and generates visual representation of the anatomical structure including a visual depiction of the anatomical structure and visual elements indicative of the level; doing so can efficiently and accurately identify target lumbar pathologic changes by recognizing vertebral body positions corresponding to the vertebral bodies included in the spinal image and intervertebral disc positions corresponding to the intervertebral discs (Jiang [0003], [0057]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190035085 A1: SYSTEMS AND METHODS FOR AUTOMATIC VERTEBRAE SEGMENTATION AND IDENTIFICATION IN MEDICAL IMAGES: the method includes: acquiring a plurality of image slices related to a vertebral column, the vertebral column including a plurality of vertebrae; obtaining a classifier for vertebrae identification; identifying, by a processor, one or more vertebral foramina in the plurality of image slices using the classifier; and determining, by the processor, the plurality of vertebrae based on the one or more vertebral foramina.
US 20220327703 A1: SYSTEM AND METHOD FOR MEDICAL IMAGING OF INTERVERTEBRAL DISCS: The method may include obtaining scanning data of a spine of a subject, determining one or more centrum parameters of each of a plurality of centrums of the spine based on the scanning data, and identifying at least one intervertebral disc based on the one or more centrum parameters.
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VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/VAISALI RAO KOPPOLU/Examiner of Art Unit 2664
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