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 .
The Final Office Action presented on 08/27/2025 is withdrawn and this Non-Final Office Action is current. Examiner is considering the claim set dated 07/11/ 2025 for this additional Non-Final rejection.
Status of Claims
Claims 1-20 are pending. Claims 1, 10, 18 and 20 are amended.
Response to Arguments
In response to Applicant’s arguments presented in page 2, second paragraph of the remarks filed on 02/27/2026, with respect to the claimed automatic landmark selection, examiner has withdrawn the previous rejection and is presenting new analysis which makes Applicant’s arguments moot.
Applicant further argues in page 4, third paragraph that the prior art does not disclose that the models are available for other times, such as for planning a procedure or diagnosing a medical condition. Examiner respectfully disagrees. The cited prior art of record Geri teaches in ¶0006: “wherein said modeling system is configured for building a case to support said surgical procedure in advance of said procedure by creating models for the particular patient using the patient medical images. Therefore, Applicant’s argument is not found persuasive.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 5, 7, 9-11, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tolkowsky et al. (US 2019/0350657 A1), in view of Feiner et al. (US 2020/0188028 A1), in further view of Geri et al. (US 2017/0035517 A1) further in view of Mattiuzzi et al. (US 20100135544 A1).
Regarding claim 10, Tolkowsky teaches, A method for aligning images (Tolkowsky, ¶0359: “3D image data and 2D x-ray images are… aligned”) for providing a (Tolkowsky, ¶0342: “the spinal roadmap is generated upon an image that is a fused combination”) comprising the steps of: an alignment computer receiving an x-ray image (Tolkowsky, ¶0050: “computer processor configured: [0051] to receive the first and second 2D x-ray images”) of the spine of the particular patient; (Tolkowsky, ¶0179: “2D x-ray images of… subject's spine”) the alignment computer receiving (Tolkowsky, ¶0123: “computer processor configured to: [0124] receive the 3D image data of the skeletal portion”) CT scan images of the spine of the particular patient; (Tolkowsky, ¶0257: “3D CT image of a subject's spine”) the alignment computer converting a plurality of vertebrae of the CT scan images into segmented (Tolkowsky, ¶0360: “segmentation of a vertebra in the 3D image data”) one or more landmarks on the X-ray image; (Tolkowsky, ¶0301: “markers 52… further facilitates registration”) the alignment computer automatically aligning the (Tolkowsky, ¶0179: “automatically determine a location of the given vertebra… register the given vertebra within the first and second 2D x-ray images to the given vertebra within the 3D image data”) using the one or more landmarks; (Tolkowsky, ¶0301: “markers 52… further facilitates registration”) to align the x-ray image with the (Tolkowsky, ¶0359: “by registering the x-ray images to the 3D image data using the above-described technique, the 3D image data and 2D x-ray images are brought into a common reference frame to which they are all aligned”). However, Tolkowsky does not explicitly teach, segmented polygons, the alignment computer automatically selecting, using a deep neural network based on learning from historical data and the alignment computer generating the patient specific 3D model utilizing the CT scan images and the X-ray image according to the aligning; storing said patient specific 3D model for providing future access to said patient specific 3D model; and outputting the patient specific 3D model to a user interface for display to a user.
In an analogous field of endeavor, Feiner teaches, the alignment computer (Feiner, ¶0009: “computer program code can also be configured, to cause the apparatus at least to align”) generating the patient specific 3D model (Feiner, ¶0078: “patient-specific virtual anatomic 3D models created”) utilizing the CT scan images (Feiner, ¶0029: “the 3D model 310 can be… a CT scan performed on the patient”) and the X-ray image (Feiner, ¶0030: “data 320 can be… X-ray”) according to the aligning; (Feiner, ¶00030: “The aligning of the 3D model 310… fusing the 3D model 310 with data 320”) storing said patient specific 3D model (Feiner, ¶0063: “AR guidance can record data… saving all or part of any data from the medical procedure such as the 3D model, fluoroscopy, tracking data, audio, or video”) for providing future access to said patient specific 3D model; (Feiner, ¶0054: “AR guidance can be stored in a memory, and retrieved at the clinician's request”) and outputting the patient specific 3D model (Feiner, ¶0078: “patient-specific virtual anatomic 3D models created”) to a user interface (Feiner, ¶0078: “AR user interface (UI)”) for display to a user, (Feiner, ¶0028: “AR guidance 100 can be presented to the clinician, at least in part using a 3D display”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky using the teachings of Feiner to introduce a patient specific 3D model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of one model for representing both X-ray and CT scans for better diagnosis of the patient. Therefore, it would have been obvious to combine the analogous arts Tolkowsky and Feiner to obtain the above-described limitations of claim 10. However, the combination of Tolkowsky and Feiner does not explicitly teach, segmented polygons and the alignment computer automatically selecting, using a deep neural network based on learning from historical data.
In another analogous field of endeavor, Geri teaches, segmented polygons (Geri, ¶0092: “image can be segmented to show only the vessels while the other image can be segmented to show only the soft tissue”; also see Fig. 7, polygonal segments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner using the teachings of Geri to introduce image segmentation into polygonal shapes. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of better inspection and alignment of images that yields more accurate 3D models. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, and Geri to obtain the above-described limitations in claim 10. However, the combination of Tolkowsky, Feiner, and Geri does not explicitly teach, the alignment computer automatically selecting, using a deep neural network based on learning from historical data.
In yet another analogous field of endeavor, Mattiuzzi teaches, the alignment computer automatically selecting, (Mattiuzzi, ¶0064: “applying the automatic trackable landmark selection step”) using a deep neural network (Mattiuzzi, ¶0069: “As classification algorithms any kind of these algorithms can be used… artificial neural networks”) based on learning from historical data, (Mattiuzzi, ¶0195: “The database records are used to train in an usual manner the classification algorithm”) (Mattiuzzi, ¶0064: “Each vector coding each pixel or voxel coinciding with a validly trackable landmark forming a record of a training database for a classification algorithm”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner and in further view of Geri using the teachings of Mattiuzzi to introduce automatic selection of landmarks. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of automatic image alignment based on a automatically selected landmark. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri and Mattiuzzi to obtain the invention in claim 10.
Regarding claim 11, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 10, Tolkowsky further teaches, wherein the CT scan images are obtained from a CT scan of the patient (Tolkowsky, ¶0279: “3D CT image data of the portion of the skeletal anatomy… acquired using a CT scanner”) that is performed with the patient in a prone or horizontal position. (Tolkowsky, ¶0360: “patient lying on his/her back… on the stomach or on the side”).
Regarding claim 15, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, A method of Tolkowsky further teaches, treating the patient using a plurality of views provided by the patient specific 3D model generated by claim 10. (Tolkowsky, ¶0343: “spinal roadmap is displayed while the intervention is performed”).
Regarding claim 17, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 10, wherein the alignment computer is configured to generate Feiner further teaches, said 3D model to have 6 degrees of freedom. (Feiner, ¶0038: “AR guidance display can include an IMU or a six-degree-of-freedom tracking technology”)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi using the additional teachings of Feiner to introduce 6 degrees of freedom. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of obtaining a spine model that can be moved and rotated in all directions. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri and Mattiuzzi to obtain the invention in claim 17.
Regarding claim 18, Tolkowsky teaches, A method for aligning images (Tolkowsky, ¶0359: “3D image data and 2D x-ray images are… aligned”) for providing a(Tolkowsky, ¶0342: “the spinal roadmap is generated upon an image that is a fused combination”) comprising the steps of: an alignment computer receiving an x-ray image of the spine of the particular patient, (Tolkowsky, ¶0050: “computer processor configured: [0051] to receive the first and second 2D x-ray images”) said x-ray image being taken by an x-ray with the patient (Tolkowsky, ¶0467: “an x-ray image in which the tool and the subject's anatomy are visible”) provided in a first position; (Tolkowsky, ¶0301: “the position of the subject relative to the 2D imaging device”) the alignment computer receiving CT scan images of the spine of the particular patient, (Tolkowsky, ¶0279: “3D CT image data of the portion of the skeletal anatomy… acquired using a CT scanner”) said CT Scan being taken with the patient provided in a second position different than said first position; (Tolkowsky, ¶0360: “patient lying on his/her back… on the stomach or on the side”) the alignment computer preparing a plurality of vertebrae of the CT (Tolkowsky, ¶0360: “registration of the 3D image data to the 2D images is performed on a per-vertebra basis even in cases in which segmentation of a vertebra in the 3D image”) the alignment computer executing an artificial intelligence algorithm (Tolkowsky, ¶0368: “deep learning stage to facilitate the matching of DRRs from the CT image of the subject's vertebra to x-ray images”) d CT scan segments with the x-ray image (Tolkowsky, ¶0179: “automatically determine a location of the given vertebra… register the given vertebra within the first and second 2D x-ray images to the given vertebra within the 3D image data”) using the one or more landmarks to align the x-ray image with the CT scan segments; (Tolkowsky, ¶0299: “markers (and/or a rigid radiopaque jig) that appear in a plurality of different in x-ray image views of the subject are used to aid registering x-ray images to 3D image data (e.g., to CT image data)” the alignment computer generating the (Tolkowsky, ¶0360: “registration of the 3D image data to the 2D images is performed with respect to a spinal segment”). However, Tolkowsky does not explicitly teach, storing said patient specific 3D model for providing future access to said patient specific 3D model to a device of a user, wherein said patient specific 3D model is available for use in diagnosing a medical condition and/or preparing a medical procedure in advance of the procedure and an artificial intelligence algorithm configured to automatically select one or more landmarks on the X-ray image, using a deep neural network, based on learning from historical data from previous alignment procedures for optimizing said selecting of the one or more landmarks.
In an analogous field of endeavor, Feiner further teaches, storing (Feiner, ¶0063: “saving all or part of any data from the medical procedure such as the 3D model”) said patient specific 3D model (Feiner, ¶0078: “patient-specific virtual anatomic 3D models created”) for providing future access to said patient specific 3D model (Feiner, ¶0054: “AR guidance can be stored in a memory, and retrieved at the clinician's request”) to a device of a user, (Feiner, ¶0070: “device can include an interface, coupled to the processor, which permits a user to manipulate the displayed augmented reality guidance”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky using the teachings of Feiner to introduce a patient specific 3D model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of one model for representing both X-ray and CT scans for better diagnosis of the patient. Therefore, it would have been obvious to combine the analogous arts Tolkowsky and Feiner to obtain the above-described limitations of claim 18. However, the combination of Tolkowsky and Feiner does not explicitly teach, wherein said patient specific 3D model is available for use in diagnosing a medical condition and/or preparing a medical procedure in advance of the procedure and an artificial intelligence algorithm configured to automatically select one or more landmarks on the X-ray image, using a deep neural network, based on learning from historical data from previous alignment procedures for optimizing said selecting of the one or more landmarks.
Geri further teaches, wherein said patient specific 3D model is available for use in diagnosing a medical condition (Geri, ¶0074: “patient specific models based on patient imaging or other diagnostic tools or laboratory inputs”) and/or preparing a medical procedure in advance of the procedure. (Geri, ¶0006: “wherein said modeling system is configured for building a case to support said surgical procedure in advance of said procedure by creating models for the particular patient using the patient medical images”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, using the teachings of Geri to introduce patient-specific 3D model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of accurate 3D model that can be used for diagnosis and/or prepare/plan for a surgical procedure. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner and Geri to obtain the above-described limitations in claim 18. However, the combination of Tolkowsky, Feiner and Geri does not explicitly teach, an artificial intelligence algorithm configured to automatically select one or more landmarks on the X-ray image, using a deep neural network, based on learning from historical data from previous alignment procedures for optimizing said selecting of the one or more landmarks.
In yet another analogous field of endeavor, Mattiuzzi teaches, an artificial intelligence algorithm configured to automatically select (Mattiuzzi, ¶0064: “applying the automatic trackable landmark selection step”) one or more landmarks on the X-ray image, (Mattiuzzi, ¶0064: “Each vector coding each pixel or voxel coinciding with a validly trackable landmark”) using a deep neural network, (Mattiuzzi, ¶0069: “As classification algorithms any kind of these algorithms can be used… artificial neural networks”) based on learning from historical data (Mattiuzzi, ¶0195: “The database records are used to train in an usual manner the classification algorithm”) from previous alignment procedures for optimizing said selecting of the one or more landmarks. (Mattiuzzi, ¶0064: “Each vector coding each pixel or voxel coinciding with a validly trackable landmark forming a record of a training database for a classification algorithm”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner and in further view of Geri using the teachings of Mattiuzzi to introduce automatic selection of landmarks. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of automatic image alignment based on a automatically selected landmark. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri and Mattiuzzi to obtain the invention in claim 18.
Regarding claim 19, it recites a method with steps corresponding to the more specified steps of the method recited in claim 17. Therefore, the recited steps of method claim 19 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 17. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri and Mattiuzzi, presented in rejection of claim 17, apply to this claim.
Regarding claim 20, it recites a method with steps corresponding to the more specified steps of the method recited in claim 18. Therefore, the recited steps of method claim 20 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 18. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri and Mattiuzzi presented in rejection of claim 18, apply to this claim. Tolkowsky further teaches, a spine deformation treatment (Tolkowsky, ¶0010: “spine surgery, minimally-invasive spine surgery”) the alignment computer accurately and automatically aligning the segmented polygons with the x-ray image (Tolkowsky, ¶0179: “automatically determine a location of the given vertebra… register the given vertebra within the first and second 2D x-ray images to the given vertebra within the 3D image data”) using the one or more landmarks (Tolkowsky, ¶0299: “markers (and/or a rigid radiopaque jig) that appear in a plurality of different in x-ray image views of the subject are used to aid registering x-ray images to 3D image data (e.g., to CT image data)”.
Regarding claim 1, it recites a method with steps corresponding to the more specified steps of the method recited in claim 10. Therefore, the recited steps of method claim 1 are mapped to the proposed combination of Tolkowsky, Feiner, Geri and Mattiuzzi in the same manner as the corresponding steps in method claim 10. In addition, Geri teaches, wherein said patient specific 3D model is available for use in diagnosing a medical condition (Geri, ¶0074: “patient specific models based on patient imaging or other diagnostic tools or laboratory inputs”) and/or preparing a medical procedure in advance of the procedure. (Geri, ¶0006: “wherein said modeling system is configured for building a case to support said surgical procedure in advance of said procedure by creating models for the particular patient using the patient medical images”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, using the additional teachings of Geri to introduce patient-specific 3D model. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of accurate 3D model that can be used for diagnosis and/or prepare/plan for a surgical procedure. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri and Mattiuzzi to obtain the invention in claim 1.
Regarding claim 2, it recites a method with steps corresponding to the more specified steps of the method recited in claim 11. Therefore, the recited steps of method claim 2 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 11. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri and Mattiuzzi presented in rejection of claim 10, apply to this claim.
Regarding claim 5, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 1, wherein said biological features of the patient include at least a portion of the spine of the patient. (Tolkowsky, ¶0021: “images being of respective locations along at least a portion of the subject's spine”).
Regarding claim 7, it recites a method with steps corresponding to the more specified steps of the method recited in claim 15. Therefore, the recited steps of method claim 7 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 15. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri and Mattiuzzi presented in rejection of claim 10, apply to this claim.
Regarding claim 9, it recites a method with steps corresponding to the more specified steps of the method recited in claim 17. Therefore, the recited steps of method claim 9 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 17. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri and Mattiuzzi presented in rejection of claim 17, apply to this claim.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tolkowsky et al. (US 2019/0350657 A1), in view of Feiner et al. (US 2020/0188028 A1), in further view of Geri et al. (US 2017/0035517 A1), still in further view of Mattiuzzi et al. (US 20100135544 A1) and yet in further view of Brown et al. (US 2021/0056354 A1).
Regarding claim 14, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 10, wherein the alignment computer converts at least a portion of the CT images. However, the combination of Tolkowsky, Feiner, Geri and Mattiuzzi does not explicitly teach, (convert an image) into segmented polygons using a deep neural network.
In an analogous field of endeavor, Brown teaches, (convert an image) into segmented polygons using a deep neural network. (Brown, ¶0025: "segment vertebrae from image volume 110 using trained neural network 150").
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi using the teachings of Brown to introduce neural network-based segmentation. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of automatically segmenting the CT image using a neural network. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri, Mattiuzzi and Brown to obtain the invention in claim 14.
Regarding claim 6, it recites a method with steps corresponding to the more specified steps of the method recited in claim 14. Therefore, the recited steps of method claim 6 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 14. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri, Mattiuzzi and Brown presented in rejection of claim 14, apply to this claim.
Claims 3-4 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Tolkowsky et al. (US 2019/0350657 A1), in view of Feiner et al. (US 2020/0188028 A1), in further view of Geri et al. (US 2017/0035517 A1), still in further view of Mattiuzzi et al. (US 20100135544 A1), and yet in further view of Lang (US 20210192759 A1).
Regarding claim 12, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 11, Tolkowsky further teaches, wherein said x-ray scan images are obtained from an x-ray of the patient (Tolkowsky, ¶0467: “an x-ray image in which the tool and the subject's anatomy are visible”) however, the combination of Tolkowsky, Feiner, Geri and Mattiuzzi does not explicitly teach, that is performed with the patient in a standing or vertical position.
In an analogous field of endeavor, Lang teaches, that is performed with the patient in a standing or vertical position. (Lang, ¶1132: "X-rays can be obtained with the patient in upright, supine and/or prone position").
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi using the teachings of Lang to introduce X-ray in vertical position. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of diagnosing the spine deformity while the patient is standing. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri, Mattiuzzi and Lang to obtain the invention in claim 12.
Regarding claim 13, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 10, wherein said x-ray scan images are obtained from a x-ray of the patient. However, the combination of Tolkowsky, Feiner, Geri and Mattiuzzi does not explicitly teach, that is performed with the patient in a standing or vertical position.
In an analogous field of endeavor, Lang teaches, that is performed with the patient in a standing or vertical position. (Lang, ¶1132: "X-rays can be obtained with the patient in upright, supine and/or prone position"). The proposed combination as well as the motivation for combining Tolkowsky, Feiner, Geri, Mattiuzzi and Lang references presented in the rejection of claim 12, apply to claim 13 and are incorporated herein by reference. Thus, the method recited in claim 13 is met by Tolkowsky, Feiner, Geri, Mattiuzzi and Lang.
Regarding claim 3, it recites a method with steps corresponding to the more specified steps of the method recited in claim 12. Therefore, the recited steps of method claim 3 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 12. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri, Mattiuzzi and Lang presented in rejection of claim 12, apply to this claim.
Regarding claim 4, it recites a method with steps corresponding to the more specified steps of the method recited in claim 13. Therefore, the recited steps of method claim 4 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 13. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri, Mattiuzzi and Lang presented in rejection of claim 12, apply to this claim.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tolkowsky et al. (US 2019/0350657 A1), in view of Feiner et al. (US 2020/0188028 A1), in further view of Geri et al. (US 2017/0035517 A1), still in further view of Mattiuzzi et al. (US 20100135544 A1) and yet in further view of Wang et al. (US 2020/0281556 A1).
Regarding claim 16, Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi teaches, The method of claim 10. However, the combination of Tolkowsky, Feiner, Geri and Mattiuzzi does not explicitly teach, wherein the alignment computer detects said one or more landmarks using multiple learning networks.
In an analogous field of endeavor, Wang teaches, wherein the alignment computer detects (Wang, ¶0029: “The machine is trained for landmark detection”) said one or more landmarks using multiple learning networks. (Wang, ¶0008: “second of the two or more networks is trained to locate… markers… A third of the two or more networks may be trained to refine positions of the… markers”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tolkowsky in view of Feiner, in further view of Geri and still in further view of Mattiuzzi using the teachings of Wang to introduce multiple neural networks. A person skilled in the art would be motivated to combine the known elements as described above and achieve the predictable result of detecting refined locations of the landmarks in an X-ray image. Therefore, it would have been obvious to combine the analogous arts Tolkowsky, Feiner, Geri, Mattiuzzi and Wang to obtain the invention in claim 16.
Regarding claim 8, it recites a method with steps corresponding to the more specified steps of the method recited in claim 16. Therefore, the recited steps of method claim 8 are mapped to the proposed combination in the same manner as the corresponding steps in method claim 16. Additionally, the rationale and motivation to combine Tolkowsky, Feiner, Geri, Mattiuzzi and Wang presented in rejection of claim 16, apply to this claim.
Conclusion
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/MEHRAZUL ISLAM/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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