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 .
Claim Status
This action is in response to the application filed on February 28, 2025. Claims 1-12 and 14-16 are pending and have been examined.
Priority
Receipt is acknowledged that application claims priority to foreign application with application number EP22193692.5 dated September 02, 2022. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Receipt is acknowledged that application is a National Stage application of PCT/EP2023/073073 with a priority date of August 23, 2023 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 28, 2025 and October 13, 2025 has been considered by the examiner.
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.
Claim(s) 1-10, 12, and 14-16 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1 and 14 these claims recite the following limitations which are found to be abstract ideas not reciting a practical application or significantly more, with claim 1 being exemplary:
detecting a 2D contour of an anatomical feature (abstract idea as a mental process as a human is capable of detecting a 2D contour of an anatomical feature);
obtaining a 3D model representative of the anatomical feature (abstract idea as a mental process as a human is capable of obtaining a 3D model representative of the anatomical feature);
estimating a pose of the anatomical feature from the 2D medical image (abstract idea as a mental process as a human is capable of estimating a pose of the anatomical feature from the 2D medical image);
adjusting the 3D model based on the estimated pose (abstract idea as a mental process as a human is capable of adjusting the 3D model based on the estimated pose);
generating a 2D projection from the pose adjusted 3D model (mathematical concepts, mathematical relationships, mathematical formulas or equations, mathematical calculations);
and predicting the 2D contour of the anatomical feature from the 2D projection (mathematical concepts, mathematical relationships, mathematical formulas or equations, mathematical calculations);
This judicial exception is not integrated into a practical application for the following reasons. Claims 1 and 14 both recite the additional element of “detecting, based on the detected 2D contour and the predicted 2D contour, the anatomical abnormality,” however, this limitation also recites an abstract idea as insignificant pre/post-solution extra activity that nonetheless do not integrate the abstract idea into a practical application.
Claim 14 further recites the additional element of “a memory that stores a plurality of instructions,” and “and a processor coupled to the memory and configured to execute the plurality of instructions to.” While these limitations include additional elements of a processor and a memory these are not sufficient to recite a practical application of the abstract ideas recited in claim 14 as it amounts to mere generic computer elements and thus amount to no more than a recitation of the words “apply it” (or an equivalent) or are no more than the mere instructions to implement an abstract idea or other exception on a computer. See MPEP 2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claims 1 and 14 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions.
Therefore, independent claims 1 and 14 are directed towards an abstract idea without a practical application or significantly more.
Regarding claims 2-6, 8-10, 12, and 15-16 the limitations are merely directed towards insignificant pre/post-solution extra activity that nonetheless do not integrate the abstract idea recited from claim 1 into a practical application.
Regarding claim 7 the limitations are merely directed towards mathematical concepts, mathematical relationships, mathematical formulas or equations, mathematical calculations that nonetheless do not integrate the abstract idea recited from claim 1 into a practical application.
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.
Claim(s) 1-5, 7, 9, 11-12, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Ehlke et al, US 20230071033.
Regarding claim 1, Wu teaches a computer-implemented method for detecting an anatomical abnormality in a 2D medical image, the method comprising (see Wu, Paragraph [0001], “an improved data processing apparatus and method and more specifically to mechanisms for providing automatic bounding region annotation for localization of abnormalities”):
obtaining the 2D medical image (Paragraph [0057], “obtained from a publicly available medical image dataset source system 114”);
detecting a 2D contour of an anatomical feature (see Wu, Paragraph [0062], “the masks 134 comprise contours of various shapes matching the identified anatomical structures”);
and predicting the 2D contour of the anatomical feature from the 2D projection (see Wu, Paragraph [0066], “the raw bounding region system 150 predicts the coordinates for the contour boundaries of the standardized anatomical zones identified in the template registered mask 142”);
detecting, based on the detected 2D contour and the predicted 2D contour, the anatomical abnormality (see Wu, Paragraph [0081], “the dataset 190 may be used as a training dataset for machine learning training of an AI or cognitive computing system designed to perform medical image analysis, such as anomaly detection and location in non-annotated and non-labeled medical images”).
Wu does not expressively teach
obtaining a 3D model representative of the anatomical feature;
predicting the 2D contour of the anatomical feature based on the 3D model, wherein predicting the 2D contour of the anatomical feature based on the 3D model comprises:
estimating a pose of the anatomical feature from the 2D medical image;
adjusting the 3D model based on the estimated pose;
generating a 2D projection from the pose adjusted 3D model,
However, Ehlke in a similar invention in the same field of endeavor teaches
obtaining a 3D model representative of the anatomical feature (see Ehlke, Paragraph [0114], “FIG. 1: Intensity-based registration of a 3-D model (a-SSIM) to an 2-D X-ray”);
predicting the 2D contour of the anatomical feature based on the 3D model, wherein predicting the 2D contour of the anatomical feature based on the 3D model comprises (see Ehlke, Paragraph [0033], “the reconstructed anatomical model can be further processed, among others, to create a patient-specific therapeutic plan by predicting the load on a planned implant through functional analysis in 3-D space from fluoroscopy or for diagnosis or post-operative 3-D assessment of the outcome”):
estimating a pose of the anatomical feature from the 2D medical image (see Ehlke, Paragraph [0111], “Normal Shape Estimation (Desired/Native/Healthy Vs. Actual State Comparison)”);
adjusting the 3D model based on the estimated pose (see Ehlke, Paragraph [0083], “adjustment is achieved by adjustment or modification of at least one feature present in the 3-D representation and the corresponding object or body part or a 2-D X-ray image of said body part”);
generating a 2D projection from the pose adjusted 3D model (see Ehlke, Paragraph [0114], “To evaluate the fit, virtual X-ray images are projected from the candidates and then compared to the original radiographs by means of 2-D similarity measures”),
The combination of Wu and Ehlke are analogous art because they are both in the same field of endeavor of x-ray imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to estimate a pose of the anatomical feature using normal shape estimation (Desired/Native/Healthy Vs. Actual State Comparison); adjust the 3D model based on the estimation by adjustment or modification of at least one feature present in the 3D representation and the corresponding object or body part or a 2D X-ray image; generate a 2D projection from the pose adjusted model by projecting virtual X-ray images from the candidates and then compared to original radiographs; obtain 3D model based on the 2D medical image, the anatomical feature, and/or user input by obtaining a three-dimensional (3-D) representation of a body part of a subject from at least one two-dimensional (2-D) X-ray image of the body part; generate a difference between the detected 2D contour and the predicted 2D contour by determining the difference between at least one virtual 2-D X-ray image and the at least one 2-D X-ray image of the body part of the subject; for the anatomical abnormality to be a joint replacement, bone fracture or bone/joint correction; and generating treatment advice, based on the detected anatomical abnormality by analyzing the datasets and further processed by common/standard programs for diagnostics, treatment planning, as well as prosthesis design as taught in the method of Ehlke in the method of Wu to provide improved 3-D information without the burden of additional or elevated radiation exposure and costs (Ehlke, Paragraph [0012]).
Regarding claim 2, Wu in view of Ehlke teaches the method of claim 1,
wherein detecting the 2D contour includes segmenting the 2D medical image(see Wu, Paragraph [0061], “the medical image segmentation system 130 receives one or more original medical images 120 from the medical image atlas source system 114, where these medical images have not yet been annotated with boundary region annotations or labeled with anomaly (findings) labels”).
The rationale of claim 1 has been applied herein.
Regarding claim 3, Wu in view of Ehlke teaches the method of claim 1,
further comprising: classifying the anatomical abnormality (see Wu, Paragraph [0038], “This dataset is then input to an AI or cognitive computing system as a training dataset to thereby train that cognitive computing system to identify and classify anomalies”).
The rationale of claim 1 has been applied herein.
Regarding claim 4, Wu in view of Ehlke teaches the method of claim 3,
wherein classifying the anatomical abnormality is performed by a machine learning algorithm (see Wu, Paragraph [0038], “the outputs of the AI or cognitive computing system may be evaluated to adjust, through a machine learning process, the operational parameters of the AI or cognitive computing system to minimize a loss (error) in the outputs generated by the AI or cognitive computing system. This training may utilize a supervised or unsupervised machine learning process”).
The rationale of claim 3 has been applied herein.
Regarding claim 5, Wu in view of Ehlke teaches the method of claim 1,
wherein classifying the anatomical abnormality is performed by a machine learning algorithm (see Wu, Paragraph [0038], “the outputs of the AI or cognitive computing system may be evaluated to adjust, through a machine learning process, the operational parameters of the AI or cognitive computing system to minimize a loss (error) in the outputs generated by the AI or cognitive computing system. This training may utilize a supervised or unsupervised machine learning process”).
The rationale of claim 1 has been applied herein.
Regarding claim 7, Wu in view of Ehlke teaches the method of claim 1,
further comprising generating a difference between the detected 2D contour and the predicted 2D contour (see Ehlke, Paragraph [0001], “e) determining the difference between the at least one virtual 2-D X-ray image and the at least one 2-D X-ray image of the body part of the subject with respect to the at least one feature”).
The rationale of claim 1 has been applied herein.
Regarding claim 9, Wu in view of Ehlke teaches the method of claim 1,
wherein the anatomical abnormality is any one of a bone fracture, and a ligament rupture (see Ehlke, Paragraph [0101], “For joint replacement, bone fracture or bone/joint correction surgery, a suitable implant type and the configuration of the respective implant components (e.g. joint socket and stem, screws, plates) are typically selected from 2-D X-ray images before the procedure”).
The rationale of claim 1 has been applied herein.
Regarding claim 11, Wu in view of Ehlke teaches the method of claim 1,
further comprising generating treatment advice, based on the detected anatomical abnormality (see Ehlke, Paragraph [0120], “virtual 2-D X-ray images generated either from 3-D models or from virtual CT datasets, can be analyzed, displayed and further processed by common/standard computer programs for diagnostics, treatment planning, as well as prosthesis design”).
The rationale of claim 1 has been applied herein.
Regarding claim 12, Wu in view of Ehlke teaches the method of claim 1,
wherein the medical image is an X-ray image (see Wu, Paragraph [0057], “using the running example of chest x-rays and lungs being the anatomical structures of interest, the initial set of medical images may be an NIH chest x-ray (CXR) dataset”).
The rationale of claim 1 has been applied herein.
As per claim 14, Claim 14 claims A system for detecting an anatomical abnormality in a 2D medical image, comprising: a memory that stores a plurality of instructions; and a processor coupled to the memory and configured to execute the plurality of instructions to: to complete the same limitations as Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
Wu further teaches a memory that stores a plurality of instructions; and a processor coupled to the memory and configured to execute the plurality of instructions to (see Wu, Paragraph [0007], “one or more processors and a memory coupled to the one or more processors”).
Regarding claim 15, Wu in view of Ehlke teaches the method of claim 14
further comprising an X-ray source and an X- ray detector (see Ehlke, Paragraph [0085], “One or more X-ray markers may, in one embodiment of the invention, be used to determine or estimate the size of an imaged body part or individual and/or, the distance between the source and/or detector and the imaged body part or individual”).
The rationale of claim 14 has been applied herein.
Regarding claim 16, Wu in view of Ehlke teaches
A non-transitory computer readable medium for storing executable instructions that, when executed, cause the method of claim 1 to be performed (see Wu, Paragraph [0048], “A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire”).
The rationale of claim 1 has been applied herein.
Claim(s) 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Ehlke et al, US 20230071033 in further view of Kunze et al, US 20210142482.
Regarding claim 6, Wu in view of Ehlke does not expressively teach the method of claim 1,
wherein the 3D model is any one of: a computer aided design model; and a reference model constructed from 3D medical images of the anatomical feature in at least one pose.
However, Kunze in a similar invention in the same field of endeavor teaches
wherein the 3D model is any one of: a computer aided design model; and a reference model constructed from 3D medical images of the anatomical feature in at least one pose (see Kunze, Paragraph [0005], “image data may involve three-dimensional (3D) image data, e.g., a 3D representation of at least the target object, such as by way of a computed tomography (CT) volume or magnetic resonance (MR) imaging volume. The target object may be an anatomical element or an anatomical feature of a patient, (e.g., a bone or an organ or a respective section or extract thereof)”).
The combination of Wu, Ehlke, and Kunze are analogous art because they are all in the same field of endeavor of x-ray imaging.. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for image data to involve three-dimensional (3D image data) and a 3D representation of the target object by way of computed tomography (CT) volume and the target object may be an anatomical element or an anatomical feature of a patient as taught in the method of Kunze in the method of Wu in view of Ehlke to enable a more robust segmentation of image data affected by interference (Kunze, Paragraph [0004]).
Regarding claim 8, Wu in view of Ehlke does not expressively teach the method of claim 1,
wherein the anatomical feature is any one of bone tissue, and ligament tissue.
However, Kunze in a similar invention in the same field of endeavor teaches
wherein the anatomical feature is any one of bone tissue, and ligament tissue (see Kunze, Paragraph [0050], “a predefined target object, (e.g., a bone or a predefined tissue region of the patient 2), may be mapped in this context”).
The combination of Wu, Ehlke, and Kunze are analogous art because they are all in the same field of endeavor of x-ray imaging.. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, for the anatomical feature to be any one of bone tissue and ligament tissue such as bone or a predefined tissue region of the patient as taught in the method of Kunze in the method of Wu in view of Ehlke to enable a more robust segmentation of image data affected by interference (Kunze, Paragraph [0004]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Ehlke et al, US 20230071033 in further view of Boddington et al, WO 2020056086.
Regarding claim 10, Wu in view of Ehlke does not expressively teach the method of claim 3,
wherein classifying the anatomical abnormality, is based on any one of Weber classification, Pauwels classification, Smith and Colles classification, Salter-Harris classification, and the AO/OTA classification.
However, Boddington in a similar invention in the same field of endeavor teaches
wherein classifying the anatomical abnormality, is based on any one of Weber classification, Pauwels classification, Smith and Colles classification, Salter-Harris classification, and the AO/OTA classification (see Boddington, Paragraph [0116], “Module 16 is a fracture identification and reduction module with access to an AO/OTA Classification Dataset interprets the image and makes a classification of the bone, bone section, type and group of the fracture”).
The combination of Wu, Ehlke, and Boddington are analogous art because they are all in the same field of endeavor of x-ray imaging.. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to classify an anatomical abnormality using AO/OTA classification as taught in the method of Boddington in the method of Wu in view of Ehlke to anticipate problems and predict outcomes in a surgery or in a postoperative follow-up period (Boddington, Paragraph [0117]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
