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 Objections
Claims 6 and 12 are objected to because of the following informalities: the acronyms “DICOM”, “RCNN”, and “YOLO” lack antecedent basis. 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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of a method for evaluating a mobile X-ray acquisition of a patient without significantly more. The claim(s) recite(s) the step of receiving an X-ray image of the patient; detecting a marker in the X-ray image using a deep learning model, wherein the marker comprises a plate of known geometry and a rod; determining a position of the rod of the marker; analyzing a projection of the rod in the X-ray image; and determining a position of an X-ray source above a bed of the patient based on the analyzed projection of the rod. This judicial exception is not integrated into a practical application because the steps generally link the use of the judicial exception to a particular technological environment, performing well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, and recite the concepts of gathering/evaluating data and performing mathematical calculations which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of deep learning model is well-understood, routine and conventional activity previously known to the industry, specified at a high level of generality, amounting to no more than the abstract idea.
Claims 2-12 are dependent upon claim 1 and includes all the limitations of claim 1. Therefore, claims 2-12 recites the same abstract idea of gathering/evaluating data and performing mathematical calculations which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps recite mathematical operations that are well-understood, routine and conventional activities previously known to the industry at a high level of generality amounting no more than the abstract idea.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of a non-transitory computer readable medium for evaluating a mobile X-ray acquisition of a patient without significantly more. The claim(s) recite(s) the step of receiving an X-ray image of the patient; detecting a marker in the X-ray image using a deep learning model, wherein the marker comprises a plate of known geometry and a rod; determining a position of the rod of the marker; analyzing a projection of the rod in the X-ray image; and determining a position of an X-ray source above a bed of the patient based on the analyzed projection of the rod. This judicial exception is not integrated into a practical application because the steps generally link the use of the judicial exception to a particular technological environment, performing well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, and recite the concepts of gathering/evaluating data and performing mathematical calculations which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of deep learning model is well-understood, routine and conventional activity previously known to the industry, specified at a high level of generality, amounting to no more than the abstract idea. The addition of general purpose computer components alone to perform such steps is not sufficient to transform a judicial exception into a patentable invention. The computer components are recited at a high level of generality and perform the basic functions of a computer (in this case, receiving data and performing mathematical operations) that would be needed to apply the abstract idea via a computer. Merely using generic computer components to perform the above identified basic computer functions to practice or apply the judicial exception does not constitute a meaningful limitation that would amount to significantly more than the judicial exception.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of an apparatus for evaluating a mobile X-ray acquisition of a patient without significantly more. The claim(s) recite(s) the step of receiving an X-ray image of the patient; detecting a marker in the X-ray image using a deep learning model, wherein the marker comprises a plate of known geometry and a rod; determining a position of the rod of the marker; analyzing a projection of the rod in the X-ray image; and determining a position of an X-ray source above a bed of the patient based on the analyzed projection of the rod. This judicial exception is not integrated into a practical application because the steps generally link the use of the judicial exception to a particular technological environment, performing well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, and recite the concepts of gathering/evaluating data and performing mathematical calculations which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of deep learning model is well-understood, routine and conventional activity previously known to the industry, specified at a high level of generality, amounting to no more than the abstract idea. The addition of general purpose computer components alone to perform such steps is not sufficient to transform a judicial exception into a patentable invention. The computer components are recited at a high level of generality and perform the basic functions of a computer (in this case, receiving data and performing mathematical operations) that would be needed to apply the abstract idea via a computer. Merely using generic computer components to perform the above identified basic computer functions to practice or apply the judicial exception does not constitute a meaningful limitation that would amount to significantly more than the judicial exception.
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.
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.
Claim(s) 1-2, 5, 10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sehnert et al. (US 2021/0106304) in view of Wang et al. (US 2020/0281556).
Sehnert et al. discloses a method for evaluating a mobile X-ray (100) acquisition of a patient comprising: receiving an X-ray image of the patient (S710); detecting a marker in the X-ray image (S720); determining a position of the marker (S720); analyzing a projection of the marker in the X-ray image (S720); and determining position of an X-ray source above a bed of the patient based on the analyzed projection (S730). Sehnert et al. discloses the subject matter substantially as claimed except for the shape of the marker. However, Sehnert et al. teaches can be different shapes or sizes allowing for ready identification ([0047]; [0054]). Therefore, it would have been obvious to one of ordinary skill in the art to have modified the shape of the marker as a change in shape is well within the skill level of one of ordinary skill in the art (MPEP 2144.04(IV)(B)). Sehnert et al. does not teach using a deep learning model. However, Wang et al. teaches in the same field of endeavor using machine learning model for estimating locations of markers for X-ray pose estimation ([0004]; [0008]; [0029]; [0078]). Therefore, it would have been obvious to one of ordinary skill in the art to have provided Sehnert et al. with deep learning model as taught by Wang et al. as it is well known to use deep learning in estimating marker locations.
With respect to claim 2, Sehnert et al. discloses analyzing the projection image ([0051]; S720).
With respect to claim 5, Sehnert et al. discloses determining distances to the bed ([0035]).
With respect to claim 10, Sehnert et al. discloses calculating a relative position of an image structure based on the determined position of the X-ray source relative to the patient (S730; S750; S760).
With respect to claim 16, Sehnert et al. discloses a non-transitory computer readable medium ([0061]).
With respect to claim 17, Sehnert et al. discloses a memory ([0061]) and processor ([0032]).
Claim(s) 7-9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sehnert et al. (US 2021/0106304) in view of Wang et al. (US 2020/0281556) as applied to claim 1, further in view of Soper et al. (US 2019/0038365).
With respect to claims 7-9, Sehnert et al. discloses the subject matter substantially as claimed except for wherein the marker comprises letters. However, Soper et al. teaches in the same field of endeavor wherein fiducial markers consists of letters ([0077]). Therefore, it would have been obvious to one of ordinary skill in the art to have provided letters as taught by Soper et al. as letters are a well known form of fiducial markers to allow for more detailed localization ([0077]).
With respect to claim 11, the Examiner’s position is that the arrangement of the marker is well within the skill level of one of ordinary skill in the art (MPEP 2144.04(VI)(C)).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sehnert et al. (US 2021/0106304) in view of Wang et al. (US 2020/0281556) as applied to claim 1, further in view of Kayser et al. (US 2023/0013233).
Sehnert et al. in view of Wang et al. discloses the subject matter substantially as claimed except for fast RCNN or YOLO. However, Kayser et al. teaches in the same field of endeavor using neural networks such as fast RCNN or YOLO as known neural network techniques for object detection ([0046]). Therefore, it would have been obvious to one of ordinary skill to have provided Sehnert et al. with known neural network techniques as taught by Kayser et al. to detect objects ([0046]).
Allowable Subject Matter
Claims 3-4 and 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record fails to disclose or render obvious the claimed combination of subject matter particularly measuring and determining the radius and height of the rod.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER LUONG whose telephone number is (571)270-1609. The examiner can normally be reached M-F 9-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anhtuan T Nguyen can be reached at (571)272-4963. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER LUONG/Primary Examiner, Art Unit 3797