Detailed Action
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed on 06/01/2026 has been entered. Claims 1-2, 5, 10-15, and 18-20 have been amended. Claims 1-20 remain pending in the application. Rejections of claims 1-10 under 35 U.S.C. 112(b) (pre-AIA 35 U. S. C. 112, second paragraph) are withdrawn.
Claim Rejections - 35 USC § 102
3. 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.
4. Claims 1-2, 4-5, and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JEREBKO (US 20180061090 A1).
Regarding claim 1, JEREBKO (Figs. 1-8) discloses a computing system (Fig. 8; computing unit 11; [0018]-[0019] and [0079]-[0081]), comprising: a memory with instructions including a digitally reconstructed radiograph view optimization instruction (storage and instructions; [0018]-[0019]); a processor (computing unit 11 comprising a microprocessor; [0019]) configured to execute the instructions to: generate a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters and three-dimensional computed tomography image data (Figs. 1-8, e.g., Fig. 1, step I, step II.5, and step III, and [0059]-[0060], [0065]-[0067]; reconstructed radiographs based on projection parameters PP and 3D CT image data VB) acquired from an imaging examination (e.g., Figs.. 4-5 and [0004], [0053]-[0054]; imaging examination of a hand or an ankle for a diagnostic procedure); identify an optimal sub-set of the plurality of digitally reconstructed radiographs to read based on a reason that the three- dimensional computed tomography image data was acquired in the imaging examination (e.g., Figs. 4-7 and [0025], [0045]-[0046], [0067], [0069]-[0070]; select desired or optimal reconstructed radiographs for diagnostic procedure); and display the identified optimal sub-set of the plurality of digitally reconstructed radiographs for reading (Figs. 1 and 8 and [0079]; display device 15 outputs images).
Regarding claim 2, JEREBKO (Figs. 1-8) discloses the system of claim 1, wherein the processor is further configured to execute the instructions to determine the different sets of projection parameters based on a pre-defined list of projection parameters (Fig. 1, steps II.6 and II.7; [0065]-[0067] and [0031]; e.g., user-defined projection parameters).
Regarding claim 4, JEREBKO (Figs. 1-8) discloses the system of claim 1, wherein the different sets of projection parameters include projection parameters for generating a digitally reconstructed radiograph in an arbitrary direction ([0031]-[0032]).
Regarding claim 5, JEREBKO (Figs. 1-8) discloses the system of claim 1, wherein the processor is configured to apply artificial intelligence to identify the optimal sub-set of the plurality of digitally reconstructed radiographs ([0067] and claim 14; machine learning).
Regarding claim 14, JEREBKO (Figs. 1-8) discloses the system of claim 1, wherein the processor is further configured to execute the instructions to identify an optimal view direction of the sub-set directly from the three-dimensional computed tomography image data (Figs. 4-7 and [0033]-[0034], [0038], and [0045], [0072]; optimal projection direction).
Regarding claim 15, JEREBKO (Figs. 1-8) discloses a computer-implemented method, comprising: generating a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters and three-dimensional computed tomography image data (Figs. 1-7, e.g., Fig. 1, step I, step II.5, and step III, and [0059]-[0060], [0065]-[0067]; reconstructed radiographs based on projection parameters PP and 3D CT image data VB) acquired from an imaging examination (e.g., Figs.. 4-5 and [0004], [0053]-[0054]; imaging examination of a hand or an ankle for a diagnostic procedure); identifying an optimal sub-set of the plurality of digitally reconstructed radiographs to read based on a reason that the three-dimensional computed tomography image data was acquired in the imaging examination (e.g., Figs. 4-7 and [0025], [0045]-[0046], [0067], [0069]-[0070]; select desired or optimal reconstructed radiographs for diagnostic procedure); and displaying the identified optimal sub-set of the plurality of digitally reconstructed radiographs for reading (Figs. 1 and 8 and [0079]; display device 15 outputs images).
Regarding claim 18, JEREBKO (Figs. 1-8) discloses a non-transitory computer-readable storage medium storing computer executable instructions (storage and instructions; [0018]-[0019]), which, when executed by a processor of a computer (computing unit 11 comprising a microprocessor; [0019]), cause the processor to: generate a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters and three-dimensional computed tomography image data (Figs. 1-8, e.g., Fig. 1, step I, step II.5, and step III, and [0059]-[0060], [0065]-[0067]; reconstructed radiographs based on projection parameters PP and 3D CT image data VB) acquired from an imaging examination (e.g., Figs.. 4-5 and [0004], [0053]-[0054]; imaging examination of a hand or an ankle for a diagnostic procedure); identify an optimal sub-set of the plurality of digitally reconstructed radiographs to read based on a reason that the three-dimensional computed tomography image data was acquired in the imaging examination (e.g., Figs. 4-7 and [0025], [0045]-[0046], [0067], [0069]-[0070]; select desired or optimal reconstructed radiographs for diagnostic procedure); and display the identified optimal sub-set of the plurality of digitally reconstructed radiographs for reading (Figs. 1 and 8 and [0079]; display device 15 outputs images).
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 6-13 and 17 are rejected under 35 U.S.C. 103 as unpatentable over JEREBKO (US 20180061090 A1) in view of GEORGESCU (US 20150238148 A1).
Regarding claim 6, JEREBKO (Figs. 1-8) discloses the system of claim 5, but does not disclose wherein the artificial intelligence includes a Deep Learning network. However, GEORGESCU (e.g., Fig. 5B) discloses a computing system, wherein the processor employs artificial intelligence to identify the optimal sub-set of the plurality of digitally reconstructed radiographs, and wherein the artificial intelligence includes a Deep Learning network (e.g., Figs. 1-5 and 13; using deep neural networks in medical image data). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using deep neural networks.
Regarding claim 7, JEREBKO (Figs. 1-8) discloses the system of claim 5, but does not disclose wherein the identification is based on a classification algorithm. However, GEORGESCU (e.g., Fig. 5B) discloses wherein the identification is based on a classification algorithm ([0021]-[0022], [0056], and [0058]-[0061]; classification algorithm). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using machine learning.
Regarding claim 8, JEREBKO (Figs. 1-8) discloses the system of claim 5, but does not disclose wherein the identification is based on a regression algorithm. However, GEORGESCU (e.g., Fig. 5B) discloses wherein the identification is based on a regression algorithm ([0023], [0032]-[0035], [0038], [0053]-[0054], and [0067]; regression algorithm). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using machine learning.
Regarding claim 9, JEREBKO (Figs. 1-8) discloses the system of claim 5, but does not disclose wherein the identification is based on a detection algorithm. However, GEORGESCU (e.g., Fig. 5B) discloses wherein the identification is based on a detection algorithm (Figs. 1, 5, and 13, and [0021]-[0025], [0028], [0037]-[0038], [0044]-[0045], and [0070]; detection of an object of interest using deep learning model). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using machine learning.
Regarding claim 10, JEREBKO in view of GEORGESCU discloses the system of claim 5, GEORGESCU (e.g., Fig. 5B) discloses wherein the processor is further configured to execute the instructions to generate and display a heat map based on a result of the detection algorithm, wherein the heat map highlights a region of interest in the optimal sub-set of the plurality of digitally reconstructed radiographs (Figs. 11-12 and 14; [0038], [0041], and [0044], detection of an object of interest, [0051], detected anatomical object can be displayed on a display device). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO for the same reason above.
Regarding claim 11, JEREBKO in view of GEORGESCU discloses the system of claim 9, GEORGESCU (e.g., Fig. 5B) discloses wherein the processor is further configured to execute the instructions to apply a detection algorithm to at least a sub-portion of the three-dimensional computed tomography image data and evaluate the digitally reconstructed radiographs based on a visibility of structures detected in the digitally reconstructed radiographs (Figs. 5, 7-8, and 14; [0051], [0057], [0064], and [0073]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO for the same reason above.
Regarding claim 12, JEREBKO in view of GEORGESCU discloses the system of claim 11, GEORGESCU (e.g., Fig. 5B) discloses wherein the processor is further configured to execute the instructions to evaluate the digitally reconstructed radiographs based on a size of the detected structures in the digitally reconstructed radiographs (e.g., Fig. 14; [0073] and [0055]-[0056]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO for the same reason above.
Regarding claim 13, JEREBKO in view of GEORGESCU discloses the system of claim 11, GEORGESCU (e.g., Fig. 5B) discloses wherein the processor is further configured to execute the instructions to generate and display a heat map based a result of the detection algorithm and a result of the segmentation, wherein the heat map highlights a region of interest in the optimal sub-set of the plurality of digitally reconstructed radiographs (Figs. 11-12 and 14; [0038], [0041], and [0044], detection of an object of interest, [0051], detected anatomical object can be displayed on a display device). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO for the same reason above.
Regarding claim 17, JEREBKO (Figs. 1-8) discloses the computer-implemented method of claim 15, but does not disclose the method further comprising: identifying the optimal sub-set of the plurality of digitally reconstructed radiographs determining based on a trained convolutional neural network. However, GEORGESCU (e.g., Fig. 5B) discloses the method further comprising: identifying the optimal sub-set of the plurality of digitally reconstructed radiographs determining based on a trained convolutional neural network ([0031] and claim 15; CNN). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from GEORGESCU to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using deep neural networks.
7. Claims 3 and 16 are rejected under 35 U.S.C. 103 as unpatentable over JEREBKO (US 20180061090 A1) in view of NOORDHOEK (US 20110135053 A1).
Regarding claim 3, JEREBKO (Figs. 1-8) discloses the system of claim 1, wherein the different sets of projection parameters include projection parameters for generating a digitally reconstructed radiograph along a curved trajectory ([0004]; cone-beam projection indicates a circular trajectory). As another reference, NOORDHOEK (Figs. 1-5) discloses a computing system, wherein the different sets of projection parameters include projection parameters for generating a digitally reconstructed radiograph along a curved trajectory (Figs. 3-4; circular trajectory). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from NOORDHOEK to the computing system and method of JEREBKO. The combination/motivation would be to provide a 3D rotational X-ray imaging system for an anatomical object detection.
Regarding claim 16, JEREBKO (Figs. 1-8) discloses the computer-implemented method of claim 15, but does not disclose determining the different sets of projection parameters using regression to infer projection directions from the three-dimensional computed tomography image data. However, NOORDHOEK (Figs. 1-5) discloses a computer-implemented method, comprising: determining the different sets of projection parameters using regression to infer projection directions from the three-dimensional computed tomography image data (e.g., Fig. 4; [0029]-[0030] and [0042]-[0044]; projections using regression algorithm). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from NOORDHOEK to the computing system and method of JEREBKO. The combination/motivation would be to provide a 3D rotational X-ray imaging system for an anatomical object detection.
8. Claims 19-20 are rejected under 35 U.S.C. 103 as unpatentable over JEREBKO (US 20180061090 A1) in view of NOORDHOEK (US 20110135053 A1).
Regarding claim 19, JEREBKO discloses the computer-readable storage medium of claim 18, but does not disclose wherein the computer executable instructions, when executed by the processor, further cause the processor to: determine the different sets of projection parameters using regression to infer projection directions from the three-dimensional computed tomography image data. However, NOORDHOEK (Figs. 1-5) discloses wherein the computer executable instructions, when executed by the processor, further cause the processor to: determine the different sets of projection parameters using regression to infer projection directions from the three-dimensional computed tomography image data (e.g., Fig. 4; [0029]-[0030] and [0042]-[0044]; projections using regression algorithm). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from NOORDHOEK to the computing system and method of JEREBKO. The combination/motivation would be to provide a 3D rotational X-ray imaging system for an anatomical object detection.
9. Claim 20 is rejected under 35 U.S.C. 103 as unpatentable over JEREBKO (US 20180061090 A1) in view of NOORDHOEK (US 20110135053 A1) and further in view of KEMP (US 20190328461 A1).
Regarding claim 20, JEREBKO in view of NOORDHOEK discloses the computer-readable storage medium claim 17, but does not disclose discloses wherein the computer executable instructions, when executed by the processor, further cause the processor to: identify the optimal sub-set of the plurality of digitally reconstructed radiographs determining based on a trained convolutional neural network. However, KEMP discloses wherein the computer executable instructions, when executed by the processor, further cause the processor to: identify the optimal sub-set of the plurality of digitally reconstructed radiographs determining based on a trained convolutional neural network (Figs. 4-6; CNN; [0017]-[0019]). Therefore, it would have been obvious to one skilled in the art at the effective filing date of the claimed invention to incorporate the teaching from KEMP to the computing system and method of JEREBKO. The combination/motivation would be to provide a computing system and a method for an anatomical object detection in medical image data using deep neural networks.
Response to Arguments
10. Applicant's arguments filed 06/01/2026 have been fully considered but they are not persuasive.
11. Applicant has amended claims 1, 15, and 18. Applicant further argues that the cited references do not disclose that “Jerebko does not disclose to identify an optimal sub-set of Jerebko's synthetic projections to read based on a reason that the three-dimensional computed tomography image data was acquired in the imaging examination”.
The examiner respectfully disagrees with applicant’s arguments. JEREBKO (Figs. 1-8) discloses a computing system (Fig. 8; computing unit 11; [0018]-[0019] and [0079]-[0081]) configured to generate a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters PP and three-dimensional computed tomography (3D CT) image data VB acquired from an imaging examination of a hand or an ankle for a diagnostic procedure (Figs. 1-8, e.g., Fig. 1, step I, step II.5, and step III, and [0059]-[0060], [0053]-[0054], [0065]-[0067], and [0069]-[0070]). JEREBKO further discloses to identify and select an optimal sub-set of the plurality of digitally reconstructed radiographs to read for the diagnostic procedure (e.g., Figs. 4-7 and [0025], [0045]-[0046], [0067], [0069]-[0070]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUZHEN SHEN whose telephone number is (571)272-1407. The examiner can normally be reached on 9:00-18:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chanh Nguyen can be reached on 571-272-7772. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YUZHEN SHEN/Primary Examiner, Art Unit 2623
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