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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Arguments
Applicant's arguments filed May 8, 2025, with respect to the pending claims, have been fully considered but they are not persuasive.
Applicant’s Representative submits that the prior art does not teach the claims because the prior art is directed to x-ray scatter removal whereas the claims are directed to fluorescence.
The examiner respectfully disagrees. The claims recite “information regarding a fluorescence substance provided in a radiation detection apparatus.” The claim does not further specify what type of fluorescence or material is being used for the medical image processing apparatus. Fluorescence is an optical phenomenon where the light/photon is absorbed at a certain wavelength and emitted at a longer wavelength, and therefore using broadest reasonable interpretation, the limitation “information regarding a fluorescence substance provided in a radiation detection apparatus” can be interpreted as any substance that absorbs light at a wavelength and emits light at a longer wavelength used in a radiation device. The prior art teaches using X-ray fluorescence to detect emitted light to detect objects (see Zhao ¶¶0276: “The x-ray system disclosed herein can perform spatially resolved x-ray fluorescence analysis. An x-ray excitation beam can be directed upon a subject to generate fluorescent x-rays, wherein the x-ray excitation beam includes a planar array of x-ray micro-beams. The individual x-ray micro-beams can each have a diameter smaller than low double-digit microns. The fluorescent x-rays can be imaged with an x-ray imaging system that includes an x-ray imaging optical system and an energy resolving and spatially resolving x-ray detector. The x-ray imaging optical system can collect fluorescent x-rays generated when a subject is illuminated by the x-ray excitation beam positioned such that its subject plane is coplanar with the plane of the planar array of microbeams within the depth of field of the x-ray imaging optical system. The energy dispersive and spatially resolving x-ray detector can be positioned at the image plane of the x-ray optical imaging system” emphasis added).
In view of this reasonable interpretation of the claims and the prior art, the examiner respectfully submits that the rejections set forth below remain proper.
Claim Rejections - 35 USC § 102
Claim(s) 1, 4-8, and 11-14 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Ying Zhao (US 2021/0244374 A1), hereinafter referred to as Zhao.
Regarding claim 1, Zhao teaches a medical image processing apparatus (Zhao Fig. 1A) comprising:
a readout unit configured to read out, from a storage unit that stores a plurality of pieces of learning result data obtained by machine learning by a neural network using a plurality of medical images corresponding to a plurality of pieces of imaging information including information regarding a fluorescence substance provided in a radiation detection apparatus, learning result data corresponding to a radiation detection apparatus used for imaging and selected based on information regarding a fluorescence substance included in imaging information corresponding to the radiation detection apparatus used for the image (Zhao ¶0102: “The visualization and/or quantitative data analysis performed on the 2D x-ray imaging apparatuses disclosed herein can be done by running algorithms developed for diagnosis and identification and characterization using artificial intelligence, deep machine learning, artificial neural network, convolution neural network, and/or deep neural network”; Zhao ¶0193: “The apparatuses disclosed herein can include a storage and/or a database”; Zhao ¶0206: “The hardware and software described herein may be used or adapted for measurements in one or more of the following modalities and methods … x-ray fluorescence … and/or all x-ray detectable contrast agents and energy apparatus induced measurement and quantification”; Zhao ¶0210: “Software for data output used for … coherent and in coherent and partially coherent or incoherent x-ray imaging, and/or other quantitative analysis task including determining atomic number, characteristics of component, or subjects, identification of a subject, or component in the region of interest, for data output used for deep machine learning”; Zhao ¶¶0276: “The x-ray system disclosed herein can perform spatially resolved x-ray fluorescence analysis. An x-ray excitation beam can be directed upon a subject to generate fluorescent x-rays, wherein the x-ray excitation beam includes a planar array of x-ray micro-beams. The individual x-ray micro-beams can each have a diameter smaller than low double-digit microns. The fluorescent x-rays can be imaged with an x-ray imaging system that includes an x-ray imaging optical system and an energy resolving and spatially resolving x-ray detector. The x-ray imaging optical system can collect fluorescent x-rays generated when a subject is illuminated by the x-ray excitation beam positioned such that its subject plane is coplanar with the plane of the planar array of microbeams within the depth of field of the x-ray imaging optical system. The energy dispersive and spatially resolving x-ray detector can be positioned at the image plane of the x-ray optical imaging system”); and
a processing unit configured to perform noise reduction processing on a medical image obtained by the radiation detection apparatus used for the imaging by inputting the obtained medical image to a neural network using the learning result data read out by the readout unit (Zhao Abstract: “the improve x-ray apparatus can reduce scatter from x-ray images acquired by two-dimensional detectors”; Zhao ¶0017: “The processor can be configured to remove scatter in the x-ray measurements”; Zhao ¶0108: “Scatter Removal with Calibration”; Zhao ¶0137: “Multiple Energy Scatter Removal”; Zhao ¶0248: “Enhanced signal-to-noise ratios (SNR) may be achieved when probing a subject if the signals of multiple x-ray beams are measured recorded individually”; Zhao ¶0424: “The modification and/or optimization can ensure each projected 2D image measured at varying x-ray emitting positions from the source is comparable in terms of image quality, accuracy, sensitivity, and signal to noise ratio”).
Regarding claim 4, Zhao teaches the medical image processing apparatus according to claim 1, wherein the imaging information includes at least one piece of information of an image resolution and a pixel pitch of the radiation detection apparatus (Note that only one of the alternative limitations is required by the claim language. Zhao ¶0017: “A distance between adjacent x-ray emitting position can be a dimension of the resolution needed in a third axis … A distance between adjacent x-ray emitting positions can be 1 pixel pitch, integer multiples of a pixel pitch, or less than 1 pixel pitch”; Zhao ¶0110: “A front high-resolution primary x-ray image can be determined”; Zhao ¶0304: “The pitch of the detector can be matched to the pitch of the multiple x-ray sources”; Zhao ¶0321: “The mechanism 200 can provide this motion in increments of integer multiples of pixel pitch (the distance between adjacent detector cells)”).
Regarding claim 5, Zhao teaches the medical image processing apparatus according to claim 1,
wherein the imaging information further includes information regarding a processing target image (Zhao ¶0030: “a contrast agent complex configured to label an imaging target”; Zhao ¶0172: “the calibration step may be simplified by including into the reference database or library the preexisting data as described”; Zhao ¶0184: “a larger segment of regions may be blocked by beam absorption particles in order to limit radiation in the region of interest for measurements where only an image of a small region of a target or a component contained in a region of interest may be needed for the particular application”; Zhao ¶0502: “A ‘component’ (see component 120 in FIG. 16) is the region within the target that may be identified by x-ray imaging and/or quantitative measurement by a set of defined quantifiable parameters and/or may be differentiated from a different component within the target based on this set of quantifiable parameters. A target may include one or more components”), and
wherein the information regarding the processing target image includes at least one piece of information of a size of an image, a use application of the image, and information regarding preprocessing (Note that only one of the alternative limitations is required by the claim language. Zhao ¶Abstract: “provide 3D imaging for medical and/or industrial applications”; Zhao ¶0144: “This method may be sufficient for most applications. In a tracking or surgical guidance application such as disclosed herein, when two or more images are taken … may provide sufficient information to retroactively fill in the image gap by extracting measurements in the region (i, j) from one or more different measurements in the sequence”; Zhao ¶0184: “a larger segment of regions may be blocked by beam absorption particles in order to limit radiation in the region of interest for measurements where only an image of a small region of a target or a component contained in a region of interest may be needed for the particular application. Such configuration may be used in 3D imaging or tomography applications”; Zhao ¶0370: “Each location of the x-ray source can produce an image of size m×n and there can be p layers”; Zhao ¶0487: “The method can also include analysis of the relative compositions, densities, and/or image information of regions of interest in an individual component as well as that of components relative to other components of a subject in position, density, and/or image (including morphology as well as dimensions of image, such as a tumor size or disease tissue size)”).
Regarding claim 6, Zhao teaches the medical image processing apparatus according to claim 1, wherein the readout unit reads out learning result data from the storage unit that is externally provided (Zhao ¶0196: “One or more of the following methods may be used, such as random number mixing with a secondary key; a second access apparatus, remote and/or on-site”; Zhao ¶0711: “the computer system may be a cloud-based computing system whose processing resources are shared by multiple distinct business entities or other users”).
Regarding claim 7, Zhao teaches the medical image processing apparatus according to claim 1, further comprising a selection unit configured to cause a user to select one of a plurality of pieces of imaging information that includes the information regarding a fluorescence substance included in the imaging information corresponding to the radiation detection apparatus used for imaging (Zhao ¶0088: “The user selects or the digital program selects the region by one or more criteria”; Zhao ¶0253: “a user select or a digital program 13 in the processor selects 4s based on one or more criteria as the result of full field imaging and/or spectral imaging in 2D or multiple dimension or 3D dimensions”; Zhao ¶0271: “When x-ray absorptiometry is performed with a polychromatic source and the selected region for detailed analysis in the hybrid system in region of interest is determined from results of the full field x-ray imaging, or user or computer input based on one or a set of criteria”; Zhao ¶0572: “live measurements of region of interest by x-ray can be made along with any other imaging methods. At Step 3, colocation can be performed based on co-location of dyes, or first, secondary, tertiary or more order dyes, each for a different modality, or common dyes, for two or more modalities”; Zhao ¶0603: “A digital programmer may select the region based on one or more criteria based on the imaging results of a full field x-ray image. Or a user may select region”),
wherein the readout unit obtains the imaging information selected by the input unit is obtained (Zhao ¶0088, ¶0253, ¶0271, ¶0572, ¶0603 discussed above; Zhao Fig. 25).
Regarding claim 8, Zhao teaches the medical image processing apparatus according to claim 6, further comprising a generation unit configured to generate correspondence information that indicates correspondence between imaging information and learning result data to be read out for each of the plurality of pieces of imaging information (Zhao ¶0468: “a data file including compressed images or image set of one or more studies may be organized into two subsets of data files, one including a small file with one or more preselected images or measurements or selected data or report relevant to the measurement, and the second including the complete data set. As the data file is transferred via a network to be viewed, the first subset is transferred first and may be available to be viewed or previewed immediately by the viewer prior to the complete data set transfer, while the second subset or the rest of the complete data set is transferred as the next step. Such preview data may be available for viewing as a stored file, linked to the complete file”), and
wherein the readout unit reads out the learning result data corresponding to the selected the radiation detection apparatus used for the imaging from the storage unit with reference to the correspondence information (Zhao ¶0468 discussed above).
Regarding claim 11, Zhao teaches a radiation imaging system in which a radiation detection apparatus used for imaging and the medical image processing apparatus according to claim 1 are communicably connected to each other (Zhao ¶Fig. 16: 100, 102; Zhao ¶0196: “Alternatively or additionally, an apparatus can be used remotely if there is Internet or Intranet communication to the database”; Zhao ¶0711: “The computer system may, in some cases, include multiple distinct computers or computing apparatuses (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the described functions”).
Regarding claim 12, Zhao further teaches that the apparatus performs a method for processing a medical image, the method comprising the steps described in claim 1 (Zhao Title & Abstract: “An x-ray apparatus and method”). Therefore, claim 12 is rejected using the same rationale as applied to claim 1 discussed above.
Regarding claim 13, Zhao teaches a non-transitory computer-readable storage medium storing a program for causing a computer to execute the method according to claim 12 (Zhao ¶0711: “Each such computing apparatus typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or apparatus (e.g., solid state storage apparatuses, disk drives, etc.)”).
Regarding claim 14, Zhao further teaches that the apparatus reads out information regarding a second fluorescence substance information, a second learning data, and second radiation detection apparatus in addition to the first fluorescence substance information, first learning data, and first radiation detection apparatus (Zhao ¶0572: “At Step 3, colocation can be performed based on co-location of dyes, or first, secondary, tertiary or more order dyes, each for a different modality, or common dyes, for two or more modalities”; Zhao ¶0638: “detectable by x-ray imaging and/or x-ray microscopy, and/or x-ray spectral measurements or x-ray spectral absorptiometry, and can include, for example, gold, silver, iodine, calcium, potassium, fluorescent dye, or a contrast agent recognizable by ultrasound or MRI or PET or CT or Optical Imaging or photoacoustic or ultrasound modalities”). Therefore, claim 14 is rejected using the same rationale as applied to claim 1 discussed above.
Claim Rejections - 35 USC § 103
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ying Zhao (US 2021/0244374 A1), in view of Shah et al. (US 2020/0285897 A1), hereinafter referred to as Zhao and Shah, respectively.
Regarding claim 9, Zhao teaches the medical image processing apparatus according to claim 1, wherein the readout unit reads out the learning result data corresponding to the radiation detection apparatus used for the imaging selected based on the information regarding the fluorescence substance included in the imaging information corresponding to the radiation detection apparatus used for the imaging.
However, Zhao does not appear to explicitly teach that storage unit further stores information regarding a structure of a neural network and the readout unit reads out learning results data based on information regarding the structure of the neural network.
Pertaining to the same field of endeavor, Shah teaches that storage unit further stores information regarding a structure of a neural network (Shah ¶0085: “imaging modality A may comprise an imaging technology with a first tissue stain or dye; and imaging modality B may comprise the same imaging technology with a second tissue stain or dye”; Shah ¶0088: “trained classifier 212, 312, 412 comprises a neural network, such as a convolutional neural network (CNN)”; Shah Figs. 5-7 & ¶0101: “The layers of this modified VGG16 are depicted in FIGS. 5, 6 and 7. Alternatively, neural network 502 may have an architecture that is different than a modified VGG16”; Shah ¶0131: “the convolutional neural network architecture takes as input an n×n pixel patch … the network architecture comprises the first through thirteenth layers of VGG16 followed by a smaller fully-connected layer of 256 nodes and a final softmax function”; Shah ¶0196: “one or more computers … are programmed or specially adapted to perform one or more of the following tasks … (6) to train a classifier (e.g., a neural network) … (7) to employ a trained classifier, which has been trained with union-labeled data, to perform classification … (16) to process data, to perform computations, and to execute any algorithm or software; and (17) to control the read or write of data to and from memory devices (tasks 1-17 of this sentence referred to herein as the “Computer Tasks”)”); and
the readout unit reads out learning results data based on information regarding the structure of the neural network (Shah ¶0196 discussed above).
Zhao and Shah are considered to be analogous art because they are directed to image processing for detecting a plurality of fluorophores or dyes. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus and method for x-ray imaging (as taught by Zhao) to store and read-out information regarding neural network structure (as taught by Shah) because the neural network may have a different architecture based on what is being detected, e.g., binary, two or more classes, simultaneous multiple-class detection, etc. (Shah Figs. 5-7 & ¶0101).
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Soo Shin/Primary Examiner, Art Unit 2667
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