CTFR 17/333,410 CTFR 93337 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-2, 4-8, and 10-15 are pending. Claim 12 is withdrawn from prosecution. Claims 1-2, 4-8, and 10-11 and 13-15 are rejected. Response to Arguments 07-37 AIA Applicant's arguments filed 03/24/2026 have been fully considered but they are not persuasive. Examiner notes that while the claims have been amended to provide more clarity with respect to the claimed invention, the subject matter remains largely similar to previously presented claims and hence the office’s stance on prior art Kato, et al., US 20090030324 A1 and Makihira, T., US 20190130170 A1 with respect to the claims is that the combination teaches all the limitations of the claims. Applicant remarks on pages 14-15 that the prior art of record pertains to fluctuation evaluation of hemangiomas. While Applicant is correct that the prior art do not track fluctuations in positions of hemangiomas, Kato tracks differences in positions of a blood vessel wall. Such teachings does not preclude teaching of the claimed invention direct to tracking similarities between spatial positions in temporally varying frames of ultrasound data. Applicant also argues on page 15 that the prior art does not generate correlation curves. However, figs. 9 and 11 of Kato depict tracking the difference values dn between consecutive frames. Applicant also the prior fail to teach the two-stage correlation analysis as claimed. However, Kato teaches determining differences between the frames and then performs second stage correlation calculations such as variance and standard deviations of the differences. Applicant also argues on page 15 that the prior art also fail to disclose a color map that shows distinctions in the fluctuations. However, at least [0103] discloses a two-dimensional color image. Therefore, the claims stand rejected. Withdrawn Objections The objections made to claims 4, 6, 8, and 13-14 have been withdrawn pursuant of Applicant’s amendments filed 10/08/2025. Withdrawn Rejections - 35 USC § 112 Pursuant of Applicant’s amendments filed 03/24/2026, the rejection of claims 1-2, 4-8, 10-11, and 13-15 under 35 U.S.C. 112(b) have been withdrawn. Withdrawn Objections - Specification Pursuant of Applicant’s responses filed 03/24/2026, the objections made to the specifications have been withdrawn. Withdrawn Claim Objections Pursuant of Applicant’s responses filed 03/24/2026, the objection made to claim 1 has been withdrawn . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 1, 4, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kato, et al., US 20090030324 A1 in view Makihira, T., US 20190130170 A1 . Regarding claim 1, Kato teaches a medical image diagnosis apparatus ( abstract ), comprising: processing circuitry ( computing section 19 of figs 2-3 and [0086] ) configured to: process received ultrasound echo signals reflected from a subject and received by an ultrasound probe ( [0016] ), and sequentially acquire, in a time direction, ultrasound images representing intensities of the received ultrasound signals as brightness for a plurality of frames, each frame including a plurality of spatial positions ( [0016], [0102] ); for each of the plurality of spatial positions included in the ultrasound images, calculate a first similarity value representing a temporal change of a correlation between corresponding spatial positions in different ultrasound images arranged in the time direction ( “The difference calculating section 33 calculates differences between the shape measured values or property measured values of two spatial distribution frames that are selected from multiple spatial distribution frames every cardiac cycle” [0106], and “every time a new spatial distribution frame Fn is obtained, the difference dn is calculated between the new frame En and the previous spatial distribution frame Fn-1. The value of the difference dn is updated and presented along with the spatial distribution frame Fn on the display section 21 every cardiac cycle” [0107]. The value “dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves ); for each of the plurality of spatial positions included in the ultrasound images, calculate a second similarity value representing a similarity between (i) the first similarity value calculated for a first spatial position and (ii) the first similarity value calculated for a second spatial position that is spatially different from the first spatial position ( “the characteristic quantity Dn of differences does not have to be calculated as the average of multiple differences dn but may also be calculated as the sum, the variance, the standard deviation, the RMS or the difference between the maximum and minimum values of the differences” [0120]. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves. This difference shows the degree of variation of the data that forms a frame, meaning the difference value “Dn” represents difference/similarity between two consecutive frames as measured at a specified location. Also note that the frames are temporally aligned as shown in fig. 7 ) ; generate a color image ( [0103] ) in which a color corresponding to the second similarity value calculated for each of the plurality of spatial positions is assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image (“ the preferred embodiment described above is an ultrasonic diagnostic apparatus that figures out the two-dimensional distribution of shape property values or property measured values and presents it as a frame every cardiac cycle” [0123], Dn being representing the “distribution of shape property values or property measured values ” The value “Dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “Dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “Dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames ). Kato does not teach calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image. However, within the same field of endeavor, Makihira teaches an information acquisition unit configured to obtain information about a change in a motion contract value of at least part of substantially the same region by using a plurality of pieces of motion contrast information at different times ( see abstract ). Makihira further teaches a method for generating an OCTA image (motion contrast (MC) image) from a single data set obtained by the foregoing flow in [0029] and fig. 4, where the method includes calculating image similarity among m frames of similar tomographic images at a position according to step S407 of fig. 4 and [0031] , to select q number of frames among the m frames for registration. [0032] then states that “To select the frame serving as a template, correlation may be calculated between all possible combinations of the frames . Then, the sum of the correlation coefficients may be determined frame by frame , and the frame having the maximum sum may be selected as the template. Next, each frame is collated with the template to determine a position shift amount (δx, δy, δθ) . Specifically, normalized cross-correlation (NCC), which is an index indicating similarity, is determined while changing the position and angle of the template . A difference in image position when NCC becomes maximum is determined as the position shift amount”. [0034] then states that “the signal processing unit 053 calculates variance values for each pixel at the same position in the q frames of luminance images that are selected in step S408 and registered in step S409, and uses the variance values as the MC values ” for various y.sub.k positions . [0037] further states that “the signal processing unit 053 determines whether the calculation of image similarity , image selection, registration, the calculation of average luminance, the calculation of MC values , and the threshold processing has been performed on all the n y-positions …When step S413 ends, there is generated MC value three-dimensional volume data which is a set of an average luminance image of the tomographic images at all the y-positions and a plurality of adjacent pieces of MC information at n y-positions ”. That is, variance MC values are obtained for various y.sub.k positions and hence teaching determining or tracking the change of the first similarity values (similarity among the frames) over time at different positions (the various y.sub.k positions); generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to each position and display the generated image ( [0050] states with respect to display of the resultant image that “a signal processing unit 053 which is an example of an average image generation unit generates an average image of a plurality of MC en face images. The image 800 is a color image in which the maximum values I.sub.max of MC values described in FIG. 9B are assigned to luminance information of respective pixels, and standard deviations (I.sub.σ) of the MC values of the respective pixels, which are MC value change information, are assigned to hue”. The MC values are shown by different hues ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato for calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image, as taught by Makihira, to easily determine regional changes in the frames ( [0050] ), and hence improving the accuracy of the generated image ( [0027] ), with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . Regarding claim 4, Kato in view of Makihira teaches all the limitations of claim 1. Kato further teaches wherein the processing circuitry is further configured to perform control wherein the given area is changeable according to a subject to be analyzed (“the ultrasonic probe 13 for use in this preferred embodiment has an array of ultrasonic vibrators, and therefore, can evaluate the elastic property at every point within an arbitrary area of the given cross-sectional plane. In this case, the operator can define an arbitrary area by specifying an ROI (=region of interest).” [0101], meaning the area can be arbitrarily chosen and hence changeable ). Regarding claim 5, Kato in view of Makihira teaches all the limitations of claim 1. Kato further teaches wherein the processing circuitry ( computing section 19 of figs 2-3 and [0086] ) is further configured to set an analysis region on which the first similarity is calculated and calculate the first similarity value with respect to a particular position that is contained in the analysis region ( “the ultrasonic probe 13 for use in this preferred embodiment has an array of ultrasonic vibrators, and therefore, can evaluate the elastic property at every point within an arbitrary area of the given cross-sectional plane. In this case, the operator can define an arbitrary area by specifying an ROI (=region of interest).” [0101]. Here, the difference value “Dn” is calculated for every point within an arbitrary area set as the analysis region. Since the difference value “Dn” represents differences/similarity of shape or property measured values of the two spatially distributed frames, such a difference value is equivalent to a similarity value between the frames ). Regarding claim 6, Kato in view of Makihira teaches all the limitations of claim 5. Kato further teaches wherein the processing circuitry ( computing section 19 of figs 2-3 and [0086] ) is further configured to perform control wherein the analysis region is changeable according to a subject to be analyzed (“the ultrasonic probe 13 for use in this preferred embodiment has an array of ultrasonic vibrators, and therefore, can evaluate the elastic property at every point within an arbitrary area of the given cross-sectional plane. In this case, the operator can define an arbitrary area by specifying an ROI (=region of interest).” [0101], meaning the area can be arbitrarily chosen and hence changeable based on the subject to be analyzed ). Regarding claim 11, Kato in view of Makihira teaches all the limitations of claim 1. Kato further teaches wherein the processing circuitry ( computing section 19 of figs 2-3 and [0086] ) is further configured to calculate the first similarity value with respect to a new frame every time the signals are acquired, and calculate the second similarity value based on the first similarity values that are calculated with respect to a given number of the plurality of frames from the new frame (“every time a new spatial distribution frame Fn is obtained, the difference dn is calculated between the new frame En and the previous spatial distribution frame Fn-1. The value of the difference dn is updated and presented along with the spatial distribution frame Fn on the display section 21 every cardiac cycle” [0107]. While “dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames ). Regarding claim 13, Kato in view of Makihira teaches all the limitations of claim 11. Kato further teaches wherein the processing circuitry ( computing section 19 of figs 2-3 and [0086] ) is further configured to perform control wherein the given number of frames is changeable according to a subject to be analyzed ([0118] indicates use of four consecutive data frames while [0106] discloses using two frames and hence teaches usage of different numbers of frames, meaning the number of frames is changeable. That is, different numbers of frames are used in the performing of the control and hence the number of frames is changeable ). Regarding claim 14, Kato teaches a medical image diagnosis apparatus ( abstract ), comprising: processing circuitry ( computing section 19 of figs 2-3 and [0086] ) configured to: process received ultrasound echo signals reflected from a subject and received by an ultrasound probe ( [0016] ), and sequentially acquire, in a time direction, ultrasound images representing intensities of the received ultrasound signals as brightness for a plurality of frames, each frame including a plurality of spatial positions ( [0016], [0102] ); for each of the plurality of spatial positions included in the ultrasound images, calculate a first similarity value representing a temporal change of a correlation between corresponding spatial positions in different ultrasound images arranged in the time direction ( “The difference calculating section 33 calculates differences between the shape measured values or property measured values of two spatial distribution frames that are selected from multiple spatial distribution frames every cardiac cycle” [0106], and “every time a new spatial distribution frame Fn is obtained, the difference dn is calculated between the new frame En and the previous spatial distribution frame Fn-1. The value of the difference dn is updated and presented along with the spatial distribution frame Fn on the display section 21 every cardiac cycle” [0107]. The value “dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves ); for each of the plurality of spatial positions included in the ultrasound images, calculate a second similarity value representing a similarity between (i) the first similarity value calculated for a first spatial position and (ii) the first similarity value calculated for a second spatial position that is spatially different from the first spatial position ( “the characteristic quantity Dn of differences does not have to be calculated as the average of multiple differences dn but may also be calculated as the sum, the variance, the standard deviation, the RMS or the difference between the maximum and minimum values of the differences” [0120]. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves. This difference shows the degree of variation of the data that forms a frame, meaning the difference value “Dn” represents difference/similarity between two consecutive frames as measured at a specified location. Also note that the frames are temporally aligned as shown in fig. 7 ) ; generate a color image ( [0103] ) in which a color corresponding to the second similarity value calculated for each of the plurality of spatial positions is assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image (“ the preferred embodiment described above is an ultrasonic diagnostic apparatus that figures out the two-dimensional distribution of shape property values or property measured values and presents it as a frame every cardiac cycle” [0123], Dn being representing the “distribution of shape property values or property measured values ” The value “Dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “Dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “Dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames ). Kato does not teach calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image. However, within the same field of endeavor, Makihira teaches an information acquisition unit configured to obtain information about a change in a motion contract value of at least part of substantially the same region by using a plurality of pieces of motion contrast information at different times ( see abstract ). Makihira further teaches a method for generating an OCTA image (motion contrast (MC) image) from a single data set obtained by the foregoing flow in [0029] and fig. 4, where the method includes calculating image similarity among m frames of similar tomographic images at a position according to step S407 of fig. 4 and [0031] , to select q number of frames among the m frames for registration. [0032] then states that “To select the frame serving as a template, correlation may be calculated between all possible combinations of the frames . Then, the sum of the correlation coefficients may be determined frame by frame , and the frame having the maximum sum may be selected as the template. Next, each frame is collated with the template to determine a position shift amount (δx, δy, δθ) . Specifically, normalized cross-correlation (NCC), which is an index indicating similarity, is determined while changing the position and angle of the template . A difference in image position when NCC becomes maximum is determined as the position shift amount”. [0034] then states that “the signal processing unit 053 calculates variance values for each pixel at the same position in the q frames of luminance images that are selected in step S408 and registered in step S409, and uses the variance values as the MC values ” for various y.sub.k positions . [0037] further states that “the signal processing unit 053 determines whether the calculation of image similarity , image selection, registration, the calculation of average luminance, the calculation of MC values , and the threshold processing has been performed on all the n y-positions …When step S413 ends, there is generated MC value three-dimensional volume data which is a set of an average luminance image of the tomographic images at all the y-positions and a plurality of adjacent pieces of MC information at n y-positions ”. That is, variance MC values are obtained for various y.sub.k positions and hence teaching determining or tracking the change of the first similarity values (similarity among the frames) over time at different positions (the various y.sub.k positions); generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to each position and display the generated image ( [0050] states with respect to display of the resultant image that “a signal processing unit 053 which is an example of an average image generation unit generates an average image of a plurality of MC en face images. The image 800 is a color image in which the maximum values I.sub.max of MC values described in FIG. 9B are assigned to luminance information of respective pixels, and standard deviations (I.sub.σ) of the MC values of the respective pixels, which are MC value change information, are assigned to hue”. The MC values are shown by different hues ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato for calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image, as taught by Makihira, to easily determine regional changes in the frames ( [0050] ), and hence improving the accuracy of the generated image ( [0027] ), with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . Regarding claim 15, Kato teaches a medical image processing method ( see fig. 8 and [0125] which discloses “a method of controlling the presentation of spatial distribution frames based on the result of comparison between the differences dn calculated by the difference calculating section 33 and the threshold value ds of differences that has been set in advance by the operator of the ultrasonic diagnostic apparatus 11” ), comprising: processing received ultrasound echo signals reflected from a subject and received by an ultrasound probe ( [0016] ), and sequentially acquire, in a time direction, ultrasound images representing intensities of the received ultrasound signals as brightness for a plurality of frames, each frame including a plurality of spatial positions ( [0016], [0102] ); for each of the plurality of spatial positions included in the ultrasound images, calculating a first similarity value representing a temporal change of a correlation between corresponding spatial positions in different ultrasound images arranged in the time direction ( “The difference calculating section 33 calculates differences between the shape measured values or property measured values of two spatial distribution frames that are selected from multiple spatial distribution frames every cardiac cycle” [0106], and “every time a new spatial distribution frame Fn is obtained, the difference dn is calculated between the new frame En and the previous spatial distribution frame Fn-1. The value of the difference dn is updated and presented along with the spatial distribution frame Fn on the display section 21 every cardiac cycle” [0107]. The value “dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves ); for each of the plurality of spatial positions included in the ultrasound images, calculating a second similarity value representing a similarity between (i) the first similarity value calculated for a first spatial position and (ii) the first similarity value calculated for a second spatial position that is spatially different from the first spatial position ( “the characteristic quantity Dn of differences does not have to be calculated as the average of multiple differences dn but may also be calculated as the sum, the variance, the standard deviation, the RMS or the difference between the maximum and minimum values of the differences” [0120]. [0050] also indicates that a frame-by-frame difference is calculated between the greatest thickness differences, strains, or elastic properties, which are figured out based on the location information or motion information of an arbitrary area of a vital tissue using ultrasonic waves. This difference shows the degree of variation of the data that forms a frame, meaning the difference value “Dn” represents difference/similarity between two consecutive frames as measured at a specified location. Also note that the frames are temporally aligned as shown in fig. 7 ) ; generating a color image ( [0103] ) in which a color corresponding to the second similarity value calculated for each of the plurality of spatial positions is assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image (“ the preferred embodiment described above is an ultrasonic diagnostic apparatus that figures out the two-dimensional distribution of shape property values or property measured values and presents it as a frame every cardiac cycle” [0123], Dn being representing the “distribution of shape property values or property measured values ” The value “Dn” represents difference, in the case of the instant application, how similar the two consecutive frames are and hence “Dn” represents a similarity value representing differences/similarity of shape or property measured values of the two spatially distributed frames. In other words, while “Dn” is indicated as a difference value between the frame, such a difference value is equivalent to a similarity value between the frames ). Kato does not teach calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image. However, within the same field of endeavor, Makihira teaches an information acquisition unit configured to obtain information about a change in a motion contract value of at least part of substantially the same region by using a plurality of pieces of motion contrast information at different times ( see abstract ). Makihira further teaches a method for generating an OCTA image (motion contrast (MC) image) from a single data set obtained by the foregoing flow in [0029] and fig. 4, where the method includes calculating image similarity among m frames of similar tomographic images at a position according to step S407 of fig. 4 and [0031] , to select q number of frames among the m frames for registration. [0032] then states that “To select the frame serving as a template, correlation may be calculated between all possible combinations of the frames . Then, the sum of the correlation coefficients may be determined frame by frame , and the frame having the maximum sum may be selected as the template. Next, each frame is collated with the template to determine a position shift amount (δx, δy, δθ) . Specifically, normalized cross-correlation (NCC), which is an index indicating similarity, is determined while changing the position and angle of the template . A difference in image position when NCC becomes maximum is determined as the position shift amount”. [0034] then states that “the signal processing unit 053 calculates variance values for each pixel at the same position in the q frames of luminance images that are selected in step S408 and registered in step S409, and uses the variance values as the MC values ” for various y.sub.k positions . [0037] further states that “the signal processing unit 053 determines whether the calculation of image similarity , image selection, registration, the calculation of average luminance, the calculation of MC values , and the threshold processing has been performed on all the n y-positions …When step S413 ends, there is generated MC value three-dimensional volume data which is a set of an average luminance image of the tomographic images at all the y-positions and a plurality of adjacent pieces of MC information at n y-positions ”. That is, variance MC values are obtained for various y.sub.k positions and hence teaching determining or tracking the change of the first similarity values (similarity among the frames) over time at different positions (the various y.sub.k positions); generating a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to each position and display the generated image ( [0050] states with respect to display of the resultant image that “a signal processing unit 053 which is an example of an average image generation unit generates an average image of a plurality of MC en face images. The image 800 is a color image in which the maximum values I.sub.max of MC values described in FIG. 9B are assigned to luminance information of respective pixels, and standard deviations (I.sub.σ) of the MC values of the respective pixels, which are MC value change information, are assigned to hue”. The MC values are shown by different hues ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato for calculating similarity values for a first spatial position and a second spatial position, and generate a color image in which colors corresponding to the second similarity value calculated for each of the plurality of positions are assigned to the respective spatial position such that fluctuation and disturbance are distinguishable in the color image, as taught by Makihira, to easily determine regional changes in the frames ( [0050] ), and hence improving the accuracy of the generated image ( [0027] ), with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . 07-21-aia AIA Claim s 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kato in view of Makihira, as applied to claim 1, and further in view of Kim, et al., US 20070053566 A1 . Regarding claim 2, Kato in view of Makihira teaches all the limitations of claim 1. The embodiment of Kato relied upon above in view of Makihira does not teach wherein the processing circuitry is further configured to generate a correlation curve representing a change of the first similarity value in a frame direction as the change of the first similarity value over time. However, in a different embodiment, Kato discloses in a second embodiment, an ultrasonic diagnostic apparatus that presents spatial distribution frames using either the differences dn or the characteristic quantity Dn of the differences as already described in detail for the first preferred embodiment and a method for controlling such an apparatus will be described as a second preferred embodiment of the present invention [0124] , wherein the processing circuitry is further configured to generate a correlation curve representing a change of the first similarity value in a frame direction as the change of the first similarity value over time (“ a graph showing the differences dn that were calculated by the ultrasonic diagnostic apparatus of this preferred embodiment every cardiac cycle [0133]. The graph represents a correlation curve of the difference values ). PNG media_image1.png 266 516 media_image1.png Greyscale Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure the first embodiment of Kato, as modified by Makihira, wherein the processing circuitry is further configured to generate a correlation curve representing a change of the first similarity value in a frame direction as the change of the first similarity value over time, as taught by the second embodiment, as such modification would allow measurements that have a certain degree of stability and can make an even more accurate diagnosis [0134] . Kato does not teach calculate a similarity to the correlation curve at the two of the plurality of positions as the second similarity value. However, within the same field of endeavor, Kim teaches an apparatus for processing an ultrasound image of a target object including a periodically moving object, including: an ROI setting unit for setting regions of interest (ROIs) to each of frames constituting ultrasound volume data acquired from a target object; a VOI setting unit for selecting a predetermined number of first reference frames from the ultrasound volume data and setting a predetermined number of volumes of interest (VOIs) by combining ROIs of the first reference frames with ROIs of frames adjacent to the reference frames; a motion compensating unit for processing the VOIs to compensate a motion of the target object; a correlation coefficient curve calculating unit for calculating a correlation coefficient curve for a predetermined interval at each VOI ( see abstract ), calculate a similarity to the correlation curve at the two of the plurality of positions as the second similarity value, including selecting volumes of interest (VOIs) among selected frames of ultrasound data ([0031]), calculating correlation coefficient curves for the selected VOIs ( [0032] and depicted in fig. 6 ), sets a moving period of the moving object based on the correlation coefficient curve in the volume data ( [0033]-[0034] and fig. 8 ), and calculation a ratio of moving periods to perform a linear interpolation of the frames ( [0036] ), and reconstructing the interpolated volume ([0037]). These steps are performed for the various VOIs. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato, as modified by Makihira, to calculate a similarity to the correlation curve at the two of the plurality of positions as the second similarity value, as taught by Kim, to improve the accuracy of the image measurements ([0004]), with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . Regarding claim 10, Kato in view of Makihira teaches all the limitations of claim 1. Kato fails to teach wherein the processing circuitry is further configured to: calculate the second similarity value for each of the plurality of positions and the plurality of frames, and generate an image representing a distribution of the second similarity values for each of the plurality of frames, and output the generated images. However, Makihara further teaches wherein the processing circuitry ( control unit 054 of [0024] ) is further configured to: calculate the second similarity value for each of the plurality of positions and the plurality of frames, and generate an image representing a distribution of the second similarity values for each of the plurality of frames, and output the generated images. Makihara further teaches a method for generating an OCTA image (motion contrast (MC) image) from a single data set obtained by the foregoing flow in [0029] and fig. 4, where the method includes calculating image similarity among m frames of similar tomographic images at a position according to step S407 of fig. 4 and [0031] , to select q number of frames among the m frames for registration. [0032] then states that “To select the frame serving as a template, correlation may be calculated between all possible combinations of the frames . Then, the sum of the correlation coefficients may be determined frame by frame , and the frame having the maximum sum may be selected as the template . Next, each frame is collated with the template to determine a position shift amount (δx, δy, δθ) . Specifically, normalized cross-correlation (NCC), which is an index indicating similarity, is determined while changing the position and angle of the template . A difference in image position when NCC becomes maximum is determined as the position shift amount”. [0034] then states that “the signal processing unit 053 calculates variance values for each pixel at the same position in the q frames of luminance images that are selected in step S408 and registered in step S409, and uses the variance values as the MC values ” for various y.sub.k positions . [0037] further states that “the signal processing unit 053 determines whether the calculation of image similarity , image selection, registration, the calculation of average luminance, the calculation of MC values , and the threshold processing has been performed on all the n y-positions …When step S413 ends, there is generated MC value three-dimensional volume data which is a set of an average luminance image of the tomographic images at all the y-positions and a plurality of adjacent pieces of MC information at n y-positions ”. That is, variance MC values are obtained for various y.sub.k positions and hence teaching calculating the second similarity value (variance MC values) for each of the plurality of positions and the plurality of frames; The color image 800 in [0050] is taught to be display a standard deviation of the MC values of the respective pixels and hence teaching the generation of the distribution image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato wherein the processing circuitry is further configured to: calculate the second similarity value for each of the plurality of positions and the plurality of frames, and generate an image representing a distribution of the second similarity values for each of the plurality of frames, and output the generated images, as taught by Makihara, to easily determine regional changes in the frames ( [0050] ), and hence improving the accuracy of the generated image ( [0027] ), with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . 07-21-aia AIA Claim s 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kato in view of Makihira, as applied to claim 1, and further in view of Honjo, et al., US 20190000414 A1 . Regarding claim 7, Kato in view of Makihira teaches all the limitations of claim 1. Kato in view of Makihira fails to teach wherein the processing circuitry is further configured to calculate the first similarity value by setting a comparison region corresponding to a plurality of pixels in a corresponding position in each of the plurality of frames, and comparing the pixels in the comparison region between frames. However, within the same field of endeavor, Honjo teaches an image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to perform a speckle noise reducing process on each of a plurality of images that were acquired by using an ultrasound wave and are in a time series.” [0021], wherein the processing circuitry is further configured to calculate the first similarity value by setting a comparison region corresponding to a plurality of pixels in a corresponding position in each of the plurality of frames and comparing the pixels in the comparison region between frames ( “the index image generating function 162 generates a parametric image of a region R2 corresponding to the region of interest R0 illustrated in FIG. 2. The parametric image is obtained by assigning a color (degrees of hue, lightness, chroma, and the like) corresponding to the magnitude of the index value of each of the pixels contained in the region of interest R0 to the pertinent position in the region R2” [0086]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato, as modified by Makihira, wherein the processing circuitry is further configured to calculate the first similarity value by setting a comparison region corresponding to a plurality of pixels in a corresponding position in each of the plurality of frames, and comparing the pixels in the comparison region between frames, as taught by Honjo, as such modification would improve the accuracy of discriminating the region or feature of interest within the plurality of frames ([0097]-[0098]) , with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . Regarding claim 8, Kato in view of Makihira and Honjo teaches all the limitations of claim 7. Kato in view of Makihira fails to teach wherein the processing circuitry is further configured to perform control wherein the comparison region is changeable. However, Honjo further teaches wherein the processing circuitry is further configured to perform control wherein the comparison region is changeable “For example, the read N pieces of B-mode image data include movements between images (positional shifts) caused by the operator not holding the imaging device steadily or by body movements (e.g., heartbeats). For this reason, the image processing circuitry 130 identifies the positional shifts in the images by performing a tracking process on the N pieces of B-mode image data. After that, the image processing circuitry 130 generates a series of pieces of B-mode image data containing no positional shifts in the time direction, by correcting the identified positional shifts in the images.” [0058]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Kato, as modified by Makihira, wherein the processing circuitry is further configured to perform control wherein the comparison region is changeable, as taught by Honjo, as such modification would improve the accuracy of discriminating the region or feature of interest within the plurality of frames ([0097]-[0098]) , with a reasonable expectation of success, as Kato is also concerned with improving image measurement accuracy according to [0010]-[0011] . Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 Farouk A Bruce whose telephone number is (408)918-7603. The examiner can normally be reached Mon-Fri 8-5pm PST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Koharski can be reached on (571) 272-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /FAROUK A BRUCE/ Examiner, Art Unit 3797 /CHRISTOPHER KOHARSKI/ Supervisory Patent Examiner, Art Unit 3797 Application/Control Number: 17/333,410 Page 2 Art Unit: 3797 Application/Control Number: 17/333,410 Page 3 Art Unit: 3797 Application/Control Number: 17/333,410 Page 4 Art Unit: 3797 Application/Control Number: 17/333,410 Page 5 Art Unit: 3797 Application/Control Number: 17/333,410 Page 6 Art Unit: 3797 Application/Control Number: 17/333,410 Page 7 Art Unit: 3797 Application/Control Number: 17/333,410 Page 8 Art Unit: 3797 Application/Control Number: 17/333,410 Page 9 Art Unit: 3797 Application/Control Number: 17/333,410 Page 10 Art Unit: 3797 Application/Control Number: 17/333,410 Page 11 Art Unit: 3797 Application/Control Number: 17/333,410 Page 12 Art Unit: 3797 Application/Control Number: 17/333,410 Page 13 Art Unit: 3797 Application/Control Number: 17/333,410 Page 14 Art Unit: 3797 Application/Control Number: 17/333,410 Page 15 Art Unit: 3797 Application/Control Number: 17/333,410 Page 16 Art Unit: 3797 Application/Control Number: 17/333,410 Page 17 Art Unit: 3797 Application/Control Number: 17/333,410 Page 18 Art Unit: 3797 Application/Control Number: 17/333,410 Page 19 Art Unit: 3797 Application/Control Number: 17/333,410 Page 20 Art Unit: 3797 Application/Control Number: 17/333,410 Page 21 Art Unit: 3797 Application/Control Number: 17/333,410 Page 22 Art Unit: 3797 Application/Control Number: 17/333,410 Page 23 Art Unit: 3797 Application/Control Number: 17/333,410 Page 24 Art Unit: 3797
Prosecution Insights