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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on October 28, 2025 has been entered.
Claim Rejections - 35 USC § 103
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6-9 and 20-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (WO 2021/218077) in view of Huang et al. (US Pub No. 2015/0190120). Note that the below rejection refers to the English translation of Li et al.
With regards to claim 1, Li et al. disclose an ultrasound diagnostic apparatus, comprising:
processing circuitry (116) (pg. 6, first paragraph) configured to:
acquire first ultrasonic image data (i.e. B-mode ultrasound image) of a first display mode (pg. 6, last paragraph, referring to the processor performing conventional B-mode ultrasound image processing on the ultrasound echo signals to generate a B-mode ultrasound image; pg. 7, 3rd paragraph, referring to the display device (118) can display the ultrasound image obtained by the processor (116); pg. 8, 2nd-3rd paragraphs, referring to the processor performing signal processing on the first ultrasound echo signal to obtain a B-mode ultrasound image of the prostate; pg. 8, last paragraph, referring to forming a B-mode ultrasound image that reflects the tissue morphology and structure of the target area and the B-mode ultrasound image is output the display device (118) for display, wherein the B-mode image is observed in real time; Figure 1);
acquire an instruction to execute a second display mode different from the first display mode while the first ultrasonic image data is displayed on a display (pg. 8, last paragraph-pg. 9, first full paragraph, referring to the B-mode ultrasound image being output to the display device (118) for display and the user can observe the B-mode in real time and wherein, after generating and displaying the B-mode ultrasound image, the elastic image acquisition preparation state is entered and the ROI in the B-mode ultrasound image for generating the elastic image is determined, wherein it is inherent that, in order for the processor to execute/enter the elastic image acquisition preparation state, a computer instruction associated with entering the elastic image acquisition preparation, which ultimately leads to executing the “second display mode” (i.e. elastography display mode), is acquired while the first ultrasound image data (i.e. B-mode ultrasound image) is displayed);
estimate, in response to acquisition of the instruction, a position of an examination target (i.e. ROI/”extra-prostate gland region”) included in the first ultrasonic image data by applying, to a trained model (i.e. “a trained deep learning neural network model”), the first ultrasonic image data, and output an estimation result (pg. 9, first-2nd full paragraphs, referring to after generating and displaying the B-mode ultrasound image, the elastic image acquisition preparation state is entered (i.e. acquisition of the instruction to execute a second display mode (i.e. elastography display mode)) and the ROI in the B-mode ultrasound image for generating the elastic image is determined, wherein the location of the ROI can be automatically determined on the B-mode ultrasound image based on a machine recognition algorithm and the region of interest frame can be automatically generated/output; pg. 9, 3rd full paragraph, referring to the display device (118) transmitting/outputting the determined coordinate information of the region of interest to the processor (116); pg. 11, 2nd-3rd full paragraphs, referring to the processor using a trained deep learning neural network to segment the extra-prostatic gland area in a B-mode ultrasound image; Figures 4A,B) ;
calculate coordinates of a region of interest (i.e. ROI, “extra-prostatic gland area” and/or the “target measurement area 505”) corresponding to the second display mode (i.e. right side of Figures 5 and/or 6 and/or elastography display mode), different from the first display mode (i.e. left side of Figures 5 and/or 6 and/or B-mode display mode), wherein the coordinates of the region of interest are calculated based on the estimation result (pg. 9, 3rd – 5th full paragraphs, referring to after automatically detecting the location of the region of interest on the B-mode image, the display device transmits the determined coordinate information of the region of interest to the processor, and the processor determines the position of the region of interest in the tissue according to the coordinate information of the region of interest, so as to perform subsequent operations on the region of interest and when the position of the region of interest is determined, enter the scanning phase of the elastography mode, in which the elastography is performed on the region of interest; pg. 10, 2nd paragraph, referring to adding the region of interest identification to the elasticity based on the position information of the region of interest obtained in the elastic acquisition preparation stage; pg. 11, 4th full paragraph-pg. 12, 2nd paragraph, referring to, after determining the extra-prostatic gland area in the B-mode ultrasound image, the processor may determine the extra-prostate gland area in the elasticity image according to the corresponding relationship between the B-mode ultrasound image and the shear wave elastic image and referring to “a region of interest frame 502 is drawn on the B-mode ultrasound image 501 and the shear wave elastic image 503 is superimposed on the region of interest frame 502 of another same B-mode ultrasound image. After determining the extra-prostatic gland area 504 based on the B-mode ultrasound image 501, the elasticity measurement value of the extra-prostatic gland area 504 based on the B-mode ultrasound image 501, the elasticity measurement value of the extra-prostatic gland area 504 is obtained according to the shear wave elasticity image 503, and the obtained elasticity measurement value is in the extra-prostatic gland area 504”; pg. 12, 2nd full paragraph-pg. 14, 2nd to last paragraph, referring to determining a target measurement position and/or selecting the final target measurement area; pg. 15, 2nd full paragraph; Figures 5-6);
acquire second ultrasonic image data (shear wave elastic image data) by executing the second display mode (pg. 9, 3rd – 5th full paragraphs, referring to after automatically detecting the location of the region of interest on the B-mode image, the display device transmits the determined coordinate information of the region of interest to the processor, and the processor determines the position of the region of interest in the tissue according to the coordinate information of the region of interest, so as to perform subsequent operations on the region of interest and when the position of the region of interest is determined, enter the scanning phase of the elastography mode, in which the elastography is performed on the region of interest, wherein the transmitting circuit 112 excites the ultrasonic probe 110 to transmit the second ultrasonic wave tracking the shear wave to the determined region of interest and receive the echo of the second ultrasonic wave to obtain the second ultrasonic echo signal and generating a shear wave elastic image based on the distribution of elastic measured values; pg. 10, 3rd-5th paragraphs, referring to determining the area of the external prostate in the B-mode ultrasound image in real time and then maintain the position and direction of the ultrasound probe, and collect the shear wave elastic image corresponding to the B-mode ultrasound image or, after obtaining the B-mode ultrasound image and before acquiring the elastic image, determine the extra-prostatic gland area of the B-mode ultrasound image, then maintain the position and direction of the ultrasound probe and collect the corresponding B-mode ultrasound image shear wave elasticity image; pg. 15, 2nd full paragraph; Figures 5-6); and
display the second ultrasound image data and the region of interest on the acquired second ultrasonic image data of the second display mode based on the calculated coordinates on the display, wherein the region of interest is displayed automatically (pg. 6, last paragraph, referring to the ultrasound images, including the elastic images, and elastic measurement results being displayed on the display device; pg. 11, last paragraph-pg. 12, first paragraph, referring to the elasticity measurement value of the extra-prostatic gland area 504 is obtained according to the shear wave elasticity image 503 and the obtained elasticity measurement value is in the extra-prostatic gland area 504; pg. 15, 2nd full paragraph, referring to the processor controlling the display to display the elasticity measurement result, and thus the region of interest is displayed “automatically”; Figures 5-6).
However, Li et al. do not specifically disclose that calculating the coordinate of the region of interest corresponding to the second display mode based on the estimation result is further based on information regarding the second display mode.
Huang et al. disclose a method and system for processing ultrasonic data and for combined use of a shear-wave ultrasonic imaging technique and a B-mode ultrasonic imaging technique, wherein a first ROI is set on a B-mode ultrasonic image according to a first input received from a user and a second ROI may be generated on the basis of the first ROI (Abstract; paragraphs [0048]-[0055]). The user therefore only needs to set the first ROI once and the second ROI is automatically generated based on the first ROI, and in this way the user operation is simplified and the two ROI are sure to target the same or a corresponding relevant tissue area (Abstract; paragraph [0022]). The second ROI may be generated in different ways according to different clinical applications, wherein, before generating the second ROI at step 240, the method may further comprise receiving a second input from the user (paragraphs [0055], note that the “second input” corresponds to the claimed information regarding the second display mode as it is associated with the second ROI which is defined in the second display mode in Li et al.). If the second input indicates a lesion application, then at step 240, a contour of the lesion in the ultrasonic image may be generated on the basis of the first ROI and may be used as the second ROI (paragraph [0055], note that if the second input (i.e. information regarding the second display mode) indicates a lesion application, then the coordinates/position defining the second ROI is accordingly adjusted). If the second input indicates a non-lesion application, the second ROI may be generated in a different way at step 240; for example, the second ROI around the first ROI may be generated according to a predetermined shape (paragraph [0055], note that if the second input (i.e. information regarding the second display mode) indicates a non-lesion application, then the coordinates/position defining the second ROI is accordingly adjusted). In an example, the first ROI may be used as the second ROI (paragraph [0055]). In another example, the first ROI may be expanded by a predetermined factor and the shape expanded from the first ROI may be used as the second ROI (paragraph [0055]).4
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have calculating the coordinate of the region of interest corresponding to the second display mode based on the estimation result of Li et al. be further based on information regarding the second display mode, as taught by Huang et al., in order adapt the ROI according to different clinical applications in a manner in a manner that simplifies user operation and ensures that the two ROIs are sure to target the same or a corresponding relevant tissue area (Abstract; paragraphs [0022], [0055]).
With regards to claim 2, Li et al. disclose that the estimation result includes one or more units of detection (i.e. segmentation of the extra-prostatic gland area, the inner glandular area, and outer glandular area (i.e. plural “units of detection” and/or referring to pixel/pixel values) of the prostate or referring to only “one” unit of detection which encompasses the extra-prostatic gland area) including the examination target (i.e. extra-prostatic gland area) (pg. 11, first full paragraph, referring to the trained deep learning neural network model being used to segment the extra-prostatic gland area in a B-mode ultrasound image, wherein the segmentation includes segmentation of the inner glandular area and outer glandular area [i.e. “one or more units of detection”]; Figures 3-4), and the processing circuitry is further configured to: determine one or more detection areas (i.e. 505, 602, “sub-areas”) based on the one or more units of detection (i.e. the “one” unit of detection comprising the extra-prostatic gland area (504)) (pg. 12, 2nd paragraph-pg. 15, first full paragraph, referring to determining the target measurement area (505) and/or dividing the extra-prostatic gland area into 4 sub-areas/sub-regions); and calculate the coordinates based on one most probable detection area (i.e.505 or “final target measurement area”) from among the determined one or more detection areas (pg. 12, 2nd paragraph-pg. 15, first full paragraph, referring to the target measurement area (505) corresponding to the area with the highest elasticity measurement value in the extra-prostatic gland area (504) and/or determining the final target measurement position to determine the final target measurement area; Figures 5-6).
With regards to claim 3, Li et al. disclose the processing circuitry is further configured to, when a plurality of detection areas among the determined one or more detection areas are determined (pg. 12, 3rd-4th paragraphs, see Figure 6, referring to the extra-prostatic gland area (601) being divided into at least two sub-areas (i.e. a plurality of detection areas which comprise of pixel areas), wherein in Figure 6, there are 4 sub-areas) calculate a total value of a likelihood (i.e. likelihood of candidate measurement area (602) corresponding to a lesion/abnormal tissue and/or meeting a pre-set condition) of those units of detection, of the one or more units of detection (i.e. 602, corresponding to a circular candidate measurement area respectively determined in each sub-area) included in each of the determined plurality of detection areas (i.e. a circular candidate measurement area (602) is included in each of the detection areas/sub-areas) based on a corresponding likelihood (i.e. likelihood of a lesion or measurement area meeting a preset condition) of each of the one or more units of detection obtained when estimating the position of the examination target (pg. 12, 4th-6th paragraphs; pg. 13, 1st-pg. 15, 2nd paragraph, Figure 6, note that the unit of detections (602) are also present in the image (i.e. B-mode image on left side of Fig. 6) in which the position of the examination target is estimated); and determine, as the one most probable detection area (i.e. final target measurement position), a particular detection area having a highest total likelihood value (i.e. determining a lesion area (i.e. detection area having a highest total likelihood value of corresponding to a lesion area) by using the pixel point corresponding to about 75% of the maximum value in the elasticity measurement values as the target measurement position which reflects the final target measurement position, etc.) among the determined plurality of detection areas (pg. 13, 1st-pg. 15, 2nd paragraph; Figure 6).
With regards to claim 4, Li et al. disclose that the processing circuitry is further configured to, when a plurality of detection areas among the determined one or more detection areas are determined (pg. 12, 3rd-4th paragraphs, see Figure 6, referring to the extra-prostatic gland area (601) being divided into at least two sub-areas (i.e. a plurality of detection areas which comprise of pixel areas), wherein in Figure 6, there are 4 sub-areas), determine, as the one most probable detection area (i.e. final target measurement position), a particular detection area including a particular unit of detection, of the one or more units of detection, having a highest likelihood, (i.e. highest likelihood of a lesion or measurement area meeting a preset condition and/or determining a lesion area (i.e. detection area having a highest total likelihood value of corresponding to a lesion area) by using the pixel point corresponding to about 75% of the maximum value in the elasticity measurement values as the target measurement position which reflects the final target measurement position, etc.) among the determined plurality of detection areas, based on a likelihood of the one or more units of detection obtained when estimating the position of the examination target (pg. 12, 4th-6th paragraphs; pg. 13, 1st-pg. 15, 2nd paragraph, Figure 6, note that the unit of detections (602) are also present in the image (i.e. B-mode image on left side of Fig. 6) in which the position of the examination target is estimated).
With regards to claim 6, Li et al. disclose that the determined one most probably detection area is a single unit of detection of the one or more units of detection or a plurality of units of detection of the one or more units of detection (pg. 12, 3rd-4th paragraphs, see Figure 6, referring to the extra-prostatic gland area (601) being divided into at least two sub-areas (i.e. a plurality of detection areas which comprise of pixel areas; see Figures 5-6).
With regards to claim 7, Li et al. disclose that the processing circuitry is further configured to, when the determined one most probably detection area is the plurality of units of detection, calculate the coordinates based on a rectangle in contact with an outer perimeter of the plurality of units of detection (pg. 14, second to last paragraph, referring to the target measurement position may be the center of the target measurement area [and thus the center would correspond to the coordinates]; referring to pg. 12, second paragraph, pg. 14, second to last paragraph- pg. 15, first paragraph, referring to the target measurement area and/or candidate measurement area can comprise a rectangle shape; see Figure 6, wherein there is at least one candidate areas (602), which can be a rectangle shape, and which is in contact with an outer perimeter of another of the plurality of units of detection/candidate areas).
With regards to claim 8, Li et al. disclose that the processing circuitry is further configured to calculate the coordinates based on a center and a long side of the rectangle (pg. 14, second to last paragraph, referring to the target measurement position may be the center of the target measurement area [and thus the center would correspond to the coordinates]; referring to pg. 12, second paragraph, pg. 14, second to last paragraph- pg. 15, first paragraph, referring to the target measurement area and/or candidate measurement area can comprise a rectangle shape; see Figure 6, wherein there is at least one candidate areas (602), which can be a rectangle shape, and which is in contact with an outer perimeter of another of the plurality of units of detection/candidate areas and wherein a center coordinate would inherently be based on a long side of the rectangle as the center calculation would be based on the coordinates of both pairs of opposite sides (including the long side) of the rectangle (i.e. center of rectangle is known in the art to be defined as (x_center = (x1 + xy)/2; y_center = (y1 + y2)/2).
With regards to claim 9, Li et al. disclose that the processing circuitry is further configured to change at least one of a size or a shape of the region of interest according to information of the second display mode (pg. 14, 2nd to last paragraph-pg. 15, second paragraph, referring to changes of the shape and area/size of the final target measurement area when displayed, and thus is according to information “of the second display mode”; pg. 16, second to last paragraph, referring to the candidate measurement position corresponding to each sub-region is determined according to the elasticity measurement values [which correspond to the second display mode], and thus the size/shape of the region of interest is ultimately according to the elasticity measurement values (i.e. information) of the second display mode).
With regards to claim 20, Li et al. disclose that the processing circuitry is further configured to change a color of an outer frame [i.e. displayed image/frame] of the region of interest according to the estimation result (pg. 9, 2nd to last paragraph, referring to pseudo-color mapping [which requires changing colors of the image/frame] being performed based on the elasticity measurement values at multiple positions in the region of interest and superimposed in the region of interest frame of the B-mode ultrasound image, and thus ultimately according to the estimation result on the B-mode ultrasound image).
With regards to claim 21, Li et al. disclose that the processing circuitry is further configured to display at least one of a character string or a mark (i.e. the marking/marks of the regions 505, 504, 602, 601/604) on a display screen of the second display mode (i.e. right side of the screen) according to the estimation result (see Figures 5-6).
With regards to claim 22, Li et al. disclose that the second display mode is a bloodstream imaging mode or an elastography mode (i.e. “Elastography”/“shear wave elastic image”) (pg. 9, 3rd-5th paragraphs).
With regards to claim 23, Li et al. disclose that the second display mode is a measurement mode associated with the bloodstream imaging mode or the elastography mode (i.e. “Elastography”/”shear wave elastic image”), and the region of interest shows a measurement region (pg. 9, 3rd-5th paragraphs, referring to the shear wave elastic image wherein pseudo-color mapping can be performed based on the elasticity measurement values at multiple position sin the region of interest and superimposed in the region of interest frame).
With regards to claim 24, Li et al. disclose that the processing circuitry is further configured to change a parameter related to the second display mode according to a position of the region of interest (pg. 9, 3rd paragraph -5th paragraph, referring to, when the region of interest is determined, enter the scanning phase of the elastography mode, wherein a shear wave can be generated inside the tissue of the region of interest by “focusing and impacting the acoustic radiation force” and a transmitting circuit (112) excites the ultrasonic probe to transmit the second ultrasonic wave tracking the shear wave to the determined region of interest and receive the echo of the second ultrasonic wave to obtain the second ultrasonic echo signal, where focusing and transmitting to the region of interest would inherently require controlling [and thus changing at least a focus position parameter, etc.] related to the second display mode [i.e. elastography scanning] according to a position of the region of interest).
With regards to claim 25, Li et al. disclose that the processing circuitry is further configured to change any one of a transmission-reception frequency of an ultrasound beam, a focus position, a gain, and a depth as the parameter (pg. 9, 3rd paragraph -5th paragraph, referring to, when the region of interest is determined, enter the scanning phase of the elastography mode, wherein a shear wave can be generated inside the tissue of the region of interest by “focusing and impacting the acoustic radiation force” and a transmitting circuit (112) excites the ultrasonic probe to transmit the second ultrasonic wave tracking the shear wave to the determined region of interest and receive the echo of the second ultrasonic wave to obtain the second ultrasonic echo signal, where focusing and transmitting to the region of interest would inherently require controlling [and thus changing] at least a focus position parameter, etc. related to the second display mode [i.e. elastography scanning] according to a position of the region of interest).
With regards to claim 26, Li et al. disclose that the trained model is a deep neural network (pg. 11, 2nd-3rd full paragraphs, referring to the processor using a trained deep learning neural network to segment the extra-prostatic gland area in a B-mode ultrasound image).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. in view of Huang et al. as applied to claim 2 above, and further in view of Wang et al. (US Pub No. 2022/0139531).
With regards to claim 5, as discussed above, the above combined references meet the limitations of claim 2. Further, Li et al. disclose that, wherein the processing circuitry is further configured to, when a plurality of detection areas among the determined one or more detection areas are determined, determine, as the one most probable detection area, a detection area corresponding to a lesion/abnormal area (pg. 12, 3rd-4th paragraphs, see Figure 6, referring to the extra-prostatic gland area (601) being divided into at least two sub-areas (i.e. a plurality of detection areas which comprise of pixel areas), wherein in Figure 6, there are 4 sub-areas; pg. 13, 1st-pg. 15, 2nd paragraph, referring to determining a lesion area (i.e. detection area having a highest total likelihood value of corresponding to a lesion area) by using the pixel point corresponding to about 75% of the maximum value in the elasticity measurement values as the target measurement position which reflects the final target measurement position, etc.; Figure 6).
However, Li et al. do not specifically disclose that the detection area [corresponding to a lesion/abnormal area] is determined as the detection area having a largest number of units of detection of the one or more units of detection overlapping each other among the determined plurality of detection areas.
Wang et al. disclose a system for lesion segmentation, wherein when two regions (i.e. “bubbles”) in an image corresponding to a detected lesion overlap, an overlap metric is calculated and, if the overlap’s metric’s value is greater than a threshold, it is concluded that the two regions/bubbles overlap too much to be distinct and a merge will take place (Abstract; paragraphs [0163]-[0164], [0170]-[0173]). This is due to a conclusion that the two local maxima of the two regions/bubbles correspond to two “centers” of the same lesion (paragraph [0163]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the detection area of the above combined references be determined as the detection area having a largest number of units of detection of the one or more units of detection, overlapping each other, among the determined plurality of detection areas, as taught by Wang et al., in order to determine whether the detection areas correspond to the same lesion and thus provide a merging of the detection areas to accurately represent the lesion (paragraphs [0163]-[0164]).
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. in view of Huang et al. as applied to claim 1 above, and further in view of Li’426 (US Pub No. 2017/0124426).
With regards to claims 10-12, as discussed above, the above combined references meet the limitations of claim 1. However, though Li et al. do disclose that the region of interest are displayed on the second ultrasonic image data (see Figures 5-6), Li et al. do not specifically disclose that the processing circuitry is further configured to rest the region of interest according to a predetermined condition, wherein the processing circuitry is further configured to calculate new coordinates of a new region of interest in a frame after a frame for which the coordinates of the region of interest are calculated, reset the new region of interest when a degree of coincidence between a region of the coordinates and a region of the new coordinates is less than a threshold, and display the new region of interest on the second ultrasonic image data or wherein the processing circuitry is further configured to calculate a value of correlation between a predetermined region of reference ultrasonic image data and a predetermined region of current ultrasonic image data, determine to reset a new region of interest when the value of correlation is less than a threshold, calculate new coordinates of the new region of interest based on the current ultrasonic image data and display the new region of interest on the second ultrasonic image data.
Li’426 discloses a system for tracking a tumor region as a region of interest with high precision (Abstract; paragraphs [0005], [0016]-[0017], [0031]). The apparatus acquires the most recent frames of ultrasound images captured in real time by an ultrasound diagnosis apparatus and performs tracking processing using an initial frame, a previous frame and a current frame (paragraphs [0021]-[0024]; Figure 9). A determination is made as to whether all the frames have undergone the tracking processing, wherein if it is determined that not all of the frames have undergone the tracking processing [wherein the tracking processing includes comparing ROIs in two of the frames and is ended after performing forward and backward matching which comprises comparing ROIs in different frames[see Figure 6, see steps S07-S209 ], the apparatus proceeds to step S1 to acquire the most recent frames (paragraphs [0022]-[0024], see Figure 9, wherein S3 [tracking processing] corresponds to the steps as depicted in Figure 6 and by going from step S4 back to S1, a new region of interest would be reset when repeating the tracking processing in S3, wherein the new region of interest would be reset/saved on the saved current frame; alternatively, as depicted in Figure 6, the “reset” of the region of interest can be viewed as corresponding to saving the current frame (Frame i) which is saved and includes the “new ROI” which was ultimately determined from the initial frame]. The tracking processing procedure, as depicted in Figure 6, comprises obtaining an initial frame that includes the ROI, wherein a reference frame (Frame r) can be obtained based on the initial frame (Frame 1) and a previous frame (Frame_i-1) (paragraphs [0026]-[0027], [0075]-[0076]; Figures 2-6). A matched region (i.e. “new coordinates of a new region of interest”) in the current frame (i.e. “a frame after a frame (i.e. Frame 1 or Frame r) is obtained according to the ROI in the reference frame (paragraph [0079]). Differentials between ROI in the reference frame and the matched region in the current frame and between the matched region in the current frame and the ROI in the previous frame are calculated and differentials between matched region in current frame and a new matched region in reference frame and new matched region in previous frame are calculated, wherein if it is determined that the difference between variation amounts obtained by forward matching and reverse matching between the reference frame and the current frame, etc. is smaller than a threshold [i.e. a “predetermined condition”], it is determined that the tracking is successful and the current frame, which includes the new ROI (i.e. matched region in current frame) is saved, which thus result in resetting the new region of interest in the current frame as the current ROI that is saved (paragraphs [0080]-[0084], note that the differences between the variation amounts correspond to a degree of coincidence between a region of the coordinates [i.e. coordinates of ROI in reference frame which is obtained from the initial frame, etc.] and a region of the new coordinates (i.e. matched region in current frame) and/or corresponds to a value of correlation between a predetermined region of reference ultrasonic image data (i.e. ROI in reference frame) and a predetermined region of current ultrasonic image data (i.e. ROI matched region in the current frame); Figures 2-6, 9). The new region of interest (i.e. matched region in current frame) is displayed (paragraphs [0026]-[0027], [0105], [0107]; Figures 8-10).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the processing circuitry of the above combined references be further configured to reset the region of interest according to a predetermined condition, wherein the processing circuitry is further configured to calculate new coordinates of a new region of interest in a frame after a frame for which the coordinates of the region of interest are calculated, reset the new region of interest when a degree of coincidence between a region of the coordinates and a region of the new coordinates is less than a threshold, and display the new region of interest on the second ultrasonic image data or wherein the processing circuitry is further configured to calculate a value of correlation between a predetermined region of reference ultrasonic image data and a predetermined region of current ultrasonic image data, determine to reset a new region of interest when the value of correlation is less than a threshold, calculate new coordinates of the new region of interest based on the current ultrasonic image data and display the new region of interest on the second ultrasonic image data, as taught by Li’426, in order to track a tumor region as a region of interest with high precision (Abstract; paragraphs [0005], [0016]-[0017], [0031]).
Claim(s) 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. in view of Huang et al. as applied to claims 1 above, and further in view of Dickie et al. (US Pub No. 2023/0225711). Note that the Provisional application No. 63/300,157 filed on January 17, 2022 supports the below teachings (see at least paragraph [0039] and Figure 2B).
With regards to claims 17-19, as discussed above, the above combined references meet the limitations of claim 1. However, though Li et al. do disclose that the processing circuitry is configured to calculate the coordinates, acquire the first ultrasonic image data and estimate the position of the examination target (pg. 8, 1st-second to last paragraphs; pg. 10, second paragraph-2nd to last paragraph; Figures 5-6), Li et al. do not specifically disclose that the processing circuitry is further configured to perform these steps “in response to a mode-transition-related user operation”.
Dickie et al. disclose an ultrasound system wherein a drop-down screen on a touchscreen device allows an operator to engage different scanning modes, including a B-mode, Doppler mode and Elastography) (Abstract; paragraphs [0065], [0066], Figure 2B, note that a drop-down menu provides a mode-transition-related user operation, wherein, in response to a user selecting one of the modes (i.e. B-mode, elastography, etc.), a transition is made to a process related to the selected mode [wherein in Li et al, those processes would correspond to calculating the coordinates, acquiring the first ultrasonic image data, estimating the position of the examination target, etc.). The touchscreen device provides an interface user trigger for initiating the different processes (paragraph [0034]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the processing circuitry of the above combined references be further configured to perform the calculating, acquiring and estimating steps “in response to a mode-transition-related user operation”, as taught by Dickie et al., in order to allow a user to provide effective triggers for initiating the different processes, thereby allowing user control of the timing of processing steps for individualized diagnostic purposes (paragraphs [0034], [0066]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-12 and 17-26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Huang has been introduced to teach that calculating the coordinates of the region of interest is further based on information regarding the second display mode.
With regards to claim 1, Applicant argues that Li fails to disclose that the processing circuitry is configured to acquire an instruction to execute a second display mode different from the first display mode while the first ultrasonic image data is displayed on a display, and estimate in response to acquisition of the instruction, a position of an examination target.
Examiner respectfully disagrees and points to pg. 8, last paragraph-pg. 9, first full paragraph of Li, which refers to the B-mode ultrasound image being output to the display device (118) for display and the user can observe the B-mode in real time and wherein, after generating and displaying the B-mode ultrasound image, the elastic image acquisition preparation state is entered and the ROI in the B-mode ultrasound image for generating the elastic image is determined, wherein it is inherent that, in order for the processor to execute/enter the elastic image acquisition preparation state, a computer “instruction” associated with entering the elastic image acquisition preparation, which ultimately leads to executing the “second display mode” (i.e. elastography display mode), is acquired while the first ultrasound image data (i.e. B-mode ultrasound image) is displayed. Li et al. therefore does disclose the above limitations.
Conclusion
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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798