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 .
Status of claims: claims 1 and 3-19 are pending below. Claim 2 is cancelled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 31st 2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 27th, 2026 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed March 31, 2026 have been fully considered but they are not persuasive.
Applicant remark – (pages 8-9) Applicant argued the lack of teaching regarding new claim amendment. Please see Claims and Remarks for further detail.
Examiner response – Examine respectfully disagree. An updated search found that McGinnis et al (US 2011/0206250) teaches the new claim amendment in figure 6 and paragraph 0069. The combine teaching of ENDO (US 2020/0138265) in view of McGinnis et al (US 2011/0206250) teaches the new claim amendment. Please see Office Action below for further detail.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3-19 are rejected under 35 U.S.C. 103 as being unpatentable over ENDO (US 2020/0138265) in view of McGinnis et al (US 2011/0206250).
Claim 1:
ENDO (US 2020/0138265) teaches the following subject matter:
A medical image processing apparatus comprising a processor configured to perform:
medical image acquisition processing of sequentially acquiring time-series medical images (0006 teaches acquires medical images in time series with discrimination sections to recognition same subject and region of interest; 0009 further teaches medical image in time series with region of interest and region of concern);
first scene recognition processing of recognizing at least one first scene from one medical image of the medical images (flowchart of figure 7 part S104 and paragraph 0108-0109 teaches discrimination image in S104 (first scene recognition) where discrimination can include the type of a lesion (a hyperplastic polyp, an adenoma, an intramucosal carcinoma, an invasive carcinoma, or the like), the range of a lesion, the size of a lesion, the visual shape of a lesion, the stage diagnosis of a cancer, the current position in a lumen (the pharynx, the esophagus, the stomach, the duodenum, or the like as an upper portion; and the appendix, the ascending colon, the transverse colon, the descending colon, the sigmoid colon, the rectum, or the like as a lower portion) where discrimination result notification such as numerical value, figure/bar display, symbol, color, voice with speaker as detail in 0109; where figure 5 is the configuration and 0074-0077 teaches detection 204B with a first convolutional neural network (CNN) 214 where CNN 214 detection (recognizing) of medical image);
second scene recognition processing of recognizing a second scene from the one medical image if the at least one first scene is recognized (figure 7 part S112 and paragraph 0113-0117, specifically 0113 teaches region of interest (region of concern/second scene recognition) for a polyp, a cancer, the colonic diverticula, an inflammation, treatment scars (an endoscopic mucosal resection (EMR), an endoscopic submucosal dissection (ESD), a clipped portion, and the like), a bleeding point, a perforation, blood vessel heteromorphism, and the like. The detection section 204B may detect a region of interest in local region with specific hue, where S120 and paragraph 0117 teaches notification by voice speaker 209A; figure 5 and 0074-0077 teach detection 204C with a second CNN 215, where CNN 215 used for discrimination (recognizing) within the detection of CNN 214) recognized, wherein the second scene is a scene in a narrower range than the first scene and is included in the at least one first scene (0009 detail “region of concern” or ROI that is within the first image (smaller region/narrower), where 0113 further detail that region of interest (ROI)/region of concern includes the polyp, cancer, scar or inflammation, clipped portion from the first image that has been processed by CNN from every local region of the target image (first image));
first notification processing of providing a notification indicating that the at least one first scene is recognized (figure 7 step S104 and paragraph 0108-0109 teaches first notification such as numerical value, figure/bar display, symbol, color; figure 5 and 0074-077 teaches where detection result is display on monitor (paragraph 0074); 0109 further detail display to notify user numerical value, figure, bar display, symbol, color); and
second notification processing of providing another notification indicating that the second scene is recognized (figure 7 step S112-S120 and paragraph 0113-0117 teaches second notification such as voice with speaker 209A; figure 5 and 0074-0077, and paragraph 0109 teaches notification by voice 209 and speaker 209A).
ENDO teaches regarding position/coordinate relating to region of interest (ROI/region of concern) by color in paragraph 0007 but not the following which is taught by McGinnis et al (US 2011/0206250):
wherein the at least one first scene is a scene of each of a plurality of regions of an examination target, and wherein the second scene is a scene that satisfies a predetermined condition for observation or diagnosis, wherein the predetermined condition comprises at least one of brightness, blur or shake, and a position of a target region within a predetermined area of the one medical image (figure 6 and 0069 detail ROI is further under evaluated by its predetermined location and tagged bright like polyps, colonic stool does not grow from the colonic wall; 0003 detail such image processing from medical test from endoscope).
ENDO and McGinnis et al are both in the field of image analysis, especially the further evaluation of region of interest from particular location/position from images obtained from endoscope such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify ENDO by McGinnis et al regarding using of position/location and brightness values to further identifying (focusing) the interest of the candidate during evaluation as disclosed by McGinnis et al in 00069.
Claim 3:
The medical image processing apparatus according to claim 1, comprising a second scene recognizer configured to perform the second scene recognition processing for each of the at least one first scene, wherein the first scene recognition processing recognizes two or more first scenes of the at least one first scene, and in accordance with the two or more first scenes recognized in the first scene recognition processing, the second scene recognizer is selected to recognize the second scene (figure 5 and 0074-0077 teach detection 204C with a second CNN 215, where CNN 215 used for discrimination (recognizing) within the detection of CNN 214 of the same medical images; paragraph 0083-0084 teaches CNN 215 takes the output of CNN 214 such as tumor, non-tumor and other scores, where CNN 215 further discriminate the above-mentioned CNN 214 outputs).
Claim 4: ENDO teach:
The medical image processing apparatus according to claim 1, wherein, after the second scene is determined to be recognized in the second scene recognition processing, the first notification processing is not performed (figure 5 and 0074 teaches dotted line where the flow of processing does not pass through CNN 214).
Claim 5: ENDO teach:
The medical image processing apparatus according to claim 1, wherein, after an image of the second scene is captured, the first notification processing is not performed (figure 5 and 0074 teaches dotted line where the flow of processing does not pass through CNN 214).
Claim 6: ENDO teach:
The medical image processing apparatus according to claim 1, wherein, after the second scene is determined to be recognized, the second scene recognition processing is not performed (figure 5 teach operation control section 204J and figure 4 and paragraph 0070 teaches control further processing of detecting, discrimination and display; figure 5 and 0074 teaches 204J operation one or the other CNN 214 detection or CNN 215 discrimination).
Claim 7: ENDO teach:
The medical image processing apparatus according to claim 1, wherein, after an image of the second scene is captured, the second scene recognition processing is not performed (figure 5 teach operation control section 204J and figure 4 and paragraph 0070 teaches control further processing of detecting, discrimination and display; figure 5 and 0074 teaches 204J operation one or the other CNN 214 detection or CNN 215 discrimination).
Claim 8: ENDO teach:
The medical image processing apparatus according to claim 1, wherein the second notification processing continuously provides a notification indicating that the second scene is recognized (figure 5 teach operation control section 204J and figure 4 and paragraph 0070 teaches control further processing of detecting, discrimination and display; figure 5 and 0074 teaches 204J operation one or the other CNN 214 detection or CNN 215 discrimination, where figure 5 and 0074-0077, and paragraph 0109 teaches notification by voice 209 and speaker 209A).
Claim 9: ENDO teach:
The medical image processing apparatus according to claim 1, wherein the first notification processing provides a notification by an indication on a screen, and the second notification processing provides a notification by sound (figure 5 and 0074 teaches 204J operation one or the other CNN 214 detection or CNN 215 discrimination, where figure 5 and 0074-0077, and paragraph 0109 teaches notification by voice 209 and speaker 209A).
Claim 10: ENDO teach:
The medical image processing apparatus according to claim 9, wherein the indication on the screen is a sample image of the at least one first scene (figure 5 and 0074-077 teaches where detection result is display on monitor (screen) (paragraph 0074); 0109 further detail display to notify user numerical value, figure, bar display, symbol, color).
Claim 11: ENDO teach:
The medical image processing apparatus according to claim 1, wherein the first scene recognition processing and the second scene recognition processing are performed by using a Convolutional Neutral Network (above teaches CNN 214 and CNN 215, where 0020 detail convolutional neural network (CNN)).
Claim 12: ENDO teach:
The medical image processing apparatus according to claim 11, wherein the first scene recognition processing recognizes the at least one first scene, based on a classification score (0083 teaches classifying output with scores from CNN 214 (first scene recognition processing)).
Claim 13: ENDO teach:
The medical image processing apparatus according to claim 11, wherein the second scene recognition processing recognizes the second scene, based on a degree of similarity (figure 5 teaches CNN 214 and CNN 215, where convolutional neural network (CNN) is known by one ordinary skill in the art that CNN learn through similarity and thus carry out the function of similarity that it has learned; one ordinary skill in the art of CNN that functions as discrimination carry out function of similarity in order to discriminate).
Claim 14: ENDO teach:
The medical image processing apparatus according to claim 1, wherein the at least one first scene and the second scene are scenes in which an image of a site inside a stomach is captured (0108 teaches use of medical image of stomach, where 0004-0006 detail the capture of medical images by endoscope).
Claim 15:
ENDO (US 2020/0138265) teaches the following subject matter:
A medical image processing method using a medical image processing apparatus comprising a processor configured to perform (0008 teaches the use of processor with the diagnosis support system with endoscope system computer system for processing):
sequentially acquiring time-series medical images (006 teaches acquires medical images in time series with discrimination sections to recognition same subject and region of interest; 0009 further teaches medical image in time series with region of interest and region of concern);
recognizing a first scene from one medical image of the medical images (flowchart of figure 7 part S104 and paragraph 0108-0109 teaches discrimination image in S104 (first scene recognition) where discrimination can include the type of a lesion (a hyperplastic polyp, an adenoma, an intramucosal carcinoma, an invasive carcinoma, or the like), the range of a lesion, the size of a lesion, the visual shape of a lesion, the stage diagnosis of a cancer, the current position in a lumen (the pharynx, the esophagus, the stomach, the duodenum, or the like as an upper portion; and the appendix, the ascending colon, the transverse colon, the descending colon, the sigmoid colon, the rectum, or the like as a lower portion) where discrimination result notification such as numerical value, figure/bar display, symbol, color, voice with speaker as detail in 0109; where figure 5 is the configuration and 0074-0077 teaches detection 204B with a first convolutional neural network (CNN) 214 where CNN 214 detection (recognizing) of medical image);
recognizing a second scene from the one medical image if the first scene is recognized (figure 7 part S112 and paragraph 0113-0117, specifically 0113 teaches region of interest (region of concern/second scene recognition) for a polyp, a cancer, the colonic diverticula, an inflammation, treatment scars (an endoscopic mucosal resection (EMR), an endoscopic submucosal dissection (ESD), a clipped portion, and the like), a bleeding point, a perforation, blood vessel heteromorphism, and the like. The detection section 204B may detect a region of interest in local region with specific hue, where S120 and paragraph 0117 teaches notification by voice speaker 209A; figure 5 and 0074-0077 teach detection 204C with a second CNN 215, where CNN 215 used for discrimination (recognizing) within the detection of CNN 214) wherein the second scene is a scene in a narrower range than the first scene and is included in the at least one first scene (0009 detail “region of concern” or ROI that is within the first image (smaller region/narrower), where 0113 further detail that region of interest (ROI)/region of concern includes the polyp, cancer, scar or inflammation, clipped portion from the first image that has been processed by CNN from every local region of the target image (first image));
providing a notification indicating that the first scene is recognized (figure 7 step S104 and paragraph 0108-0109 teaches first notification such as numerical value, figure/bar display, symbol, color; figure 5 and 0074-077 teaches where detection result is display on monitor (paragraph 0074); 0109 further detail display to notify user numerical value, figure, bar display, symbol, color); and
providing another notification indicating that the second scene is recognized (figure 7 step S112-S120 and paragraph 0113-0117 teaches second notification such as voice with speaker 209A; figure 5 and 0074-0077, and paragraph 0109 teaches notification by voice 209 and speaker 209A).
ENDO teaches regarding position and coordinate relating to region of interest by color in paragraph 0007 but not the following which is taught by McGinnis et al (US 2011/0206250):
wherein the at least one first scene is a scene of each of a plurality of regions of an examination target, and wherein the second scene is a scene that satisfies a predetermined condition for observation or diagnosis, wherein the predetermined condition comprises at least one of brightness, blur or shake, and a position of a target region within a predetermined area of the one medical image (figure 6 and 0069 detail ROI is further under evaluated by its predetermined location and tagged bright like polyps, colonic stool does not grow from the colonic wall; 0003 detail such image processing from medical test from endoscope).
ENDO and McGinnis et al are both in the field of image analysis, especially the further evaluation of region of interest from particular location/position from images obtained from endoscope such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify ENDO by McGinnis et al regarding using of position/location and brightness values to further identifying (focusing) the interest of the candidate during evaluation as disclosed by McGinnis et al in 00069.
Claim 16: ENDO teach:
ENDO (US 2020/0138265) teaches the following subject matter:
A non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to execute the medical image processing method according to claim 15 is recorded (0068 teaches use of memory such as ROM 211, RAM 212 for carrying out operations above).
Claim 17: ENDO teach:
The medical image processing apparatus according to The medical image processing apparatus according to wherein the first scene recognition processing recognizes a designated organ from the medical image, and wherein the second scene recognition processing recognizes a designated region within the designated organ for diagnosis (figure 7 S102-S106 and paragraph 0108-1019 teaches discrimination of organ such as pharynx, the esophagus, the stomach, the duodenum, or the like as an upper portion; and the appendix, the ascending colon, the transverse colon, the descending colon, the sigmoid colon, the rectum; figure 7 and paragraph 0113-0117 teaches region of interest (region of concern) detected in Step S112 can include a polyp, a cancer, the colonic diverticula, an inflammation, treatment scars).
Claim 18: ENDO teach:
The medical image processing apparatus according to The medical image processing apparatus according to wherein the first notification processing provides a first notification indicating that the designated organ is recognized, wherein the second notification processing provides a second notification, different from the first notification, indicating that the designated region is recognized (above teaches figure 7 and paragraph 0108-0109 teaches first notification such as numerical value, figure/bar display, symbol, color, and paragraph 0113-0117 teach second nonfiction such as voice with speaker).
Claim 19: ENDO teach:
The medical image processing apparatus according to 18, The medical image processing apparatus according to wherein the second notification is provided after the first notification (in the flowchart of figure 7 the notification of the second notification from step S110-S120 is after first notification from S102-S106).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
OOSAKE (US 2022/0285010) teaches MEDICAL IMAGE PROCESSING APPARATUS, MEDICAL IMAGE PROCESSING METHOD, AND PROGRAM - sequentially acquire a plurality of medical images in a chronological manner; recognize, on the basis of the acquired medical images, a specific scene in the medical images; acquire a recognition frequency of the recognized specific scene; and cause the monitor to display, in accordance with the recognition frequency, a notification indication that changes in two or more stages and that indicates a degree of recognition (abstract).
Ng et al (US 2022/002857) teaches SYSTEMS AND METHODS FOR ANALYSIS OF MEDICAL IMAGES FOR SCORING OF INFLAMMATORY BOWEL DISEASE – Use of endoscope is passed through the mouth and throat into the esophagus of a patient, allowing a doctor to view the esophagus, stomach for examination (paragraph 0004), with use of process includes processing, by the machine learning model or by a second machine learning model in addition to the machine learning model, the EMR feature vector. The process includes generating an updated score representing the severity of IBD in the patient indicated by the EMR data (paragraph 0023)
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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