CTFR 17/192,830 CTFR 93146 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 17-20, 22, 24-27, 29, 31-34, 36, and 38-43 are pending, claims 1-16, 21, 23, 28, 30, 35, and 37 have been cancelled, and claims 17-20, 22, 24-27, 29, 31-34, 36, and 38-43 are currently under consideration for patentability under 37 CFR 1.104. Previous 35 USC 112 Rejections have been withdrawn in light of Applicant’s arguments and amendments. Response to Arguments Applicant’s arguments with respect to claim(s) 17-20, 22, 24-27, 29, 31-34, 36, and 38-43 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. 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. 07-21-aia AIA Claim (s) 17-20, 22, 24-27, 29, 31-34, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0238086), in view of Imaizumi (US 2018/0249900) and Liang (US 2018/0225820) and Matsuzaki (US 2010/0277650) . Regarding claim 17, Saito discloses a medical image processing apparatus comprising one or more processors (16, figures 1-2) configured to: cause an image sensor (48, figure 2) to sequentially capture a plurality of images of an observation target irradiated with illumination light (s10, figure 16); perform detections of a detection target from the plurality of images to output results of the detections of the detection target for the plurality of images (see s11 and arrows from it, figure 16); according to results of the detections of the detection target, display information (broadly interpreted as any visual change on the display that indicates presence or absence of the detection target) indicating presence or absence of the detection target on a monitor so as to be superimposed (interpreted as to “place” something, Merriam-Webster definition) on the plurality of images displayed on the monitor or not to overlap with the plurality of images displayed on the monitor (s18, figure 16 | the oxygen saturation image indicates a lesion/target has been detected); display, in a first mode (normal observation mode s10, figure 16) and a second mode (special observation mode s12, figure 16), an image among the plurality of images to an observer (displayed…[0098]); and switch from the first mode to the second mode (see s10 and s12, figure 16). Saito is silent regarding in response to having detected the detection target, determine the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames, the consecutive frames being longer than a time range, and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold; determine an amount of change of among positions of the detection target in the consecutive frames of the plurality of images; determine whether the amount of change exceeds an amount of change threshold value; and switch automatically from the first mode to the second mode in a case where the detection target is continuously detected and the amount of change is within the amount of change threshold value in a state where the first mode is used. Imaizumi teaches an endoscope system (1, figure 1) with an endoscope (21, figure 1) and a video processor (31, figure 1) with a detection supporting section (33, figure 1). The detection supporting section (33, figures 1-2) has a detecting section (34, figure 2), a continuous detection determining section (35, figure 2), and a detection result output section (36, figure 2). The detecting section (34, figure 2) is a circuit to which the observation image of the subject is sequentially inputted, the circuit detecting a lesion candidate region L based on a predetermined feature value ([0032]). The predetermined feature value is calculated by calculating, for each of predetermined small regions on the observation image, a change amount or tilt value between respective pixels in the small region and pixels adjacent to the pixels ([0034]). The continuous detection determining section (35, figure 2) is a circuit that determines whether the lesion candidate region L is continuously detected, where a RAM (35, figure 2) stores lesion candidate information of an immediately preceding frame ([0039]). The lesion candidate region can be tracked and when the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images, the continuous detection determining section determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section (36, figure 2; [0040]). Liang teaches a process (100, figure 1a) for monitoring colonoscopic video quality and detecting polyps in colonoscopy ([0057]). For each image frame, an informativeness score is determined (quality of the image frame [0059]; 120, figure 1a). A traffic light indicator (174, figure 1b) can provide an indication of the informativeness over time (i.e., over the last three seconds). A green light can indicate that a significant number of image frames include informative frame and a yellow light can indicate that a given number of non-informative image frames has been detected (i.e., greater than a threshold value) ([0062]). Upon determining the image frames to be informative (i.e., having an informativeness score or any other suitable probability greater than a threshold value), these image frames can be transmitted to a polyp detection system automatically ([0099]-[0100]). Matsuzaki teaches a control unit (2, figure 1) with a feature region extracting unit (22, figure 1) and a detecting unit (23, figure 1). The detecting unit sets a condition of image to image change detection on the basis of feature amount of a feature region extracted by the feature-region extracting unit and calculates an image to image change amount, thereby detecting a change among a plurality of images ([0023]). The results are accumulated as an image to image change amount of the whole area of the image ([0027]). The calculated statistic is associated with a target image (k) as an image change amount of the image (k) ([0027]). The scene-change detecting unit (233, figure 1) detects a scene change image by comparative judgement of the statistic associated with each image and a predetermined threshold (S109, figure 2; [0028]). It would have been obvious to one of ordinary skill in the art before the time of filing to modify the apparatus of Saito to use a detection supporting section (33, figure 2) as taught by Imaizumi to switch between from the first mode to the second mode. Doing so would track a lesion candidate region L in observation images ([0040]). Further, it would have been obvious to modify the apparatus of Saito and Imaizumi to track informativeness (detection of a lesion) over time ([0062]) and automatically switch/transmit to a second mode based on if there is continuous detection ([0099]) as taught by Liang. Doing so would indicate if there is a significant number of image frames that include informative frames (i.e., frames with a lesion) and automatically switch/transmit to a second mode for further analysis ([0062]; [0099]; Liang) . Additionally, it would have been obvious to modify the apparatus to have a detecting unit (23, figure 1) as taught by Matsuzaki to determine if the amount of change in a feature exceeds a threshold ([0006]). Doing so would calculate an image-to-image change amount for a feature region ([0023]) and a scene change image by comparative judgement with a predetermined threshold ([0028]) . The modified apparatus would have in response to having detected the detection target, determine the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames (change amount…pixels…[0034]; Imaizumi), the consecutive frames being longer than a time range (same lesion candidate region…continuously…detected on a sequentially inputted plurality of observation images…[0040] | the Examiner interpreted there to be a time range corresponding to the number of sequentially inputted plurality of observation images), and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold (significant number of image frames includes informative frames [0062]; Liang); determine an amount of change of among positions of the detection target in the consecutive frames of the plurality of images (calculates an image-to-image change amount of each region…extracted feature region [0027]; Matsuzaki) ; determine whether the amount of change exceeds an amount of change threshold value (comparative judgement…an a predetermined threshold [0028]; Matsuzaki) ; and switch automatically from the first mode to the second mode in a case where the detection target is continuously detected (change amount…pixels…[0034]; Imaizumi) and the amount of change is within the amount of change threshold value (calculates an image-to-image change…[0027] | comparative judgement…an a predetermined threshold [0028]; Matsuzaki | the modified apparatus would have the amount of change within the amount of change threshold value in order to still detect the feature is in the scene) in a state where the first mode is used (automatically…[0099]; Liang). Regarding claim 18, Saito further discloses the one or more processors are configured to: in the first mode, acquire a first image which is illuminated by a first light of a first wavelength range among the plurality of images captured by the image sensor (s10, figure 16 | [0055]; Saito); and in the second mode, acquire a second image which is illuminated by a second light of a second wavelength range among the plurality of images captured by the image sensor (s12, figure 16 | [0055]), the second wavelength range being different from the first wavelength range (special observation mode…turns on the first blue laser light source and the second blue laser light source [0055]; see figure , the first and second blue laser light has different wavelength ranges). Regarding claim 19, Saito further discloses the first wavelength range is of white light (entirely visible light region [0058]); and the second wavelength range is of light different from the white light (special observation mode….alternately…[0055] | first blue laser light has a different wavelength range, see figure 4). Regarding claim 20, Saito further discloses the one or more processors are configured to display an index (broadly interpreted as a device that indicates a value or quantity | color table 87, figure 8; [0096]) indicating a region of interest of the observation target. Regarding claim 22, Saito further discloses the one or more processors are further configured to: perform classification of the detection target (color table 87, figure 8 [0096]); and according to a result of the classification of the detection target, display information indicating the result of the classification on the monitor (pseudo color…[0097]). Regarding claim 24, Saito discloses an endoscope system (10, figure 1) comprising: a light source (light source device 14, figure 1; 34 and 36, figure 2) configured to emit illumination light; an image sensor (48, figure 2) configured to capture a plurality of images of an observation target irradiated with the illumination light (s10, figure 16); and one or more processors (16, figures 1-2) configured to: perform detections of a detection target from the plurality of images captured by the image sensor to output results of the detections of the detection target for the plurality of images (see s11 and arrows from it, figure 16); according to results of the detections of the detection target, display information (broadly interpreted as any visual change on the display that indicates presence or absence of the detection target) indicating presence or absence of the detection target on a monitor so as to be superimposed (interpreted as to “place” something, Merriam-Webster definition) on the plurality of images displayed on the monitor or not to overlap with the plurality of images displayed on the monitor (s18, figure 16 | the oxygen saturation image indicates a lesion/target has been detected); display, in a first mode (normal observation mode s10, figure 16) and a second mode (special observation mode s12, figure 16), an image among the plurality of images to an observer (displayed…[0098]); switch from the first mode to the second mode (see s10 and s12, figure 16). Saito is silent regarding in response to having detected the detection target, determine the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames, the consecutive frames being longer than a time range, and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold; [[and]] determine an amount of change of among positions of the detection target in the consecutive frames of the plurality of images; determine whether the amount of change exceeds an amount of change threshold value; and switch automatically from the first mode to the second mode in a case where the detection target is continuously detected and the amount of change is within the amount of change threshold value in a state where the first mode is used. Imaizumi teaches an endoscope system (1, figure 1) with an endoscope (21, figure 1) and a video processor (31, figure 1) with a detection supporting section (33, figure 1). The detection supporting section (33, figures 1-2) has a detecting section (34, figure 2), a continuous detection determining section (35, figure 2), and a detection result output section (36, figure 2). The detecting section (34, figure 2) is a circuit to which the observation image of the subject is sequentially inputted, the circuit detecting a lesion candidate region L based on a predetermined feature value ([0032]). The predetermined feature value is calculated by calculating, for each of predetermined small regions on the observation image, a change amount or tilt value between respective pixels in the small region and pixels adjacent to the pixels ([0034]). The continuous detection determining section (35, figure 2) is a circuit that determines whether the lesion candidate region L is continuously detected, where a RAM (35, figure 2) stores lesion candidate information of an immediately preceding frame ([0039]). The lesion candidate region can be tracked and when the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images, the continuous detection determining section determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section (36, figure 2; [0040]). Liang teaches a process (100, figure 1a) for monitoring colonoscopic video quality and detecting polyps in colonoscopy ([0057]). For each image frame, an informativeness score is determined (quality of the image frame [0059]; 120, figure 1a). A traffic light indicator (174, figure 1b) can provide an indication of the informativeness over time (i.e., over the last three seconds). A green light can indicate that a significant number of image frames include informative frame and a yellow light can indicate that a given number of non-informative image frames has been detected (i.e., greater than a threshold value) ([0062]). Upon determining the image frames to be informative (i.e., having an informativeness score or any other suitable probability greater than a threshold value), these image frames can be transmitted to a polyp detection system automatically ([0099]-[0100]). Matsuzaki teaches a control unit (2, figure 1) with a feature region extracting unit (22, figure 1) and a detecting unit (23, figure 1). The detecting unit sets a condition of image to image change detection on the basis of feature amount of a feature region extracted by the feature-region extracting unit and calculates an image to image change amount, thereby detecting a change among a plurality of images ([0023]). The results are accumulated as an image to image change amount of the whole area of the image ([0027]). The calculated statistic is associated with a target image (k) as an image change amount of the image (k) ([0027]). The scene-change detecting unit (233, figure 1) detects a scene change image by comparative judgement of the statistic associated with each image and a predetermined threshold (S109, figure 2; [0028]). It would have been obvious to one of ordinary skill in the art before the time of filing to modify the system of Saito to use a detection supporting section (33, figure 2) as taught by Imaizumi to switch between from the first mode to the second mode. Doing so would track a lesion candidate region L in observation images ([0040]). Further, it would have been obvious to modify the system of Saito and Imaizumi to track informativeness (detection of a lesion) over time ([0062]) and automatically switch/transmit to a second mode based on if there is continuous detection ([0099]) as taught by Liang. Doing so would indicate if there is a significant number of image frames that include informative frames (i.e., frames with a lesion) and automatically switch/transmit to a second mode for further analysis ([0062]; [0099]; Liang) . Additionally, it would have been obvious to modify the system to have a detecting unit (23, figure 1) as taught by Matsuzaki to determine if the amount of change in a feature exceeds a threshold ([0006]). Doing so would calculate an image-to-image change amount for a feature region ([0023]) and a scene change image by comparative judgement with a predetermined threshold ([0028]) . The modified system would have in response to having detected the detection target, determine the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames (change amount…pixels…[0034]; Imaizumi), the consecutive frames being longer than a time range (same lesion candidate region…continuously detected on a sequentially inputted plurality of observation images…[0040] | the Examiner interpreted there to be a time range corresponding to the number of sequentially inputted plurality of observation images), and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold (significant number of image frames includes informative frames [0062]; Liang); determine an amount of change of among positions of the detection target in the consecutive frames of the plurality of images (calculates an image-to-image change amount of each region…extracted feature region [0027]; Matsuzaki) ; determine whether the amount of change exceeds an amount of change threshold value (comparative judgement…an a predetermined threshold [0028]; Matsuzaki) ; and switch automatically from the first mode to the second mode in a case where the detection target is continuously detected (change amount…pixels…[0034]; Imaizumi) and the amount of change is within the amount of change threshold value (calculates an image-to-image change…[0027] | comparative judgement…an a predetermined threshold [0028]; Matsuzaki | the modified apparatus would have the amount of change within the amount of change threshold value in order to still detect the feature is in the scene) in a state where the first mode is used (automatically…[0099]; Liang). Regarding claim 25, Saito further discloses the one or more processors are configured to: in the first mode, acquire a first image which is illuminated by a first light of a first wavelength range among the plurality of images captured by the image sensor (s10, figure 16 | [0055]; Saito); and in the second mode, acquire a second image which is illuminated by a second light of a second wavelength range among the plurality of images captured by the image sensor (s12, figure 16 | [0055]), the second wavelength range being different from the first wavelength range (special observation mode…turns on the first blue laser light source and the second blue laser light source [0055]; see figure 4, the first and second blue laser light has different wavelength ranges). Regarding claim 26, Saito further discloses the first wavelength range is of white light (entirely visible light region [0058]); and the second wavelength range is of light different from the white light (special observation mode…alternately…[0055] | first blue laser light has a different wavelength range, see figure 4). Regarding claim 27, Saito further discloses the one or more processors are configured to display an index (broadly interpreted as a device that indicates a value or quantity | color table 87, figure 8; [0096]) indicating a region of interest of the observation target. Regarding claim 29, Saito further discloses the one or more processors are further configured to: perform classification of the detection target (color table 87, figure 8; [0096]); and according to a result of the classification of the detection target, display information indicating the result of the classification on the monitor (pseudo color…[0097]). Regarding claim 31, Saito discloses a method of operating an endoscope system (10, figure 1) including a light source (light source device 14, figure 1; 34 and 36, figure 2) configured to emit illumination light, an image sensor (48, figure 2), and one or more processors (16, figures 1-2), the method comprising; by the image sensor, capturing a plurality of images of an observation target irradiated with the illumination light (s10, figure 16); by the one or more processors, performing detections of a detection target from the plurality of images captured by the image sensor to output results of the detections of the detection target for the plurality of images (s11 and arrows from it, figure 16); by the one or more processors, according to results of the detections of the detection target, displaying information (broadly interpreted as any visual change on the display that indicates presence or absence of the detection target) indicating presence or absence of the detection target on a monitor so as to be superimposed (interpreted as to “place” something, Merriam-Webster definition) on the plurality of images displayed on the monitor or not to overlap with the plurality of images displayed on the monitor (s18, figure 16 | the oxygen saturation image indicates a lesion/target has been detected); by the one or more processors, displaying, in a first mode (normal observation mode s10, figure 16) and a second mode (special observation mode s12, figure 16), an image among the plurality of images to an observer (displayed…[0098]); switching from the first mode to the second mode (see s10 and s12, figure 16). Saito is silent regarding by the one or more processors, determining the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames, the consecutive frames being longer than a time range, and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold in response to having detected the detection target; by the one or more processors, determining an amount of change of among positions of the detection target in the consecutive frames of the plurality of images; by the one or more processors, determining whether the amount of change exceeds an amount of change threshold value; and by the one or more processors, switching automatically from the first mode to the second mode in a case where the detection target is continuously detected and the amount of change is within the amount of change threshold value in a state where the first mode is used. Imaizumi teaches an endoscope system (1, figure 1) with an endoscope (21, figure 1) and a video processor (31, figure 1) with a detection supporting section (33, figure 1). The detection supporting section (33, figures 1-2) has a detecting section (34, figure 2), a continuous detection determining section (35, figure 2), and a detection result output section (36, figure 2). The detecting section (34, figure 2) is a circuit to which the observation image of the subject is sequentially inputted, the circuit detecting a lesion candidate region L based on a predetermined feature value ([0032]). The predetermined feature value is calculated by calculating, for each of predetermined small regions on the observation image, a change amount or tilt value between respective pixels in the small region and pixels adjacent to the pixels ([0034]). The continuous detection determining section (35, figure 2) is a circuit that determines whether the lesion candidate region L is continuously detected, where a RAM (35, figure 2) stores lesion candidate information of an immediately preceding frame ([0039]). The lesion candidate region can be tracked and when the same lesion candidate region L is continuously or intermittently detected on a sequentially inputted plurality of observation images, the continuous detection determining section determines that the detection of the lesion candidate region L continues and outputs a determination result to the detection-result output section (36, figure 2; [0040]). Liang teaches a process (100, figure 1a) for monitoring colonoscopic video quality and detecting polyps in colonoscopy ([0057]). For each image frame, an informativeness score is determined (quality of the image frame [0059]; 120, figure 1a). A traffic light indicator (174, figure 1b) can provide an indication of the informativeness over time (i.e., over the last three seconds). A green light can indicate that a significant number of image frames include informative frame and a yellow light can indicate that a given number of non-informative image frames has been detected (i.e., greater than a threshold value) ([0062]). Upon determining the image frames to be informative (i.e., having an informativeness score or any other suitable probability greater than a threshold value), these image frames can be transmitted to a polyp detection system automatically ([0099]-[0100]). Matsuzaki teaches a control unit (2, figure 1) with a feature region extracting unit (22, figure 1) and a detecting unit (23, figure 1). The detecting unit sets a condition of image to image change detection on the basis of feature amount of a feature region extracted by the feature-region extracting unit and calculates an image to image change amount, thereby detecting a change among a plurality of images ([0023]). The results are accumulated as an image to image change amount of the whole area of the image ([0027]). The calculated statistic is associated with a target image (k) as an image change amount of the image (k) ([0027]). The scene-change detecting unit (233, figure 1) detects a scene change image by comparative judgement of the statistic associated with each image and a predetermined threshold (S109, figure 2; [0028]). It would have been obvious to one of ordinary skill in the art before the time of filing to modify the method of Saito to use a detection supporting section (33, figure 2) as taught by Imaizumi to switch between from the first mode to the second mode. Doing so would track a lesion candidate region L in observation images ([0040]). Further, it would have been obvious to modify the method of Saito and Imaizumi to track informativeness (detection of a lesion) over time ([0062]) and automatically switch/transmit to a second mode based on if there is continuous detection ([0099]) as taught by Liang. Doing so would indicate if there is a significant number of image frames that include informative frames (i.e., frames with a lesion) and automatically switch/transmit to a second mode for further analysis ([0062]; [0099]; Liang) . Additionally, it would have been obvious to modify the method to have a detecting unit (23, figure 1) as taught by Matsuzaki to determine if the amount of change in a feature exceeds a threshold ([0006]). Doing so would calculate an image-to-image change amount for a feature region ([0023]) and a scene change image by comparative judgement with a predetermined threshold ([0028]) . The modified method would comprise by the one or more processors, determining the detection target as being continuously detected in response to detecting that a pixel or a group of pixels having the detection target is present in consecutive frames (change amount…pixels…[0034]; Imaizumi), the consecutive frames being longer than a time range (same lesion candidate region…continuously…detected on a sequentially inputted plurality of observation images…[0040] | the Examiner interpreted there to be a time range corresponding to the number of sequentially inputted plurality of observation images), and a rate of the detection target being detected in the plurality of images is greater than or is equal to a threshold in response to having detected the detection target (significant number of image frames includes informative frames [0062]; Liang); by the one or more processors, determining an amount of change of among positions of the detection target in the consecutive frames of the plurality of images (calculates an image-to-image change amount of each region…extracted feature region [0027]; Matsuzaki) ; by the one or more processors, determining whether the amount of change exceeds an amount of change threshold value (comparative judgement…an a predetermined threshold [0028]; Matsuzaki) ; and by the one or more processors, switching automatically from the first mode to the second mode in a case where the detection target is continuously detected (change amount…pixels…[0034]; Imaizumi) and the amount of change is within the amount of change threshold value (calculates an image-to-image change…[0027] | comparative judgement…an a predetermined threshold [0028]; Matsuzaki | the modified apparatus would have the amount of change within the amount of change threshold value in order to still detect the feature is in the scene) in a state where the first mode is used (automatically…[0099]; Liang). Regarding claim 32, Saito further discloses in the first mode, the one or more processors acquire a first image which is illuminated by a first light of a first wavelength range among the plurality of images captured by the image sensor (s10, figure 16 | [0055]; Saito); and in the second mode, the one or more processors acquire a second image which is illuminated by a second light of a second wavelength range among the plurality of images captured by the image sensor (s12, figure 16 | [0055]), the second wavelength range being different from the first wavelength range (special observation mode…turns on the first blue laser light source and the second blue laser light source [0055]; see figure , the first and second blue laser light has different wavelength ranges). Regarding claim 33, Saito further discloses the first wavelength range is of white light (entirely visible light region [0058]); and the second wavelength range is of light different from the white light (special observation mode….alternately…[0055] | first blue laser light has a different wavelength range, see figure 4). Regarding claim 34, Saito further discloses by the one or more processors, displaying an index (broadly interpreted as a device that indicates a value or quantity | color table 87, figure 8; [0096]) indicating a region of interest of the observation target. Regarding claim 36, Saito further discloses by the one or more processors, performing classification of the detection target (color table 87, figure 8 [0096]); and by the one or more processors, according to a result of the classification of the detection target, displaying information indicating the result of the classification on the monitor (pseudo color…[0097]) . 07-21-aia AIA Claim (s) 38, 40, and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0238086) and Imaizumi (US 2018/0249900) and Liang (US 2018/0225820) and Matsuzaki (US 2010/0277650) as applied to claim 17 (for claim 38) and claim 24 (for claim 40) and claim 31 (for claim 42), in further view of Fukazawa (US 2021/0169305, priority date back to PCT/JP2018/031471) . Regarding claims 38 and 40, Saito and Imaizumi and Liang and Matsuzaki disclose all of the features in the current invention as shown above in claims 17 and 24. They are silent regarding the one or more processors are configured to, in a state where the second mode is used after switching from the first mode to the second mode, switch from the second mode to the first mode in response to an elapse of a certain period of time after the switching from the first mode to the second mode. Fukazawa teaches an endoscope (5001, figure 1) connected to light source apparatus (5043, figure 1) and CCU (5039, figure 1). A user can input instruction to change imaging conditions, like a type of illumination light, of the endoscope ([0060]). The light source apparatus is able to supply light in a predetermined wavelength band corresponding to special light observation and white light for normal observation ([0075]). It is possible to switch between a timing to apply white light and a timing to apply excitation light by switching between types of light sources ([0109]). It would have been obvious to one of ordinary skill in the art before the time of filing to modify the apparatus/system to switch between the second mode to the first mode using the timing of the applied light sources ([0109]) as taught by Fukazawa. Doing so would provide an alternative method of switching between the light modes ([0109]). The modified apparatus/system would have the one or more processors are configured to, in a state where the second mode is used after switching from the first mode to the second mode, switch from the second mode to the first mode in response to an elapse of a certain period of time after the switching from the first mode to the second mode (switching between a timing…[0109]; Fukazawa). Regarding claim 42, Saito and Imaizumi and Liang and Matsuzaki disclose all of the features in the current invention as shown above in claim 31. They are silent regarding by the one or more processors, in a state where the second mode is used after switching from the first mode to the second mode, switching from the second mode to the first mode in response to an elapse of a certain period of time after the switching from the first mode to the second mode. Fukazawa teaches an endoscope (5001, figure 1) connected to light source apparatus (5043, figure 1) and CCU (5039, figure 1). A user can input instruction to change imaging conditions, like a type of illumination light, of the endoscope ([0060]). The light source apparatus is able to supply light in a predetermined wavelength band corresponding to special light observation and white light for normal observation ([0075]). It is possible to switch between a timing to apply white light and a timing to apply excitation light by switching between types of light sources ([0109]). It would have been obvious to one of ordinary skill in the art before the time of filing to modify the method of using the apparatus/system to switch between the second mode to the first mode using the timing of the applied light sources ([0109]) as taught by Fukazawa. Doing so would provide an alternative method of switching between the light modes ([0109]). The modified method would comprise by the one or more processors, in a state where the second mode is used after switching from the first mode to the second mode, switching from the second mode to the first mode in response to an elapse of a certain period of time after the switching from the first mode to the second mode (switching between a timing…[0109]; Fukazawa) . 07-21-aia AIA Claim (s) 39, 41, and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Saito (US 2015/0238086) and Imaizumi (US 2018/0249900) and Liang (US 2018/0225820) and Matsuzaki (US 2010/0277650) as applied to claim 19 (for claim 39) and claim 26 (for claim 41) and claim 33 (for claim 43), in further view of Ganapati (US 2018/0247153) . Regarding claim 39 and 41, Saito and Imaizumi and Liang and Matsuzaki disclose all of the features in the current invention as shown above for claim 19 and 26. They are silent regarding the one or more processors are configured to: use a learning model learned using a first-light image which is illuminated by the first light when the first image is to be input; and apply a learning model learned using a second- light image which is illuminated by the second light when the second image is to be input. Ganapati teaches an endoscope controller (252, figure 2) that performs machine learning model training, where the MLM training engine (270, figure 2) is communicably coupled with an imager (not shown; [0033]). The imager is responsible for supply training data to the MLM training engine ([0033]). The MLM training engine trains a machine learning model, such as a neural network, for differentiating tissue types of interest ([0033]) or highlight areas believed to be certain tissue types ([0015]). There are one or more MLM models trained by the system and stored in the MLM data store (280, figure 2; [0032]). It would have been obvious to modify the apparatus/system of Saito and Imaizumi and Liang and Matsuzaki to have an endoscope controller (252, figure 2) with an MLM analysis engine (265, figure 2), an MLM training engine (270, figure 2), and an MLM data store (280, figure 2) to train and/or use machine learning to identify areas of certain tissue types taught by Ganapati ([0014-0015]). Doing so would provide training on and/or differentiation of tissue types ([0033]). The modified apparatus/system would have one or more processors (252, figure 2; Ganapati) are configured to: use a learning model learned using a first-light image which is illuminated by the first light when the first image is to be input ([0032]); and apply a learning model learned using a second-light image which is illuminated by the second light when the second image is to be input (MLM models store…[0032]). Regarding claim 43, Saito and Imaizumi and Liang and Matsuzaki disclose all of the features in the current invention as shown above for claim 33. They are silent regarding by the one or more processors, using a learning model learned using a first-light image which is illuminated by the first light when the first image is to be input; and by the one or more processors, applying a learning model learned using a second-light image which is illuminated by the second light when the second image is to be input. Ganapati teaches an endoscope controller (252, figure 2) that performs machine learning model training, where the MLM training engine (270, figure 2) is communicably coupled with an imager (not shown; [0033]). The imager is responsible for supply training data to the MLM training engine ([0033]). The MLM training engine trains a machine learning model, such as a neural network, for differentiating tissue types of interest ([0033]) or highlight areas believed to be certain tissue types ([0015]). There are one or more MLM models trained by the system and stored in the MLM data store (280, figure 2; [0032]). It would have been obvious to modify the method of using the apparatus/system of Saito and Imaizumi and Liang and Matsuzaki to have an endoscope controller (252, figure 2) with an MLM analysis engine (265, figure 2), an MLM training engine (270, figure 2), and an MLM data store (280, figure 2) to train and/or use machine learning to identify areas of certain tissue types taught by Ganapati ([0014-0015]). Doing so would provide training on and/or differentiation of tissue types ([0033]). The modified method would comprise by the one or more processors, using a learning model learned using a first-light image which is illuminated by the first light when the first image is to be input ([0032]); and by the one or more processors, applying a learning model learned using a second-light image which is illuminated by the second light when the second image is to be input (MLM models store…[0032]). 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 PAMELA F WU whose telephone number is (571)272-9851. The examiner can normally be reached M-F: 8-4 PM. 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, Michael Carey can be reached at 571-270-7235. 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. PAMELA F. WU Examiner Art Unit 3795 May 29, 2026 /RYAN N HENDERSON/Primary Examiner, Art Unit 3795 Application/Control Number: 17/192,830 Page 2 Art Unit: 3795 Application/Control Number: 17/192,830 Page 3 Art Unit: 3795 Application/Control Number: 17/192,830 Page 4 Art Unit: 3795 Application/Control Number: 17/192,830 Page 5 Art Unit: 3795 Application/Control Number: 17/192,830 Page 6 Art Unit: 3795 Application/Control Number: 17/192,830 Page 7 Art Unit: 3795 Application/Control Number: 17/192,830 Page 8 Art Unit: 3795 Application/Control Number: 17/192,830 Page 9 Art Unit: 3795 Application/Control Number: 17/192,830 Page 10 Art Unit: 3795 Application/Control Number: 17/192,830 Page 11 Art Unit: 3795 Application/Control Number: 17/192,830 Page 12 Art Unit: 3795 Application/Control Number: 17/192,830 Page 13 Art Unit: 3795 Application/Control Number: 17/192,830 Page 14 Art Unit: 3795 Application/Control Number: 17/192,830 Page 15 Art Unit: 3795 Application/Control Number: 17/192,830 Page 16 Art Unit: 3795 Application/Control Number: 17/192,830 Page 17 Art Unit: 3795 Application/Control Number: 17/192,830 Page 18 Art Unit: 3795 Application/Control Number: 17/192,830 Page 19 Art Unit: 3795 Application/Control Number: 17/192,830 Page 20 Art Unit: 3795 Application/Control Number: 17/192,830 Page 21 Art Unit: 3795 Application/Control Number: 17/192,830 Page 22 Art Unit: 3795 Application/Control Number: 17/192,830 Page 23 Art Unit: 3795 Application/Control Number: 17/192,830 Page 24 Art Unit: 3795 Application/Control Number: 17/192,830 Page 25 Art Unit: 3795
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