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 .
Response to Amendment
The amendment filed on 4/2/2026 has been entered. Claims 1, 7-15, and 19-26 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection, 101 rejection, and 112 rejection previously set forth in the Non-Final Office Action mailed 1/2/2026.
Response to Arguments
Applicant’s arguments, see pages 10-12, filed 4/2/2026, with respect to the rejection under 35 USC § 102 of Claims 1, 24, and 26, have been fully considered and are persuasive in light of Applicant’s amendments. Applicant amended the independent claim with newly added limitations. Such newly added limitations change the scope of the claims, render the previous 102 rejections identified in the Non-Final Office Action dated 1/2/2026 moot, and require a new ground of rejection.
Therefore, the 102 rejections previously identified in the non-final action dated 1/2/2026 have been withdrawn.
However, upon further search and consideration, a new ground of rejection is made. Please see section 35 USC § 103 below for further explanation.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The present rejection(s) reference specific passages from cited prior art. However, Applicant is advised that the rejections are based on the entirety of each cited prior art. That is, each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI)) Therefore, Applicant is advised to review all portions of the cited prior art if traversing a rejection based on the cited prior art.
Claims 1, 9-12, 15, and 19-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2024/0242345 A1 to Chen et al. (“Chen”) in view of U.S. Patent Appl. Publ. No. 2019/0370971 A1 to Saikou et al. (“Saikou”).
Regarding claim 1, Chen discloses a medical support device (an auxiliary evaluation system 10 is adapted to be electrically connected to a detection instrument 16; Fig. 1, paragraph 0015) comprising:
a processor (the auxiliary evaluation system 10 includes a computing device 12; Fig. 1, paragraph 0015),
wherein an actual size estimation Al is operated to estimate an actual size of a region to be observed (in the auxiliary evaluation system and method in the disclosure, after real-time image is obtained, the size of the abnormal feature on the real-time image is analyzed and evaluated through artificial intelligence (AI); Fig. 2, paragraph 0025) which has been recognized by performing a recognition process on a medical image including the region to be observed (the computing device 12 further includes an anomaly detection model 124 to detect the abnormal feature 22 through the anomaly detection model 124, and mark a selection box 24 around the abnormal feature 22; Fig. 1-2, paragraph 0016),
wherein a recognition result obtained by the recognition process includes geometric characteristics of the region to be observed in the medical image (as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the section box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and a position information corresponding to the abnormal feature 22 through calculation; Fig. 2, paragraph 0017), and
wherein the geometric characteristics include a position and/or an approximate size of the region to be observed in the medical image (as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the section box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and a position information corresponding to the abnormal feature 22 through calculation; Fig. 2, paragraph 0017), and
the processor is configured to:
operate the actual size estimation AI to estimate the actual size of the region to be observed (as shown in step S18, the size prediction model 123 in the computing device 12 calculates a size of the corresponding abnormal feature 22 according to the position information and depth; Fig. 1-2, paragraph 0017)
output the actual size of the region to be observed,
wherein the output of the actual size of the region to be observed is implemented by displaying the actual size of the region to be observed on a screen (as shown in step S20, the computing device 12 adds the bounding box 26 and the size of the abnormal feature 22 to the real-time image 18 and outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size; Fig. 2-3, paragraph 0017).
However, Chen does not explicitly disclose wherein the processor is configured to operate the actual size estimation AI to estimate the actual size of the region to be observed in a case where an amount of change in the position over time is equal to or less than a first threshold value, in a case where an amount of change in the approximate size over time is equal to or less than a second threshold value, in a case where a first value obtained by smoothing the amount of change in the position over time is equal to or less than the first threshold value, and/or in a case where a second value obtained by smoothing the amount of change in the approximate size over time is equal to or less than the second threshold value.
Saikou teaches the processor is configured to operate tumor judgement processing in a case where an amount of change in the position over time is equal to or less than a first threshold value (in a case where the optical flow (movement vector) calculated in S104B is equal to or less than the first threshold, it is possible to device that the doctor is observing the part in the imaging range. In this case, it is possible for the first processing unit 110 to decide that “the tumor judgement process with respect to the input images is necessary; Fig. 4, paragraph 0076). Saikou teaches that performing the image processing only on images in which the optical flow is below a threshold helps to speed up the tumor judgment process (paragraph 0044).
Saikou is considered to be analogous to the claimed invention because it is in the same field of image processing on endoscopic images. It would have been prima facie obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the medical support device of Chen to incorporate the teachings of Saikou by operating actual size estimation AI in a case where an amount of change in the position over time is equal to or less than a first threshold value. Doing so would help to speed up the actual size estimation AI, as recognized by Saikou.
Regarding claim 9, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the recognition process is a process that uses an object recognition AI (the segmentation model 121 is a neural network model; Fig. 1, paragraph 0020) using a bounding box method (after the selection box 24 is detected by the segmentation model 121, an exact contour of the abnormal feature 22 is segmented by the segmentation model 121, as shown in Fig. 5B, to mark a bounding box; Fig. 2 and 5B, paragraph 0020), and
the approximate size is a bounding box size that is applied to the region to be observed by the object recognition AI (during an endoscopic diagnosis process, when the abnormal feature 22 is found through the anomaly detection model 124 or the abnormal feature 22 is found manually by the doctor, the computing device 12 displays the size (the width W and the height H) of the bounding box 26 on the real-time image 18; Fig. 4, paragraph 0017).
Regarding claim 10, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the geometric characteristics include the position (position information of the abnormal feature 22, including (x.sub.1, y.sub.1), (x.sub.2, y.sub.2), (x.sub.3, y.sub.3), and (x.sub.4, y.sub.4), is obtained by calculating the bounding box 26; Fig. 5B, paragraph 0020), and
the processor is configured to operate the actual size estimation AI on a condition that the position is present in a closed region surrounding a portion of the medical image (the computing device 12 further includes an anomaly detection model 124, to detect the abnormal feature 22 through the anomaly detection model 124, and mark a selection box 24 around the abnormal feature; Fig. 3, paragraph 0016). As shown in Fig. 2, the size of the abnormal feature is calculated using AI after the abnormal feature is present in a closed region of the medical image.
Regarding claim 11, Chen, as previously modified by Saikou, discloses the medical support device according to claims 1 and 10. Chen further discloses wherein the closed region is set in a central portion of the medical image (the selection box 24 can be located in a central portion of the medical image such as the portion indicated in annotated Fig. 5A below; Fig. 5A).
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Regarding claim 12, Chen, as previously modified by Saikou, discloses the medical support device according to claims 1 and 10. Chen further discloses wherein the closed region is set in the medical image in response to a given instruction (a doctor manually marks the selection box 24 around the abnormal feature 22 through the computing device 12 to circle the abnormal feature; paragraph 0016).
Regarding claim 15, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the processor is configured to perform the recognition process on the medical image (the computing device 12 further includes an anomaly detection model 124 to detect the abnormal feature 22 through the anomaly detection model 124, and mark a selection box 24 around the abnormal feature 22; Fig. 1-2, paragraph 0016).
Regarding claim 19, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the processor is configured to output operating state information that is capable of specifying an operating state of the actual size estimation AI (finally, as shown in step S20, the computing device 12 adds the bounding box 26 and the size of the abnormal feature 22 to the real-time image 18 and outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size; Fig. 4, paragraph 0017). When the operating state of the actual size estimation AI is “on”, the computing device outputs the labeled real-time image. When the operating state of the actual size estimation AI is “off”, the computing device does not output the labeled real-time image.
Regarding claim 20, Chen, as previously modified by Saikou, discloses the medical support device according to claims 1 and 19. Chen further discloses wherein the output of the operating state information is implemented by displaying the operating state information on a screen (finally, as shown in step S20, the computing device 12 adds the bounding box 26 and the size of the abnormal feature 22 to the real-time image 18 and outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size; Fig. 4, paragraph 0017).
Regarding claim 21, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the medical image is an endoscope image captured by an endoscope (the detection instrument 16 is an endoscopic system, such as a colonoscopy detection instrument; Fig. 1, paragraph 0015).
Regarding claim 22, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses wherein the region to be observed is a lesion (the abnormal feature 22 includes hyperplastic tissue or diseased tissue of the target 20, that is, polyps, tumors, or other formations generated on the target issue; Fig. 3, paragraph 0018).
Regarding claim 23, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. Chen further discloses an endoscope system comprising:
the medical support device according to claim 1 (see rejection of claim 1 above); and
an endoscope that images the region to be observed (the detection instrument 16 is an endoscopic system, such as a colonoscopy detection instrument. In this case, the target 20 is intestinal target; Fig. 1, paragraph 0015).
Regarding claim 24, Chen discloses a medical support method comprising:
operating an actual size estimation AI to estimate an actual size of a region to be observed which has been recognized by performing a recognition process on a medical image including the region to be observed (in the auxiliary evaluation system and method in the disclosure, after real-time image is obtained, the size of the abnormal feature on the real-time image is analyzed and evaluated through artificial intelligence (AI); Fig. 2, paragraph 0025), wherein a recognition result obtained by the recognition process includes geometric characteristics of the region to be observed in the medical image (as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the section box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and a position information corresponding to the abnormal feature 22 through calculation; Fig. 2, paragraph 0017), and wherein the geometric characteristics include a position and/or an approximate size of the region to be observed in the medical image (as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the section box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and a position information corresponding to the abnormal feature 22 through calculation; Fig. 2, paragraph 0017);
operating the actual size estimation AI to estimate the actual size of the region to be observed (as shown in step S18, the size prediction model 123 in the computing device 12 calculates a size of the corresponding abnormal feature 22 according to the position information and depth; Fig. 1-2, paragraph 0017); and
outputting the actual size of the region to be observed,
wherein the output of the actual size of the region to be observed is implemented by displaying the actual size of the region to be observed on a screen (as shown in step S20, the computing device 12 adds the bounding box 26 and the size of the abnormal feature 22 to the real-time image 18 and outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size; Fig. 2-3, paragraph 0017).
However, Chen does not explicitly disclose wherein the processor is configured to operate the actual size estimation AI to estimate the actual size of the region to be observed in a case where an amount of change in the position over time is equal to or less than a first threshold value, in a case where an amount of change in the approximate size over time is equal to or less than a second threshold value, in a case where a first value obtained by smoothing the amount of change in the position over time is equal to or less than the first threshold value, and/or in a case where a second value obtained by smoothing the amount of change in the approximate size over time is equal to or less than the second threshold value.
Saikou teaches the processor is configured to operate tumor judgement processing in a case where an amount of change in the position over time is equal to or less than a first threshold value (in a case where the optical flow (movement vector) calculated in S104B is equal to or less than the first threshold, it is possible to device that the doctor is observing the part in the imaging range. In this case, it is possible for the first processing unit 110 to decide that “the tumor judgement process with respect to the input images is necessary; Fig. 4, paragraph 0076). Saikou teaches that performing the image processing only on images in which the optical flow is below a threshold helps to speed up the tumor judgment process (paragraph 0044).
Saikou is considered to be analogous to the claimed invention because it is in the same field of image processing on endoscopic images. It would have been prima facie obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the medical support device of Chen to incorporate the teachings of Saikou by operating actual size estimation AI in a case where an amount of change in the position over time is equal to or less than a first threshold value. Doing so would help to speed up the actual size estimation AI, as recognized by Saikou.
Regarding claim 25, Chen, as previously modified by Saikoum, discloses the medical support method according to claim 24. Chen further discloses using an endoscope that images the region to be observed (the detection instrument 16 is an endoscopic system, such as a colonoscopy detection instrument. In this case, the target 20 is intestinal target; Fig. 1, paragraph 0015).
Regarding claim 26, Chen discloses a non-transitory computer-readable storage medium storing a program executable by a computer to execute a medical support process comprising:
operating an actual size estimation AI to estimate an actual size of a region to be observed which has been recognized by performing a recognition process on a medical image including the region to be observed (in the auxiliary evaluation system and method in the disclosure, after real-time image is obtained, the size of the abnormal feature on the real-time image is analyzed and evaluated through artificial intelligence (AI); Fig. 2, paragraph 0025), wherein a recognition result obtained by the recognition process includes geometric characteristics of the region to be observed in the medical image, and wherein the geometric characteristics include a position and/or an approximate size of the region to be observed in the medical image (as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the section box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and a position information corresponding to the abnormal feature 22 through calculation; Fig. 2, paragraph 0017);
operating the actual size estimation AI to estimate the actual size of the region to be observed (as shown in step S18, the size prediction model 123 in the computing device 12 calculates a size of the corresponding abnormal feature 22 according to the position information and depth; Fig. 1-2, paragraph 0017); and
outputting the actual size of the region to be observed,
wherein the output of the actual size of the region to be observed is implemented by displaying the actual size of the region to be observed on a screen (as shown in step S20, the computing device 12 adds the bounding box 26 and the size of the abnormal feature 22 to the real-time image 18 and outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size; Fig. 2-3, paragraph 0017).
However, Chen does not explicitly disclose wherein the processor is configured to operate the actual size estimation AI to estimate the actual size of the region to be observed in a case where an amount of change in the position over time is equal to or less than a first threshold value, in a case where an amount of change in the approximate size over time is equal to or less than a second threshold value, in a case where a first value obtained by smoothing the amount of change in the position over time is equal to or less than the first threshold value, and/or in a case where a second value obtained by smoothing the amount of change in the approximate size over time is equal to or less than the second threshold value.
Saikou teaches the processor is configured to operate tumor judgement processing in a case where an amount of change in the position over time is equal to or less than a first threshold value (in a case where the optical flow (movement vector) calculated in S104B is equal to or less than the first threshold, it is possible to device that the doctor is observing the part in the imaging range. In this case, it is possible for the first processing unit 110 to decide that “the tumor judgement process with respect to the input images is necessary; Fig. 4, paragraph 0076). Saikou teaches that performing the image processing only on images in which the optical flow is below a threshold helps to speed up the tumor judgment process (paragraph 0044).
Saikou is considered to be analogous to the claimed invention because it is in the same field of image processing on endoscopic images. It would have been prima facie obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the medical support device of Chen to incorporate the teachings of Saikou by operating actual size estimation AI in a case where an amount of change in the position over time is equal to or less than a first threshold value. Doing so would help to speed up the actual size estimation AI, as recognized by Saikou.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Chen as applied to Saikou as applied to claim 1 above, and further in view of U.S. Patent Appl. Publ. No. 2020/0129042 A1 to Takahashi et al. (“Takahashi”).
Regarding claim 13, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. However, Chen, as previously modified by Saikou, does not explicitly disclose wherein a plurality of the regions to be observed are included in the medical image,
the recognition process is a process of recognizing each of the plurality of regions to be observed which are included in the medical image,
the recognition result is obtained for each of the plurality of regions to be observed,
the actual size estimation AI is operated for each of the plurality of regions to be observed which have been recognized by performing the recognition process on the medical image, and
the processor is configured to operate the actual size estimation AI for each of the plurality of regions to be observed according to the recognition result for each of the plurality of regions to be observed.
Takahashi teaches wherein a plurality of the regions to be observed are included in the medical image, the recognition process is a process of recognizing each of the plurality of regions to be observed which are included in the medical image, and the recognition result is obtained for each of the plurality of regions to be observed (note that in a case where a plurality of abnormal regions 30 are detected from one video frame 14, the above processing is performed for each of the plurality of abnormal regions; Fig. 1, paragraph 0094).
Takahashi is considered to be analogous to the claimed invention because it is in the same field of endoscope systems capable of image recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have performed the recognition process and the size estimation process of Chen, as modified by Saikou, on multiple regions of interest in an image, as taught by Takahashi, to increase the functionality of the endoscope of Chen by enabling multiple regions of interest to be processed.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Chen as applied to Saikou as applied to claim 1 above, and further in view of U.S. Patent Appl. Publ. No. 2025/0209776 A1 to Iwadate et al. (“Iwadate”).
Regarding claim 14, Chen, as previously modified by Saikou, discloses the medical support device according to claim 1. However, Chen, as modified by Saikou, does not explicitly disclose wherein, in a case where there is a time period in which the recognition result is not obtained by the recognition process, the recognition result in the time period is interpolated on the basis of the recognition result obtained by the recognition process before and/or after the time period.
Iwadate teaches wherein, in a case where there is a time period in which the recognition result is not obtained by the recognition process, the recognition result in the time period is interpolated on the basis of the recognition result obtained by the recognition process before and/or after the time period (the endoscopic image Ia is an image captured at predetermined time intervals in at least one of the insertion process of the endoscope 3 to the subject and/or the ejection process of the endoscope 3 from the subject. In the present example embodiment, the image processing device 1 analyses the endoscopic image Ia, and displays the information on the detection result on the display device 2; Fig. 2, paragraph 0035). Iwadate teaches a recognition process performed on selected images at a predetermined time interval which could include every 10th image. As such, the recognition result is not obtained for all images and the recognition result can be interpolated.
Iwadate is considered to be analogous to the claimed invention because it is in the same field of endoscope systems capable of image recognition. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to perform the image processing of Chen, as modified by Saikou, on images taken at a predetermined time interval, as taught by Iwadate, to increase the functionality of the endoscope of Chen by lowering power consumption.
Allowable Subject Matter
Claims 7-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 7, Chen, as previously modified Saikou, discloses the medical support device according to claim 1. However, Chen, as previously modified Saikou, does explicitly teach wherein the first value is a moving average value of the amount of change in the position over time. Chen and Saikou do not explicitly teach wherein the processor is configured to operate the actual size estimation AI to estimate the actual size of the region to be observed in a case where in a case where a first value obtained by smoothing the amount of change in the position over time is equal to or less than the first threshold value, and/or in a case where a second value obtained by smoothing the amount of change in the approximate size over time is equal to or less than the second threshold value.
Regarding claim 8, Chen, as previously modified Saikou, discloses the medical support device according to claim 1. However, Chen, as previously modified Saikou, does explicitly teach wherein the second value is a moving average value of the amount of change in the approximate size over time. Chen and Saikou do not explicitly teach wherein the processor is configured to operate the actual size estimation AI to estimate the actual size of the region to be observed in a case where in a case where a first value obtained by smoothing the amount of change in the position over time is equal to or less than the first threshold value, and/or in a case where a second value obtained by smoothing the amount of change in the approximate size over time is equal to or less than the second threshold value.
Additionally, there is no reason, teaching, or suggestion provided with any prior art of record to modify the above medical support device to have the above features.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA G STARKEY whose telephone number is (571)272-3375. The examiner can normally be reached Monday-Friday 8:00-5:00 ET.
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/OLIVIA GRACE STARKEY/ Examiner, Art Unit 3795
/MICHAEL J CAREY/ Supervisory Patent Examiner, Art Unit 3795
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