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
Applicant's response to the last office action, filed July 28, 2026 has been entered and made of record. Claims 1, 4, 8, 10, 19, and 21-22 are amended. Claims 1-20 are pending in this application for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1-8, 10-22 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
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.
Claims 1, 3-4, 17, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Intrator et al, (US-PGPUB 20260017791), (based on Prov. Appl. 63/487,729 filed on March 1, 2023) in view of Wang (US-PGPUB 20200175662); and further in view of Usuda, (US-PGPUB 20210161366); and further in view of Nakamura et al, (US-PGPUB 20210035676)
Regarding claim 1, Intrator discloses a medical support device, (see at least:
Fig. 1, “system 100”), comprising:
a processor, (115 in Fig. 1B, Par. 0021, “EVA 115 may include … general-
purpose processor”), wherein the processor is configured to:
acquire a first medical image obtained by imaging an inside of a luminal organ
with an endoscope and a second medical image obtained temporally later than the first medical image by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, (see at least: Fig. 1A, Par. 0020, [Par. 0019 in Prov. Appl], System 100 includes an endoscope or colonoscope 105 coupled to a display 110 for capturing images of a colon and displaying a live video feed of the colonoscopy procedure. Further, Fig. 2, Par. 0036, [Par. 0035 in the Prov. Appl], acquiring a first plurality of image frames and a second plurality of image frames by using an endoscope or colonoscope 105, the first plurality of image frames include image frames from a video feed of a colonoscopy obtained during an insertion or forward phase of a procedure, whereas the second plurality of image frames include image frames of the video feed obtained during a retraction or backward phase of the procedure, [i.e., acquire a first medical image, “implicit by acquiring a first plurality of images frames”, obtained by imaging an inside of a luminal organ with an endoscope, “implicit by using endoscope or colonoscope 105”, and a second medical image obtained, “implicitly by acquiring a second plurality of images frames, temporally later than the first medical image, “a retraction or backward phase of the procedure is implicitly performed after the insertion or forward phase of a procedure”, by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, “implicitly inserting the endoscope or colonoscope 105 inside the colon”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image comparison between the first embedding representation and the second embedding representation, by using a classifier based on generating a cosine similarity score, which may be used to determine the likelihood that the first area of interest is the second area of interest, (see Fig. 2, “output the same area of interest”), [i.e., execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by using classifier to perform comparison between the first embedding representation and the second embedding representation and output the same area of interest”, in a case where a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image are similar or the same area of interest based on cosine similarity score”]);
wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, (see at least: Fig. 3, step 301, Par. 0044, [Par. 0043 in the Prov. Appl], identifying a first area of interest, including using a machine-learning model trained to identify one or more particular types of areas of interest, (a polyp), [i.e., output the first medical image by executing the first output process, “using a machine-learning model trained to identify one or more particular types of areas of interest”]);
the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other, (see at least: Fig. 3, step 311, Par. 0049-0050, [Par. 0048-0049 in the Prov. Appl], process block 311 relates to identifying a second area of interest in the portion of the body, [i.e., output the second medical image by executing the second output process, “implicit by using a machine-learning model trained to identify one or more particular types of areas of interest (polyps)”]);
Intrator does not expressly disclose wherein the first medical image is divided into a plurality of first divided regions and the second medical image is divided into a plurality of second divided regions; derive a first similarity for each of the first divided regions based on a correct answer data associated with the first medical image and the second medical image to output a first derivation result; derive a second similarity for each of the second divided regions based on the correct answer data to output a second derivation result; and execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in response to determining that a similarity between a first partial region that is at least one of the plurality of first divided regions and a second partial region that is at least one of the plurality of second divided regions exceeds a threshold value, based on the first derivation result and the second derivation result; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other.
However, Wang discloses wherein the first medical image is divided into a plurality of first divided regions and the second medical image is divided into a plurality of second divided regions, (see at least: Par. 0039, division processing for dividing the brains included in the brain image B0, “i.e., first medical image”, and the standard brain image Bs, “second medical image”, into a plurality of regions, “i.e., first divided regions and second divided regions”, corresponding to each other);
derive a first similarity for each of the first divided regions based on a correct answer data associated with the first medical image and the second medical image to output a first derivation result, (see at least: Par. 0039, first correction amount calculation processing for calculating a correction amount for matching the density characteristics of each of the plurality of regions …. the first reference pixel for each of the plurality of regions in the standard brain image Bs, and displaying control processing for displaying the corrected brain image B0 on the display 14, [i.e., derive a first similarity for each of the first divided regions, “matching the density characteristics of each of the plurality of regions in the brain image B0 and the standard brain image Bs”, based on a correct answer data, “first correction amount”, associated with the first medical image and the second medical image, “the brain image B0 and standard brain image Bs”, to output a first derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]);
derive a second similarity for each of the second divided regions based on the correct answer data to output a second derivation result, (see at least: Par. 0039, second correction amount calculation processing for calculating a second correction amount for matching first other pixel values other than the first reference pixel included in each of the plurality of regions in the brain image B0 with pixel values of second other pixels corresponding to the first other pixels for each of the plurality of regions in the standard brain image Bs, based on the first correction amount, [i.e., derive a second similarity for each of the second divided regions, “second correction amount for matching first other pixel values …in the standard brain image Bs”, based on the correct answer data, “based on the first correction amount”, to output a second derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in response to determining display control processing for displaying the corrected brain image B0 on the display 14, [i.e., executing a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by displaying the corrected brain image B0 on the display 14”, in response to determining a similarity between a first partial region that is at least one of the plurality of first divided regions and a second partial region that is at least one of the plurality of second divided regions, “implicit by determining correction processing for similarity between the first partial region and the second partial region”, based on the first derivation result and the second derivation result, “based on the first correction amount and the second correction amount”]).
Intrator and Wang are combinable because they are both concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Intrator, to use the division unit 22, as though by Wang, in order to divide the brains included in the brain image B0 and the standard brain image Bs into a plurality of regions corresponding to each other, (Wang, Par. 0042)
The combination of Intrator and Wang as whole does not expressly disclose outputting the first medical image and/or a second output process of outputting the second medical image in response to determining that a similarity between a first partial region that is at least one of the pluralities of first divided regions and a second partial region that is at least one of the pluralities of second divided regions exceeds a threshold value.
However, Usuda discloses outputting the first medical image and/or the second medical image in case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image exceeds a threshold value, (see at least: Figs. 11-12, Par. 0053-0055, the oversight determination unit 46 may perform the oversight determination processing by using the image similarity between a medical image acquired after the first timing and a medical image at the first timing, and since the region of interest 51 is included in a medical image at or after the first timing during a period (region-of-interest display period) in which the region of interest 51 is displayed in the first display region 34a, the image similarity to the medical image at the first timing is greater than or equal to a certain value, [i.e., the first medical image and/or the second medical image is output, “displaying the region of interest 51”, in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image exceeds a threshold value, “the similarity between the medical image 52 at the second timing and the image of the first part to be observed OP1 is greater than or equal to a certain value”]).
Intrator, Wang, and Usuda are combinable because they are both concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator and Wang, to use the oversight determination unit 46, as though by Usuda, in order to perform image similarity between a medical image acquired after the first timing and a medical image at the first timing, in a case where the user overlooks, (Usuda, Par. 0056)
The combine teaching Intrator, Wang, and Usuda as whole does not expressly disclose that the first medical image is output based on discriminating the first partial region and a first other region that is another region in the first medical image from each other; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other.
However, Nakamura discloses that the first medical image is output based on discriminating the first partial region and a first other region that is another region in the first medical image from each other; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other, (see at least: Par. 0054, the feature information acquisition unit 22 comprises a discriminator in which machine learning is performed so as to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, “implicitly discriminating the plurality of lesions from the first and second medical images G1 and G2”; and from Fig. 6, Par. 0063, displaying the first medical image G1 and the second medical image G2, and an interpretation report 41 for the first medical image G1 and an interpretation report 42 for the second medical image G2, [i.e., the first medical image is output, “see Fig. 6, displaying first medical images G1”, based on discriminating the first partial region and a first other region that is another region in the first medical image from each other, “implicitly based on discriminating one or more lesions from the first medical images G1”, and that the second medical image is output, “see Fig. 6, displaying second medical images G2”, based on discriminating the second partial region and a second other region that is another region in the second medical image from each other, “implicitly based on discriminating one or more lesions from the second medical images G2”]. Note that the one or more lesions in medical image G1 correspond to the first partial region, and the one or more lesions in medical image G2 correspond to the second partial region).
Intrator, Wang, Usuda, and Nakamura are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, and Usuda, to use the discriminator, as though by Nakamura, in order to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, (Nakamura, Par. 0054).
Regarding claim 3, the combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
Furthermore, Intrator discloses wherein the first medical image is an image obtained in an insertion step in which the endoscope is inserted into the luminal organ, and the second medical image is an image obtained in a removal step in which the endoscope is removed from the luminal organ, (see at least: Fig. 2, Par. 0036, [Par. 0035 in the Prov. Appl], acquiring a first plurality of image frames and a second plurality of image frames by using an endoscope or colonoscope 105, the first plurality of image frames include image frames from a video feed of a colonoscopy obtained during an insertion or forward phase of a procedure, whereas the second plurality of image frames include image frames of the video feed obtained during a retraction or backward phase of the procedure, [i.e., wherein the first medical image is an image obtained in an insertion step in which the endoscope is inserted into the luminal organ, “one or more image frames during an insertion or forward phase of a procedure, and the second medical image is an image obtained in a removal step in which the endoscope is removed from the luminal organ, “obtaining one or more image frames during a retraction or backward phase of the procedure”]).
Regarding claim 4, the combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
Wang further discloses wherein the first partial region is the at least one of the plurality of first divided regions obtained by dividing the first medical image according to a first rule, and the second partial region is the at least one of the plurality of second divided regions obtained by dividing the second medical image according to the first rule, (see at least: Par. 0039, dividing the brains included in the brain image B0 and the standard brain image Bs into a plurality of regions corresponding to each other, “i.e., the divided plurality of regions corresponding to each other corresponds to the first rule”).
Regarding claim 17, the combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
Intrator further discloses wherein the first medical image and the second medical image are images obtained by imaging a region
In the other hand, Usuda discloses irradiating a region with the same type light, (Fig. 2, Par. 0038, an endoscope 31 that captures an image of a photographic subject irradiated with at least one of light)
Regarding claim 20, claim 20 recites substantially similar limitations as set forth in claim 1. As such, claim 20 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “the endoscope apparatus comprising: the medical support device according to claim 1; and the endoscope”.
However, Usuda discloses the endoscope apparatus, (Fig. 2, “endoscope system 21”), comprising: the medical support device, (Fig. 2, “processor device 33”), according to claim 1; and the endoscope, “Fig. 3, endoscope 41”).
Regarding claim 21, claim 21 recites substantially similar limitations as set forth in claim 1. As such, claim 21 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “a medical support method”. However, Intrator discloses the “medical support method”, (see at least: Par. 0011, [Par. 0010 in the Prov. Appl.], “an endoscopic systems and methods”).
Regarding claim 22, claim 22 recites substantially similar limitations as set forth in claim 1. As such, claim 22 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “a non-transitory computer-readable storage medium storing a program executable by a computer to execute a medical support process”. However, Intrator discloses the “non-transitory computer-readable storage medium storing a program executable by a computer to execute a medical support process”, (see at least: Par. 0066, [Par. 0065 in the Prov. Appl], “a tangible machine-readable storage medium stores information in a non-transitory form”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator, Wang, Usuda, and Nakamura, as applied to claim 1 above; and further in view of Endo, (US-PGPUB 20210196099)
The combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
Furthermore, Nakamura discloses discriminating the first partial region and a first other region in the first medical image; and discriminating the second partial region and a second other region in the second medical image, (Nakamura, Par. 0054, 0063)
However, combination of Intrator, Wang, Usuda, and Nakamura as whole, does not expressly disclose the emphasizing the first and second regions of interest.
However, Endo discloses emphasizing the first and second regions of interest, (see at least: Par. 0102, emphasis region setting unit 42 may set emphasis regions for all the endoscopic images 38 in which the region of interest is detected, [i.e., implicitly emphasizing the first and second regions of interest]).
Intrator, Wang, Usuda, Nakamura, and Endo are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, Usuda, and Nakamura, to use the emphasis region setting unit 42, as though by Endo, in order to emphasize one or more regions of interest detected from the endoscopic images, (Endo, Par. 0102) .
Claims 8, 10, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Intrator et al, (US-PGPUB 20260017791), (based on Prov. Appl. 63/487,729 filed on March 1, 2023) in view of Wang (US-PGPUB 20200175662); and further in view of Ranguelova et al, (US-PGPUB 20110311148); and further in view of Nakamura et al, (US-PGPUB 20210035676)
Regarding claim 8, Intrator discloses a medical support device comprising:
a processor, (115 in Fig. 1B, Par. 0021, “EVA 115 may include … general-
purpose processor”), wherein the processor is configured to:
acquire a first medical image obtained by imaging an inside of a luminal organ with an endoscope and a second medical image obtained temporally later than the first medical image by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, (see at least: Fig. 1A, Par. 0020, [Par. 0019 in Prov. Appl], System 100 includes an endoscope or colonoscope 105 coupled to a display 110 for capturing images of a colon and displaying a live video feed of the colonoscopy procedure. Further, Fig. 2, Par. 0036, [Par. 0035 in the Prov. Appl], acquiring a first plurality of image frames and a second plurality of image frames by using an endoscope or colonoscope 105, the first plurality of image frames include image frames from a video feed of a colonoscopy obtained during an insertion or forward phase of a procedure, whereas the second plurality of image frames include image frames of the video feed obtained during a retraction or backward phase of the procedure, [i.e., acquire a first medical image, “implicit by acquiring a first plurality of images frames”, obtained by imaging an inside of a luminal organ with an endoscope, “implicit by using endoscope or colonoscope 105”, and a second medical image obtained, “implicitly by acquiring a second plurality of images frames, temporally later than the first medical image, “a retraction or backward phase of the procedure is implicitly performed after the insertion or forward phase of a procedure”, by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, “implicitly inserting the endoscope or colonoscope 105 inside the colon”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image exceeds a threshold value, (see at least: Fig. 2, Par. 0038-0039, [Par. 0037-0038 in the Prov. Appl], and Fig. 3, steps 321, 323, Par. 0051-0053, [Par. 0050-0051 in the Prov. Appl], using of a multi-frame embedder to provide embedding representations based on the first and second pluralities of latent representations; and then performing a comparison between the first embedding representation and the second embedding representation, by using a classifier based on generating a cosine similarity score, which may be used to determine the likelihood that the first area of interest is the second area of interest, (see Fig. 2, “output the same area of interest”), [i.e., execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by using classifier to perform comparison between the first embedding representation and the second embedding representation and output the same area of interest”, in a case where a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image are similar or the same area of interest based on cosine similarity score”]).
Intrator does not expressly disclose wherein the first partial region is at least one of a plurality of first divided regions obtained by dividing a first region that is a part of the first medical image according to a second rule, and the second partial region is at least one of a plurality of second divided regions obtained by dividing a second region that is a part of the second medical image according to the second rule; wherein the processor is further configured to: derive the similarity between each of the plurality of first divided regions and each of the plurality of second divided regions based on each of the plurality of first divided regions and each of the plurality of second divided regions; and extract, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value as the first partial region and the second partial region in which the similarity exceeds the threshold value, wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, and the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other.
However, Wang et al discloses wherein the first partial region is at least one of a plurality of first divided regions obtained by dividing a first region that is a part of the first medical image according to a second rule, and the second partial region is at least one of a plurality of second divided regions obtained by dividing a second region that is a part of the second medical image according to the second rule, (see at least: Par. 0039, dividing the brains included in the brain image B0 and the standard brain image Bs into a plurality of regions corresponding to each other, “i.e., the divided plurality of regions corresponding to each other corresponds to the second rule”);
wherein the processor is further configured to derive the similarity between each of the plurality of first divided regions and each of the plurality of second divided regions based on each of the plurality of first divided regions and each of the plurality of second divided regions, (see at least: Par. 0039, first correction amount calculation processing for calculating a correction amount for matching the density characteristics of each of the plurality of regions …. the first reference pixel for each of the plurality of regions in the standard brain image Bs, [i.e., derive the similarity between each of the plurality of first divided regions and each of the plurality of second divided regions based on each of the plurality of first divided regions and each of the plurality of second divided regions, “implicit by matching the density characteristics of each of the plurality of regions in the brain image B0 and the standard brain image Bs”]).
Intrator and Wang are combinable because they are both concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Intrator, to use the division unit 22, as though by Wang, in order to divide the brains included in the brain image B0 and the standard brain image Bs into a plurality of regions corresponding to each other, (Wang, Par. 0042)
The combination of Intrator and Wang as whole does not expressly disclose extracting, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value as the first partial region and the second partial region in which the similarity exceeds the threshold value, wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, and the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other.
Ranguelova discloses extracting, from the plurality of first divided regions and
the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value as the first partial region and the second partial region in which the similarity exceeds the threshold value, (see at least: Fig. 4, Par. 0032-0033, processor 14 searches for matching first and second regions in the first and second image, and In a first step 41, processor 14 selects a first one of the identified regions in the first image. In a second step 42, processor 14 selects a second one of the identified regions in the second image; and on Par. 0049, only first and second regions from matches with a matching score above a threshold being chosen, [i.e., extracting, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region, “implicit by extracting first and second regions from matches”, in which the similarity exceeds the threshold value as the first partial region and the second partial region in which the similarity exceeds the threshold value, “implicit by choosing only first and second regions from matches with a matching score above a threshold”]).
Intrator, Wang, and Ranguelova are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator and Wang, to use steps 35, 41, 42, as though by Ranguelova, in order to search for matching first and second regions in the first and second image, (Par. 0032-0033), and further choosing only first and second regions from matches with a matching score above a threshold, (Par. 0042)
The combination of Intrator, Wang, and Ranguelova as whole does not expressly disclose wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, and the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other
However, Nakamura discloses that wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, and the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other, (see at least: Par. 0054, the feature information acquisition unit 22 comprises a discriminator in which machine learning is performed so as to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, “implicitly discriminating the plurality of lesions from the first and second medical images G1 and G2”; and from Fig. 6, Par. 0063, displaying the first medical image G1 and the second medical image G2, and an interpretation report 41 for the first medical image G1 and an interpretation report 42 for the second medical image G2, [i.e., the first medical image is output, “see Fig. 6, displaying first medical images G1”, based on discriminating the first partial region and a first other region that is another region in the first medical image from each other, “implicitly based on discriminating one or more lesions from the first medical images G1”, and that the second medical image is output, “see Fig. 6, displaying second medical images G2”, based on discriminating the second partial region and a second other region that is another region in the second medical image from each other, “implicitly based on discriminating one or more lesions from the second medical images G2”]. Note that the one or more lesions in medical image G1 correspond to the first partial region, and the one or more lesions in medical image G2 correspond to the second partial region).
Intrator, Wang, Ranguelova, and Nakamura are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, and Ranguelova, to use the discriminator, as though by Nakamura, in order to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, (Nakamura, Par. 0054).
Regarding claim 10, the combination of Intrator, Wang, Ranguelova, and Nakamura as whole discloses the limitations of claim 1.
Nakamura further discloses wherein the processor derives the similarity by using AI, (see at least: Par. 0054, implicit by using machine learning for recognizing lesions]).
Regarding claim 12, the combination of Intrator, Wang, Ranguelova, and Nakamura as whole, discloses the limitations of claim 8.
Nakamura further discloses wherein each of the first region and the second region is a feature region recognized by performing an object recognition process as a region having features determined in advance, (see at least: Par. 0054, using a discriminator in which machine learning is performed … to output the probability that each pixel (voxel) in the first and second medical images G1 and G2 is each of a plurality of lesions, [i.e., one or more pixels lesions in the first and second medical images G1 and G2 correspond to the a feature region recognized by performing an object recognition process, by implicitly training the machine learning with known pixels lesions]).
Regarding claim 13, the combination of Intrator, Wang, Ranguelova, and Nakamura as whole, discloses the limitations of claim 12.
Nakamura further discloses wherein the feature region is a region in which a lesion is shown, a marked region, a region in which an organ is shown, and/or a region in which a treatment tool is shown, (see at least: Par. 0054, “lesions”).
Regarding claim 14, the combination of Intrator, Wang, Ranguelova, and Nakamura as whole, discloses the limitations of claim 12.
Nakamura further discloses wherein object recognition AI is used in the object recognition process, (see at least: Par. 0054, implicit by using machine learning for recognizing lesions).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator, Wang, Ranguelova, and Nakamura, as applied to claim 10 above; and further in view of Maliksi et al, (US-PGPUB 20240265545)
The combination of Intrator, Wang, Ranguelova, and Nakamura as whole discloses the limitations of claim 8.
The combination of Intrator, Wang, Ranguelova, and Nakamura as whole does not expressly disclose wherein the similarity is derived according to a second instruction given from an outside.
Maliksi discloses wherein the similarity is derived according to a second instruction given from an outside, (see at least: Par. 0040, correlation can be determined based on a selection input from the user to select corresponding regions of interest between first medical image 102 and second medical image 104. ….the multi-modal medical images correlating system 200 further includes a graphical user interface (GUI) 204 that can accept the inputs from the user for correlation determination, [i.e., wherein the similarity, “correlation determination”, is derived according to a second instruction given from an outside, “implicit by using graphical user interface (GUI) 204”]).
Intrator, Wang, Ranguelova, Nakamura, and Maliksi are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, Ranguelova, and Nakamura, to use GUI 204, as though by Maliksi, in order to enable the user to select corresponding regions of interest between first medical image 102 and second medical image 104, (Maliksi, Par. 0040)
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator, Wang, Ranguelova, and Nakamura, as applied to claim 12 above; and further in view of Futamura et al, (US-PGPUB 20210056690)
The combination of Intrator, Wang, Ranguelova, and Nakamura as whole, discloses the limitations of claim 12.
The combination of Intrator, Wang, Ranguelova, and Nakamura as whole does not expressly disclose wherein the first medical image is stored in a first storage region on a condition that the first region is recognized as the feature region by performing the object recognition process.
Futamura discloses wherein the first medical image is stored in a first storage region on a condition that the first region is recognized as the feature region by performing the object recognition process, (see at least: Par. 0101-0102, the controller 21 sends the medical image and the display information of the lesion-detected regions to the image server 3, thereby storing the medical image of the lesion detected region).
Intrator, Wang, Ranguelova, Nakamura , and Futamura are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, Ranguelova, and Nakamura, to use the image server 3, as though by Futamura, in order to store the medical image of the lesion detected region, (Futamura, Par. 0101).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator, Wang, Usuda, and Nakamura, as applied to claim 1 above; and further in view of Ishida et al, (US-PGPUB 20180182480)
The combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
The combination of Intrator, Wang, Usuda, and Nakamura as whole does not expressly disclose wherein the first medical image is stored in a second storage region in response to a third instruction given from an outside.
However, Ishida discloses wherein the first medical image is stored in a second storage region in response to a third instruction given from an outside, (see at least: Par. 0065, receiving a user's instruction to store the medical image to the PACS server, [i.e., wherein the first medical image is stored in a second storage region, “PACS server”, in response to a third instruction given from an outside, “upon receiving a user's instruction to store the medical image to the PACS server”]).
Intrator, Wang, Usuda, Nakamura, and Ishida are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, Usuda, and Nakamura, to use the user’s instructions, as though by Ishida, in order to store the medical image to the PACS server, (Ishida, Par. 0065).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator, Wang, Usuda, and Nakamura, as applied to claim 1 above; and further in view of Moffet, (US-PGPUB 20140208273)
The combination of Intrator, Wang, Usuda, and Nakamura as whole discloses the limitations of claim 1.
The combination of Intrator, Wang, Usuda, and Nakamura as whole does not expressly disclose wherein the first medical image output by executing the first output process is displayed on a first screen, and the second medical image output by executing the second output process is displayed on a second screen.
Moffet discloses wherein the first medical image output by executing the first output process is displayed on a first screen, and the second medical image output by executing the second output process is displayed on a second screen, (see at least: Par. 0004, a first display screen may be used to display a first medical image (or images) and a second display screen may be used to display a second medical image (or images).
Intrator, Wang, Usuda, Nakamura, and Moffet are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, Usuda, and Nakamura, to display the first and second medical images in the first and second display screens, as though by Moffet, in order to compare images from studies of a patient from different times, or to compare images from a patient with a corresponding reference image, (Moffet, Par. 0004)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Intrator et al, (US-PGPUB 20260017791), (based on Prov. Appl. 63/487,729 filed on March 1, 2023) in view of Wang (US-PGPUB 20200175662); and further in view of Kim et al, (US-PGPUB 2024/0205509); and further in view of Nakamura et al, (US-PGPUB 20210035676)
Regarding claim 19, Intrator discloses a medical support device, (see at least:
Fig. 1, “system 100”), comprising:
a processor, (115 in Fig. 1B, Par. 0021, “EVA 115 may include … general-
purpose processor”), wherein the processor is configured to:
acquire a first medical image obtained by imaging an inside of a luminal organ
with an endoscope and a second medical image obtained temporally later than the first medical image by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, (see at least: Fig. 1A, Par. 0020, [Par. 0019 in Prov. Appl], System 100 includes an endoscope or colonoscope 105 coupled to a display 110 for capturing images of a colon and displaying a live video feed of the colonoscopy procedure. Further, Fig. 2, Par. 0036, [Par. 0035 in the Prov. Appl], acquiring a first plurality of image frames and a second plurality of image frames by using an endoscope or colonoscope 105, the first plurality of image frames include image frames from a video feed of a colonoscopy obtained during an insertion or forward phase of a procedure, whereas the second plurality of image frames include image frames of the video feed obtained during a retraction or backward phase of the procedure, [i.e., acquire a first medical image, “implicit by acquiring a first plurality of images frames”, obtained by imaging an inside of a luminal organ with an endoscope, “implicit by using endoscope or colonoscope 105”, and a second medical image obtained, “implicitly by acquiring a second plurality of images frames, temporally later than the first medical image, “a retraction or backward phase of the procedure is implicitly performed after the insertion or forward phase of a procedure”, by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, “implicitly inserting the endoscope or colonoscope 105 inside the colon”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image comparison between the first embedding representation and the second embedding representation, by using a classifier based on generating a cosine similarity score, which may be used to determine the likelihood that the first area of interest is the second area of interest, (see Fig. 2, “output the same area of interest”), [i.e., execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by using classifier to perform comparison between the first embedding representation and the second embedding representation and output the same area of interest”, in a case where a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image are similar or the same area of interest based on cosine similarity score”]);
wherein the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, (see at least: Fig. 3, step 301, Par. 0044, [Par. 0043 in the Prov. Appl], identifying a first area of interest, including using a machine-learning model trained to identify one or more particular types of areas of interest, (a polyp), [i.e., output the first medical image by executing the first output process, “using a machine-learning model trained to identify one or more particular types of areas of interest”]);
the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other, (see at least: Fig. 3, step 311, Par. 0049-0050, [Par. 0048-0049 in the Prov. Appl], process block 311 relates to identifying a second area of interest in the portion of the body, [i.e., output the second medical image by executing the second output process, “implicit by using a machine-learning model trained to identify one or more particular types of areas of interest (polyps)”]);
Intrator does not expressly disclose wherein the first medical image is divided into a plurality of first divided regions and the second medical image is divided into a plurality of second divided regions; derive a first similarity for each of the first divided regions based on a correct answer data associated with the first medical image and the second medical image to output a first derivation result; derive a second similarity for each of the second divided regions based on the correct answer data to output a second derivation result; and execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in response to determining that a difference between a first partial region that is at least one of the plurality of first divided regions and a second partial region that is at least one of the plurality of second divided regions is less that a threshold value, based on the first derivation result and the second derivation result; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other.
However, Wang discloses wherein the first medical image is divided into a plurality of first divided regions and the second medical image is divided into a plurality of second divided regions, (see at least: Par. 0039, division processing for dividing the brains included in the brain image B0, “i.e., first medical image”, and the standard brain image Bs, “second medical image”, into a plurality of regions, “i.e., first divided regions and second divided regions”, corresponding to each other);
derive a first similarity for each of the first divided regions based on a correct answer data associated with the first medical image and the second medical image to output a first derivation result, (see at least: Par. 0039, first correction amount calculation processing for calculating a correction amount for matching the density characteristics of each of the plurality of regions …. the first reference pixel for each of the plurality of regions in the standard brain image Bs, and displaying control processing for displaying the corrected brain image B0 on the display 14, [i.e., derive a first similarity for each of the first divided regions, “matching the density characteristics of each of the plurality of regions in the brain image B0 and the standard brain image Bs”, based on a correct answer data, “first correction amount”, associated with the first medical image and the second medical image, “the brain image B0 and standard brain image Bs”, to output a first derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]);
derive a second similarity for each of the second divided regions based on the correct answer data to output a second derivation result, (see at least: Par. 0039, second correction amount calculation processing for calculating a second correction amount for matching first other pixel values other than the first reference pixel included in each of the plurality of regions in the brain image B0 with pixel values of second other pixels corresponding to the first other pixels for each of the plurality of regions in the standard brain image Bs, based on the first correction amount, [i.e., derive a second similarity for each of the second divided regions, “second correction amount for matching first other pixel values …in the standard brain image Bs”, based on the correct answer data, “based on the first correction amount”, to output a second derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in response to determining display control processing for displaying the corrected brain image B0 on the display 14, [i.e., executing a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by displaying the corrected brain image B0 on the display 14”, in response to determining a similarity between a first partial region that is at least one of the plurality of first divided regions and a second partial region that is at least one of the plurality of second divided regions, “implicit by determining correction processing for similarity between the first partial region and the second partial region”, based on the first derivation result and the second derivation result, “based on the first correction amount and the second correction amount”]).
Intrator and Wang are combinable because they are both concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Intrator, to use the division unit 22, as though by Wang, in order to divide the brains included in the brain image B0 and the standard brain image Bs into a plurality of regions corresponding to each other, (Wang, Par. 0042)
The combination of Intrator and Wang as whole does not expressly disclose outputting the first medical image and/or a second output process of outputting the second medical image in response to determining that a difference between a first partial region that is at least one of the pluralities of first divided regions and a second partial region that is at least one of the pluralities of second divided regions is less than a threshold value.
However, Kim et al discloses outputting the first medical image and/or a second output process of outputting the second medical image in response to determining that a difference between a first partial region that is at least one of the pluralities of first divided regions and a second partial region that is at least one of the pluralities of second divided regions is less than a threshold value, (see at least: Fig. 5, Par. 0090, When a distance between adjacent ROIs in the horizontal direction is less than or equal to a first threshold distance, the display device 100 may integrate the ROIs into a single region, [i.e., implicitly outputting the first medical image and/or the second medical image, “implicit by integrating the ROIs into a single region”, in case where a similarity difference between a first partial region and a second partial region exceeds is less than a threshold value, “distance between adjacent ROIs is less than or equal to a first threshold distance”]).
Intrator, Wang, and Kim are combinable because they are both concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator and Wang, to measure distance between the first and second regions of interest and to compare the measured to threshold distance, as though by Kim, in order to integrate adjacent ROI regions in a horizontal direction (an x-axis direction) among ROIs included in an image, when the distance is less than the threshold distance, (Kim, Par. 0090)
The combine teaching Intrator, Wang, and Kim as whole does not expressly disclose that the first medical image is output based on discriminating the first partial region and a first other region that is another region in the first medical image from each other; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other.
However, Nakamura discloses that the first medical image is output based on discriminating the first partial region and a first other region that is another region in the first medical image from each other; and that the second medical image is output based on discriminating the second partial region and a second other region that is another region in the second medical image from each other, (see at least: Par. 0054, the feature information acquisition unit 22 comprises a discriminator in which machine learning is performed so as to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, “implicitly discriminating the plurality of lesions from the first and second medical images G1 and G2”; and from Fig. 6, Par. 0063, displaying the first medical image G1 and the second medical image G2, and an interpretation report 41 for the first medical image G1 and an interpretation report 42 for the second medical image G2, [i.e., the first medical image is output, “see Fig. 6, displaying first medical images G1”, based on discriminating the first partial region and a first other region that is another region in the first medical image from each other, “implicitly based on discriminating one or more lesions from the first medical images G1”, and that the second medical image is output, “see Fig. 6, displaying second medical images G2”, based on discriminating the second partial region and a second other region that is another region in the second medical image from each other, “implicitly based on discriminating one or more lesions from the second medical images G2”]. Note that the one or more lesions in medical image G1 correspond to the first partial region, and the one or more lesions in medical image G2 correspond to the second partial region).
Intrator, Wang, Kim, and Nakamura are combinable because they are all concerned with image-based features detection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Intrator, Wang, and Kim, to use the discriminator, as though by Nakamura, in order to discriminate whether or not each pixel (voxel) in the first and second medical images G1 and G2 represents a lesion, (Nakamura, Par. 0054).
Allowable Subject Matter
Claims 5-7 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.
With respect to claim 5, the prior art of record, alone or in reasonable combination, does not teach or suggest, the following underlined limitation(s), (in consideration of the claim as a whole):
“wherein the processor is further configured to: derive a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; and extract, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value according to the derived map, as the first partial region and the second partial region in which the similarity exceeds the threshold value”.
The relevant prior art of record, Intrator et al, (US-PGPUB 20260017791), discloses a medical support device, (see at least: Fig. 1, “system 100”), comprising:
a processor, (115 in Fig. 1B, Par. 0021, “EVA 115 may include … general-
purpose processor”), wherein the processor is configured to:
acquire a first medical image obtained by imaging an inside of a luminal organ
with an endoscope and a second medical image obtained temporally later than the first medical image by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, (see at least: Fig. 1A, Par. 0020, [Par. 0019 in Prov. Appl], System 100 includes an endoscope or colonoscope 105 coupled to a display 110 for capturing images of a colon and displaying a live video feed of the colonoscopy procedure. Further, Fig. 2, Par. 0036, [Par. 0035 in the Prov. Appl], acquiring a first plurality of image frames and a second plurality of image frames by using an endoscope or colonoscope 105, the first plurality of image frames include image frames from a video feed of a colonoscopy obtained during an insertion or forward phase of a procedure, whereas the second plurality of image frames include image frames of the video feed obtained during a retraction or backward phase of the procedure, [i.e., acquire a first medical image, “implicit by acquiring a first plurality of images frames”, obtained by imaging an inside of a luminal organ with an endoscope, “implicit by using endoscope or colonoscope 105”, and a second medical image obtained, “implicitly by acquiring a second plurality of images frames, temporally later than the first medical image, “a retraction or backward phase of the procedure is implicitly performed after the insertion or forward phase of a procedure”, by imaging the inside of the luminal organ with the endoscope while the endoscope is inserted into the luminal organ, “implicitly inserting the endoscope or colonoscope 105 inside the colon”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image provide embedding representations based on the first and second pluralities of latent representations; and then performing a comparison between the first embedding representation and the second embedding representation, by using a classifier based on generating a cosine similarity score, which may be used to determine the likelihood that the first area of interest is the second area of interest, (see Fig. 2, “output the same area of interest”), [i.e., execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by using classifier to perform comparison between the first embedding representation and the second embedding representation and output the same area of interest”, in a case where a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image are similar or the same area of interest based on cosine similarity score”]);
the first medical image output by executing the first output process is an image represented in an aspect in which the first partial region and a first other region that is another region in the first medical image are discriminable from each other, (see at least: Fig. 3, step 301, Par. 0044, [Par. 0043 in the Prov. Appl], identifying a first area of interest, including using a machine-learning model trained to identify one or more particular types of areas of interest, (a polyp), [i.e., output the first medical image by executing the first output process, “using a machine-learning model trained to identify one or more particular types of areas of interest”]);
the second medical image output by executing the second output process is an image represented in an aspect in which the second partial region and a second other region that is another region in the second medical image are discriminable from each other, (see at least: Fig. 3, step 311, Par. 0049-0050, [Par. 0048-0049 in the Prov. Appl], process block 311 relates to identifying a second area of interest in the portion of the body, [i.e., output the second medical image by executing the second output process, “implicit by using a machine-learning model trained to identify one or more particular types of areas of interest (polyps)”]);
However, Intrator fails to teach or suggest, either alone or in combination with the other cited references, deriving a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; and extract, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value according to the derived map, as the first partial region and the second partial region in which the similarity exceeds the threshold value.
A further prior art of record, Wang (US-PGPUB 20200175662) discloses wherein the first medical image is divided into a plurality of first divided regions and the second medical image is divided into a plurality of second divided regions, (see at least: Par. 0039, division processing for dividing the brains included in the brain image B0, “i.e., first medical image”, and the standard brain image Bs, “second medical image”, into a plurality of regions, “i.e., first divided regions and second divided regions”, corresponding to each other);
derive a first similarity for each of the first divided regions based on a correct answer data associated with the first medical image and the second medical image to output a first derivation result, (see at least: Par. 0039, first correction amount calculation processing for calculating a correction amount for matching the density characteristics of each of the plurality of regions …. the first reference pixel for each of the plurality of regions in the standard brain image Bs, and displaying control processing for displaying the corrected brain image B0 on the display 14, [i.e., derive a first similarity for each of the first divided regions, “matching the density characteristics of each of the plurality of regions in the brain image B0 and the standard brain image Bs”, based on a correct answer data, “first correction amount”, associated with the first medical image and the second medical image, “the brain image B0 and standard brain image Bs”, to output a first derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]);
derive a second similarity for each of the second divided regions based on the correct answer data to output a second derivation result, (see at least: Par. 0039, second correction amount calculation processing for calculating a second correction amount for matching first other pixel values other than the first reference pixel included in each of the plurality of regions in the brain image B0 with pixel values of second other pixels corresponding to the first other pixels for each of the plurality of regions in the standard brain image Bs, based on the first correction amount, [i.e., derive a second similarity for each of the second divided regions, “second correction amount for matching first other pixel values …in the standard brain image Bs”, based on the correct answer data, “based on the first correction amount”, to output a second derivation result, “implicit by displaying control processing for displaying the corrected brain image B0 on the display 14”]); and
execute a first output process of outputting the first medical image and/or a second output process of outputting the second medical image in response to determining display control processing for displaying the corrected brain image B0 on the display 14, [i.e., executing a first output process of outputting the first medical image and/or a second output process of outputting the second medical image, “implicit by displaying the corrected brain image B0 on the display 14”, in response to determining a similarity between a first partial region that is at least one of the plurality of first divided regions and a second partial region that is at least one of the plurality of second divided regions, “implicit by determining correction processing for similarity between the first partial region and the second partial region”, based on the first derivation result and the second derivation result, “based on the first correction amount and the second correction amount”]).
However, Wang fails to teach or suggest, either alone or in combination with the other cited references, deriving a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; and extract, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value according to the derived map, as the first partial region and the second partial region in which the similarity exceeds the threshold value.
A further prior art of record, Usuda, (US-PGPUB 20210161366), discloses outputting the first medical image and/or the second medical image in case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image exceeds a threshold value, (see at least: Figs. 11-12, Par. 0053-0055, the oversight determination unit 46 may perform the oversight determination processing by using the image similarity between a medical image acquired after the first timing and a medical image at the first timing, and since the region of interest 51 is included in a medical image at or after the first timing during a period (region-of-interest display period) in which the region of interest 51 is displayed in the first display region 34a, the image similarity to the medical image at the first timing is greater than or equal to a certain value, [i.e., the first medical image and/or the second medical image is output, “displaying the region of interest 51”, in a case where a similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image exceeds a threshold value, “the similarity between the medical image 52 at the second timing and the image of the first part to be observed OP1 is greater than or equal to a certain value”]).
However, while disclosing performing similarity between a first partial region that is a part of the first medical image and a second partial region that is a part of the second medical image, Usuda fails to teach or suggest, either alone or in combination with the other cited references, deriving a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; and extract, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value according to the derived map, as the first partial region and the second partial region in which the similarity exceeds the threshold value.
Another prior art of record, Kim et al, (US-PGPUB 20230125925) discloses deriving a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; and extract a first divided region and the second divided region, from the plurality divided regions, (Par. 0057, the blood vessel extraction model may be trained to divide the feature map of the retinal image, “i.e., first image” and the blood vessel image, “the second image”, into the same size, and generate a similarity distribution by comparing similarities, “i.e., deriving a map showing a distribution of the similarities”, between any one (blood vessel patch) of the divided blood vessel images and a feature map (feature patch) of a plurality of the divided retinal images, reflect the similarity distribution in the retinal image, and extract the blood vessel from a region (part of image) corresponding to the blood vessel patch from the retinal image using the retinal to which the similarity distribution is reflected image and the blood vessel patch. However, while disclosing deriving a map showing a distribution of the similarities between the plurality of first divided regions and the plurality of second divided regions based on the first medical image and the second medical image; Kim et al fails to teach or suggest, either alone or in combination with the other cited references, extracting, from the plurality of first divided regions and the plurality of second divided regions, the first divided region and the second divided region in which the similarity exceeds the threshold value according to the derived map, as the first partial region and the second partial region in which the similarity exceeds the threshold value.
Regarding claims 6 and 7, claims 6 and 7 are also in condition for allowance in view of at least their dependency from claim 5.
Conclusion
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMARA ABDI whose telephone number is (571)272-0273. The examiner can normally be reached 9:00am-5:30pm.
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, Vu Le can be reached at (571) 272-7332. 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.
/AMARA ABDI/Primary Examiner, Art Unit 2668 09/22/2026
Prosecution Insights