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 .
This communication is in response to Application No. 18/970,897 filed 12/06/2024. Claims 1-16 are pending.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35. U.S.C 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/04/2025 have been entered and considered. Initialed copies of the PTO-1449 by the examiner are attached.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, and 13-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shimoda et al. (JP 2021049314 A, English translation hereinafter referred to as “Shimoda”).
Regarding claim 1, Shimoda teaches an image processing device comprising (image processing device 82, Shimoda, [0077]; Fig. 11): a processor, wherein the processor is configured to (Fig. 2 processor 11, Shimoda):
acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope (“the image area designated by the first area designation data is an image area corresponding to the route to which the endoscope 8 capturing the endoscopic image is to travel” Shimoda, [0034]; i.e., the lumen direction is that of the route of the endoscope), in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image (“The endoscopic image input to the G1-AI model 32 is normalized to a predetermined size and shape, and is virtually divided into a plurality of unit image regions by a predetermined grid line as illustrated in FIG. 6” Shimoda, [0035]; “The G1-AI model 32 is machine-learned using a plurality of pieces of teacher data in which a correct answer of the first region designation data is associated with each of the teacher endoscope images. Specifically, a plurality of teacher endoscope images are prepared, an image region corresponding to a route to which the endoscope capturing the image is to be advanced is specified (for example, by a person) with respect to each teacher endoscope image, and a plurality of teacher data are generated by tagging each teacher endoscope image with the correct answer of the first region designation data designating the specified image region. The G1-AI model 32 is learned by a predetermined machine learning algorithm using the plurality of pieces of teacher data generated in this manner. For example, the G1-AI model 32 is learned by a known learning algorithm of a CNN suitable for image classification similar to that of the P-AI model 31” Shimoda, [0036]), and
output lumen direction information that is information indicating the lumen direction (“the G1-AI model 32 outputs a probability value for each image region of all combinations of two unit image regions adjacent to each other on the left and right, all combinations of two unit image regions vertically adjacent to each other, and all combinations of two unit image regions diagonally adjacent to each other, in addition to one unit image region divided by the grid lines in FIG. 6. That is, the probability value of each image area indicates a route to which each image area of the target endoscopic image should travel or a probability that something is to be performed” Shimoda, [0037]).
Regarding claim 2, Shimoda teaches the image processing device according to claim 1,
wherein the lumen corresponding region is a region in a predetermined range including a lumen region in the image (“The endoscopic image input to the G1-AI model 32 is normalized to a predetermined size and shape, and is virtually divided into a plurality of unit image regions by a predetermined grid line as illustrated in FIG. 6” Shimoda, [0035]).
Regarding claim 4, Shimoda teaches the image processing device according to claim 1, wherein a direction of a division region overlapping the lumen corresponding region among the plurality of division regions is the lumen direction (“The "region position data" is data that can identify each of one or more organ regions identified on the basis of the inference result of the P-AI model 31 from among a plurality of organ regions obtained by virtually dividing the luminal organ in the major axis direction as position information of the endoscope 8, and is data for identifying the position of the distal end portion of the endoscope 8 in the luminal organ” Shimoda, [0027]; “the G1-AI model 32 outputs a probability value for each image region of all combinations of two unit image regions adjacent to each other on the left and right, all combinations of two unit image regions vertically adjacent to each other” Shimoda, [0037]).
Regarding claim 13, Shimoda teaches a display device that displays information corresponding to the lumen direction information output by the processor of the image processing device (“The guide information displayed in the present embodiment includes information for guiding a route or a direction to which the endoscope 8 is to travel, observation, information for guiding a place (point) to be performed by the endoscope 8” Shimoda, [0054]) according to claim 1.
Regarding claim 14, Shimoda teaches an endoscope device comprising: the image processing device according to any one of claim 1 (image processing device 82, Shimoda, [0077]; Fig. 11); and the endoscope (endoscope imaging unit 81, Shimoda, [0077]; Fig. 11).
Regarding claim 15, Shimoda teaches an image processing method comprising: acquiring a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope (“the image area designated by the first area designation data is an image area corresponding to the route to which the endoscope 8 capturing the endoscopic image is to travel” Shimoda, [0034]; i.e., the lumen direction is that of the route of the endoscope), in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image (“The endoscopic image input to the G1-AI model 32 is normalized to a predetermined size and shape, and is virtually divided into a plurality of unit image regions by a predetermined grid line as illustrated in FIG. 6” Shimoda, [0035]; “The G1-AI model 32 is machine-learned using a plurality of pieces of teacher data in which a correct answer of the first region designation data is associated with each of the teacher endoscope images. Specifically, a plurality of teacher endoscope images are prepared, an image region corresponding to a route to which the endoscope capturing the image is to be advanced is specified (for example, by a person) with respect to each teacher endoscope image, and a plurality of teacher data are generated by tagging each teacher endoscope image with the correct answer of the first region designation data designating the specified image region. The G1-AI model 32 is learned by a predetermined machine learning algorithm using the plurality of pieces of teacher data generated in this manner. For example, the G1-AI model 32 is learned by a known learning algorithm of a CNN suitable for image classification similar to that of the P-AI model 31” Shimoda, [0036]); and
outputting lumen direction information that is information indicating the lumen direction (“the G1-AI model 32 outputs a probability value for each image region of all combinations of two unit image regions adjacent to each other on the left and right, all combinations of two unit image regions vertically adjacent to each other, and all combinations of two unit image regions diagonally adjacent to each other, in addition to one unit image region divided by the grid lines in FIG. 6. That is, the probability value of each image area indicates a route to which each image area of the target endoscopic image should travel or a probability that something is to be performed” Shimoda, [0037]).
Regarding claim 16, Shimoda teaches a non-transitory computer-readable storage medium storing an image processing program executable by a first computer to execute image processing comprising (storage device, Shimoda, [0019]; recording medium, Shimoda, [0023]):
acquiring a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope(“the image area designated by the first area designation data is an image area corresponding to the route to which the endoscope 8 capturing the endoscopic image is to travel” Shimoda, [0034]; i.e., the lumen direction is that of the route of the endoscope), in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image (“The endoscopic image input to the G1-AI model 32 is normalized to a predetermined size and shape, and is virtually divided into a plurality of unit image regions by a predetermined grid line as illustrated in FIG. 6” Shimoda, [0035]; “The G1-AI model 32 is machine-learned using a plurality of pieces of teacher data in which a correct answer of the first region designation data is associated with each of the teacher endoscope images. Specifically, a plurality of teacher endoscope images are prepared, an image region corresponding to a route to which the endoscope capturing the image is to be advanced is specified (for example, by a person) with respect to each teacher endoscope image, and a plurality of teacher data are generated by tagging each teacher endoscope image with the correct answer of the first region designation data designating the specified image region. The G1-AI model 32 is learned by a predetermined machine learning algorithm using the plurality of pieces of teacher data generated in this manner. For example, the G1-AI model 32 is learned by a known learning algorithm of a CNN suitable for image classification similar to that of the P-AI model 31” Shimoda, [0036]); and
outputting lumen direction information that is information indicating the lumen direction (“the G1-AI model 32 outputs a probability value for each image region of all combinations of two unit image regions adjacent to each other on the left and right, all combinations of two unit image regions vertically adjacent to each other, and all combinations of two unit image regions diagonally adjacent to each other, in addition to one unit image region divided by the grid lines in FIG. 6. That is, the probability value of each image area indicates a route to which each image area of the target endoscopic image should travel or a probability that something is to be performed” Shimoda, [0037]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Shimoda et al. in view of Nishimura (US 20230410334 A1, hereinafter referred to as “Nishimura”).
Regarding claim 3, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the lumen corresponding region is an end part of an observation range of the camera in a direction in which a position of the lumen region is estimated from a fold region in the image.
However, Nishimura explicitly teaches wherein the lumen corresponding region is an end part of an observation range of the camera in a direction in which a position of the lumen region is estimated from a fold region in the image (“the recognition unit 264 recognizes the structure such as the lumen direction and the fold included in the endoscopic image together with the positional relationship in the depth direction using the region information and the depth information.” Nishimura, [0150]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Nishimura of having wherein the lumen corresponding region is an end part of an observation range of the camera in a direction in which a position of the lumen region is estimated from a fold region in the image.
Wherein having Shimoda’s endoscopic image processing device wherein the lumen corresponding region is an end part of an observation range of the camera in a direction in which a position of the lumen region is estimated from a fold region in the image.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Nishimura are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ, while Nishimura specifies a direction in which the endoscope is advanceable based on region information. Please see Shimoda et al. (JP 2021049314 A), Paragraph [0003] and Nishimura (US 20230410334 A1), Paragraph [0009].
Regarding claim 5, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the trained model is a data structure configured to cause the processor to estimate a position of the lumen region based on a shape and/or an orientation of a fold region in the image.
However, Nishimura explicitly teaches wherein the trained model is a data structure configured to cause the processor to estimate a position of the lumen region based on a shape and/or an orientation of a fold region in the image (“The segmentation result image includes a region of fold edges having a concentric shape and a region of a normal lumen” Nishimura, [0224]; wherein the normal lumen is a region where the endoscope can be advanced based on the region information (i.e., segmented folds)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Nishimura of having wherein the trained model is a data structure configured to cause the processor to estimate a position of the lumen region based on a shape and/or an orientation of a fold region in the image.
Wherein having Shimoda’s endoscopic image processing device wherein the trained model is a data structure configured to cause the processor to estimate a position of the lumen region based on a shape and/or an orientation of a fold region in the image.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Nishimura are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ, while Nishimura specifies a direction in which the endoscope is advanceable based on region information. Please see Shimoda et al. (JP 2021049314 A), Paragraph [0003] and Nishimura (US 20230410334 A1), Paragraph [0009].
Claim(s) 8 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Shimoda et al. in view of Bell et al. (“Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes”, 2013, hereinafter referred to as “Bell”).
Regarding claim 8, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the division regions include a central region of the image and a plurality of radial regions that are present radially from the central region toward an outer edge of the image.
However, Bell explicitly teaches wherein the division regions include a central region of the image and a plurality of radial regions that are present radially from the central region toward an outer edge of the image (Bell, pg. 188 Fig 3 (b) shows lumen-centered spatial partition with a central region and regions towards the outer edge of the image).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Bell of having wherein the division regions include a central region of the image and a plurality of radial regions that are present radially from the central region toward an outer edge of the image.
Wherein having Shimoda’s endoscopic image processing device wherein the division regions include a central region of the image and a plurality of radial regions that are present radially from the central region toward an outer edge of the image.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Bell are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ using an AI model, while Bell partitions endoscopic images into lumen-centered regions to identify feature vectors for artificial neural networks (ANNs) based on consistently aligning the center of the partition with the lumen center. Please see Shimoda et al. (JP 2021049314 A), Paragraphs [0003] and [0027] and Bell et al. (“Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes”) pg. 189 Col 1-2.
Regarding claim 10, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region.
However, Bell explicitly teaches wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region (Bell, pg. 188 Fig 3 (b) shows lumen-centered spatial partition with a central region and peripheral regions towards the outer edge side of the image with respect to the central region).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Bell of having wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region.
Wherein having Shimoda’s endoscopic image processing device wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Bell are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ using an AI model, while Bell partitions endoscopic images into lumen-centered regions to identify feature vectors for artificial neural networks (ANNs) based on consistently aligning the center of the partition with the lumen center. Please see Shimoda et al. (JP 2021049314 A), Paragraphs [0003] and [0027] and Bell et al. (“Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes”) pg. 189 Col 1-2.
Regarding claim 11, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the division regions are obtained by dividing the image into regions in three or more directions toward an outer edge of the image with a center of the image as a starting point.
However, Bell explicitly teaches wherein the division regions are obtained by dividing the image into regions in three or more directions toward an outer edge of the image with a center of the image as a starting point (Bell, pg. 188 Fig 3 (b) shows lumen-centered spatial partition with a central region and 4 (i.e., directions/quadrants) regions towards the outer edge side of the image with respect to the central region).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Bell of having wherein the division regions are obtained by dividing the image into regions in three or more directions toward an outer edge of the image with a center of the image as a starting point.
Wherein having Shimoda’s endoscopic image processing device wherein the division regions are obtained by dividing the image into regions in three or more directions toward an outer edge of the image with a center of the image as a starting point.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Bell are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ using an AI model, while Bell partitions endoscopic images into lumen-centered regions to identify feature vectors for artificial neural networks (ANNs) based on consistently aligning the center of the partition with the lumen center. Please see Shimoda et al. (JP 2021049314 A), Paragraphs [0003] and [0027] and Bell et al. (“Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes”) pg. 189 Col 1-2.
Regarding claim 12, Shimoda teaches the image processing device according to claim 1, Shimoda fails to explicitly teach wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region, and the peripheral regions are obtained by dividing the outer edge side of the image with respect to the central region in three or more directions from the central region toward an outer edge of the image.
However, Bell explicitly teaches wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region, and the peripheral regions are obtained by dividing the outer edge side of the image with respect to the central region in three or more directions from the central region toward an outer edge of the image (Bell, pg. 188 Fig 3 (b) shows lumen-centered spatial partition with a central region and 4 (i.e., directions/quadrants) regions towards the outer edge side of the image with respect to the central region).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Shimoda of having an image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, with the teachings of Bell of having wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region, and the peripheral regions are obtained by dividing the outer edge side of the image with respect to the central region in three or more directions from the central region toward an outer edge of the image.
Wherein having Shimoda’s endoscopic image processing device wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region, and the peripheral regions are obtained by dividing the outer edge side of the image with respect to the central region in three or more directions from the central region toward an outer edge of the image.
The motivation behind the modification would have been to obtain endoscopic image processing device capable of providing position and direction of an endoscope to enhance guidance, since both Shimoda and Bell are directed to processing an image captured by an endoscope. Wherein Shimoda endoscopic insertion procedure helps guide the insertion part of an endoscope into an organ without damaging the inside of the organ using an AI model, while Bell partitions endoscopic images into lumen-centered regions to identify feature vectors for artificial neural networks (ANNs) based on consistently aligning the center of the partition with the lumen center. Please see Shimoda et al. (JP 2021049314 A), Paragraphs [0003] and [0027] and Bell et al. (“Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes”) pg. 189 Col 1-2.
Allowable Subject Matter
Claim(s) 6-7 and 9 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. (US 20110160534 A1) – an endoscopic navigation system in which outputs a result image for indicating a lumen direction by filtering out dark areas and fold curves of the image(s).
Hasegawa et al. (US 20060015011 A1) – estimates shape of a range within an endoscopic image according to a continuity distribution of pixels and inserts a direction within a body cavity in which the endoscope should be further inserted.
Wang et al. (“A lumen detection-based intestinal direction vector acquisition method for wireless endoscopy systems”, 2014) – intestinal direction vector (IDV) for a single image used for navigation using adaptive threshold segmentation and radial texture detection to achieve lumen detection.
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/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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