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 .
Election/Restrictions
Claims 16 and 17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 23 June, 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5 December, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 4, 7 – 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Yeung et al (U.S. Patent Publication No. 2018/0296281 A1, hereinafter “Yeung”) in view of Lee et al (U.S. Patent No. 12635848 B2, hereinafter “Lee”).
Regarding claim 1, Yeung teaches an endoscope control system that determines an insertion operation detail of an endoscope (¶ 0084: In some instances, the steering control system ( or method) may utilize an artificial intelligence algorithm ( e.g., a deep machine learning algorithm) to process image data as input and provide a predicted steering direction and/or steering control signal as output for guiding the movement of the robotic endoscope.), comprising one or more processors having hardware (¶ 0084: c) one or more processors…),
wherein the one or more processors are configured to:
acquire an image of a lumen imaged by the endoscope (¶ 0084: In some instances, the control system may comprise: a) a first sensor (e.g., an image sensor) configured to provide a first input data stream, wherein the first input data stream comprises data relating to the position of the distal end of the robotic endoscope relative to the center of a lumen, compartment, or cavity;);
classify the acquired lumen image as any one of a plurality of types (¶ 0149: Other types of machine learning methods, such as recurrent neural networks (RNN) or convolutional neural network (CNN) systems (for the processing of image data)… The convolutional, pooling and ReLU layers may act as learnable features extractors, while the fully connected layers acts as a machine learning classifier.);
determine the insertion operation detail of the endoscope using an insertion operation selection model generated by machine learning when the lumen image is classified (¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; ¶ 0099: In some cases, the steering direction for the distal end of a colonoscope may be determined relative to a wall of the colonic lumen and/or relative to the center of the colonic lumen (emphasis added).; ¶ 0141: The steering control system may employ the artificial neural network (ANN) to determine steering.; Examiner’s note: As Yeung teaches a CNN that can perform classification in ¶ 0149, as well as determining steering directions based on the features of an image, it is understood that Yeung uses an machine learning architecture to determine the steering directions based on the “class” and “content” of an image.); and
determine the insertion operation detail of the endoscope using an algorithm for determining the insertion operation detail when the lumen image is classified (¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; ¶ 0099: In some cases, the steering direction for the distal end of a colonoscope may be determined relative to a wall of the colonic lumen and/or relative to the center of the colonic lumen. (emphasis added); ¶ 0141: The steering control system may employ the artificial neural network (ANN) to determine steering.; Examiner’s note: Regarding the algorithm of this claim, there is no distinction between it and a model that can perform the same function, specifically the models of Yeung ¶ 0141. As such, the examiner is interpreting the broad recitation of “an algorithm” to include models which are capable of performing the described task of “determine the insertion operation detail of the endoscope”. Additionally, with respect to ¶ 0099, Yeung discloses determining steering directions from various landmarks of the images such as the wall of the colonic lumen or the center of the colonic lumen. This is understood as determining multiple operation details based on the detected features or objects within an image.).
Yeung does not explicitly teach the lumen image is classified as a first type; and the lumen image is classified as a second type.
However Lee does teach the lumen image is classified as a first type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.); and
the lumen image is classified as a second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
This motivation for the combination of Yeung and Lee is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 2, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the one or more processors are configured to:
control a movement of the endoscope in accordance with the insertion operation detail determined using the insertion operation selection model (¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; ¶ 0099: In some cases, the steering direction for the distal end of a colonoscope may be determined relative to a wall of the colonic lumen and/or relative to the center of the colonic lumen. (emphasis added); ¶ 0141: The steering control system may employ the artificial neural network (ANN) to determine steering.); and
control the movement of the endoscope in accordance with the insertion operation detail determined using the algorithm (¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; ¶ 0099: In some cases, the steering direction for the distal end of a colonoscope may be determined relative to a wall of the colonic lumen and/or relative to the center of the colonic lumen. (emphasis added); ¶ 0141: The steering control system may employ the artificial neural network (ANN) to determine steering.; Examiner’s note: Regarding the algorithm of this claim, there is no distinction between it and a model that can perform the same function, specifically the models of Yeung ¶ 0141. As such, the examiner is interpreting the broad recitation of “an algorithm” to include models which are capable of performing the described task of “control the movement of the endoscope”. Additionally, with respect to ¶ 0099, Yeung discloses determining steering directions from various landmarks of the images such as the wall of the colonic lumen or the center of the colonic lumen. This is understood as determining multiple operation details based on the detected features or objects within an image.).
Additionally, Lee teaches the lumen image is classified as a first type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.); and
the lumen image is classified as a second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Regarding claim 3, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the one or more processors are configured to:
display, on a display apparatus, information on the insertion operation detail determined using the insertion operation selection model (¶ 0166: Some or all of the sensor data may be displayed on the GUI. In some cases, an image frame with augmented information may be displayed on the display screen. The augmented information may comprise still and/or moving images and/or information (such as text, graphics, charts, plots, and the like) to be overlaid onto an image frame displayed on a screen.); and
display, on the display apparatus, information on the insertion operation detail determined using the algorithm (¶ 0166: Some or all of the sensor data may be displayed on the GUI. In some cases, an image frame with augmented information may be displayed on the display screen. The augmented information may comprise still and/or moving images and/or information (such as text, graphics, charts, plots, and the like) to be overlaid onto an image frame displayed on a screen.).
Additionally, Lee teaches the lumen image is classified as a first type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.); and
the lumen image is classified as a second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Regarding claim 4, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the one or more processors are configured to classify the lumen image (Figure 5A; ¶ 0098: For instance, the environment of the colon lumen has many flexural looping or bending sections through which a colonoscope must be steered in order to navigate through the colon.; ¶ 0132: FIG. 5A shows exemplary images that comprise information about a location of the lumen center and/or a direction of the lumen relative to the distal end of a colonoscope. In some cases, the lumen center can be directly visualized in the image. In some cases, texture of the lumen wall may also be visible and may be indicative of the orientation or direction of the lumen immediately ahead of the distal end of the colonoscope. In some cases, the location of the lumen center may be determined from image data using suitable image processing techniques, e.g., feature extraction, edge detection, pattern recognition, pattern matching, and the like.).
Additionally, Lee teaches the lumen image is classified as a second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Regarding claim 7, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the insertion operation selection model is generated by machine learning using, as training data, an image for learning, which is a lumen image imaged in the past (¶ 0161: In some instance, the input data of the training dataset may comprise sensor data derived from at least one proximity sensor. In some instances, the input data of the training dataset may comprise sensor data from at least one image sensor.), and a label that is assigned to the image for learning and indicates an insertion operation detail of an endoscope (¶ 0091: As used herein, the term "machine learning" may refer to any of a variety of artificial intelligence algorithms (e.g., artificial neural networks (ANN) comprising multiple hidden layers) used to perform supervised learning (emphasis added), unsupervised learning, reinforcement learning, or any combination thereof.; ¶ 0161: The sensor data collected from the optical image sensor may comprise information indicative of a center position of a colon lumen or a location of an obstacle relative to the guiding portion ( e.g., distal end) of the robotic colonoscope.; ¶ 0162: FIG. 8B schematically illustrates another example of input sensor data and training datasets that may be supplied to a machine learning algorithm 803, e.g., a neural network, for learning the weighting factors and other parameters of the network that are used to map input values to the desired output).
Regarding claim 8, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the one or more processors are configured to determine the insertion operation detail of the endoscope using the algorithm based on information on a structural component in the lumen image when the lumen image is classified (¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; ¶ 0140: For example, images may be examined for the presence of a visible lumen center by extracting the specific feature of the lumen center using, for example, the pattern matching or pattern recognition methods as described elsewhere herein…. The steering control system may employ the artificial neural network (ANN) to determine steering direction and/or to provide steering control signals to the robotic endoscope.; Examiner’s note: Regarding the algorithm of this claim, there is no distinction between it and a model that can perform the same function, specifically the models of Yeung ¶ 0141. As such, the examiner is interpreting the broad recitation of “the algorithm” to include models which are capable of performing the described task of “determine the insertion operation detail of the endoscope”.).
Additionally, Lee teaches the lumen image is classified as a second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Regarding claim 9, the Yeung and Lee combination teaches the endoscope control system according to claim 8.
Additionally, Yeung teaches wherein the one or more processors are configured to determine the insertion operation detail of the endoscope using the algorithm based on information indicating a depth of the lumen image (¶ 0007: In some embodiments, the first input data stream or at least second input data stream comprise video data. In some embodiments, the first image sensor and at least second image sensor are used to provide a stereoscopic image that comprises depth information about the proximity of the walls of the lumen or other obstacles relative to the distal end of the robotic endoscope.; ¶ 0084: … determine a steering direction and/or generate a steering control output signal based on an analysis of the first and second input data streams using a machine learning architecture; Examiner’s note: Regarding the algorithm of this claim, there is no distinction between it and a model that can perform the same function, specifically the models of Yeung ¶ 0141. As such, the examiner is interpreting the broad recitation of “the algorithm” to include models which are capable of performing the described task of “determine the insertion operation detail of the endoscope”.).
Regarding claim 10, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Lee teaches wherein the one or more processors are configured to classify the lumen image as the first type or the second type using an image classification model generated by machine learning (Figure 2, Step S200 and S300; Col. 8, Line 28 – 31: inputting the image to an artificial neural network model 200 trained to classify an obtained image (operation S200), the artificial neural network model 200 classifying and outputting the image).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Regarding claim 11, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Yeung teaches wherein the one or more processors are configured to determine a series of insertion operation details of the endoscope using the algorithm which is suitable for a situation around a distal end of the endoscope when the lumen image is classified as the second type (¶ 0012: generate a steering control output signal based on an analysis of the first input data stream using a machine learning architecture, wherein the steering control output signal adapts to changes in the data of the first data stream in real time.; ¶ 0098: The environment may include any type of obstacle or contour that requires a determination of a new steering direction in order to avoid the obstacle or navigate around the contour.; ¶ 0141: The steering control system may employ the artificial neural network (ANN) to determine steering.; Examiner’s note: Regarding the algorithm of this claim, there is no distinction between it and a model that can perform the same function, specifically the models of Yeung ¶ 0141. As such, the examiner is interpreting the broad recitation of “an algorithm” to include models which are capable of performing the described task of “determine a series of insertion operation details of the endoscope”).
Regarding claim 13, the Yeung and Lee combination teaches the endoscope control system according to claim 4.
Additionally, Yeung teaches wherein the one or more processors are configured to determine
the insertion operation detail for changing a direction of a distal end of the endoscope to move the boundary of the bending portion near a center of the lumen image (¶ 0098: For instance, the environment of the colon lumen has many flexural looping or bending sections through which a colonoscope must be steered in order to navigate through the colon.; ¶ 0099: In some cases, once the position of the distal end relative to the lumen center and/or the surrounding wall are known, the distal end may be steered and guided towards the lumen center and away from contacting the colon wall or obstacles.; Examiner’s note: As written “move the boundary of the bending portion near a center of the lumen image” can be interpreted multiple ways, however the examiner is interpreting this to mean that the imaged boundary of the bending portion (the wall of the lumen that is near the center of the image as the upcoming bend places the boundary directly in front of the distal head) is moved in the images as the distal end’s direction changed. As the distal end navigates through a loop, this boundary will move in the lumen image in accordance with the movement of the distal end.),
the insertion operation detail for advancing the distal end (¶ 0098: For instance, the environment of the colon lumen has many flexural looping or bending sections through which a colonoscope must be steered in order to navigate through the colon.; ¶ 0101: Once the steering direction or direction of movement for the distal end is determined, the distal end may be actuated by one or more actuators to bend, turn, pivot, twist, or move in accordance with the steering direction.), and
the insertion operation detail for changing the direction of the distal end to a direction in which the lumen is estimated to exist after the distal end enters the bending portion (¶ 0098: For instance, the environment of the colon lumen has many flexural looping or bending sections through which a colonoscope must be steered in order to navigate through the colon.; ¶ 0131: In some instances, the image data may provide information about lumen wall contour that is useful for determining an orientation or direction of the colon relative to the distal end of the colonoscope).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yeung et al (U.S. Patent Publication No. 2018/0296281 A1, hereinafter “Yeung”) in view of Lee et al (U.S. Patent No. 12635848 B2, hereinafter “Lee”) and further in view of Nishimura et al (U.S. Patent Publication No. 2020/0138269 A1, hereinafter “Nishimura”).
Regarding claim 5, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Lee teaches wherein the one or more processors are configured to classify the lumen image as the second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Yeung does not explicitly teach wherein the lumen image includes a stenosis part.
However, Nishimura does teach wherein the lumen image includes a stenosis part (Figure 5; ¶ 0055: When the situation determination section 113 recognizes the collapse of the intestinal tract in a plurality of endoscopic images input to the image input section 112…).
Nishimura is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the insertion support device (as taught by Nishimura) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Nishimura improves the scope insertion situation by generating auxiliary information that prompts the scope to be straightened (See ¶ 0067).
This motivation for the combination of Yeung, Lee, and Nishimura is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 14, the Yeung and Lee combination teaches the endoscope control system according to claim 5.
Additionally, Yeung teaches wherein the one or more processors are configured to determine
the insertion operation detail for changing a direction of a distal end of the endoscope to move a lumen to a center of the lumen image (¶ 0098: Typically, the environment to be examined using the colonoscope ( or endoscope) may comprise a substantially hollow space, and the steering direction or steering control signal provided to the colonoscope (or endoscope) by the automated steering system may be a specified direction relative to a wall of the hollow space, or relative to an identified or predicted center point of the hollow space immediately ahead of the advancing distal end of the colonoscope (or endoscope).),
the insertion operation detail for changing the direction of the distal end to move an obstacle that interferes with the advancement of the distal end to a direction toward an outside of the lumen image (¶ 0098: The environment may include any type of obstacle or contour that requires a determination of a new steering direction in order to avoid the obstacle or navigate around the contour.; Examiner’s note: By steering the scope to navigate around an obstruction within the lumen, the scope’s distal end would move away from the obstruction, thus in the image, the obstruction would be moving toward an outside of the lumen image as the center of the image would be changed in accordance with the movement of the distal head around the obstruction.),
and the insertion operation detail for advancing the endoscope (¶ 0101: Once the steering direction or direction of movement for the distal end is determined, the distal end may be actuated by one or more actuators to bend, turn, pivot, twist, or move in accordance with the steering direction.).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yeung et al (U.S. Patent Publication No. 2018/0296281 A1, hereinafter “Yeung”) in view of Lee et al (U.S. Patent No. 12635848 B2, hereinafter “Lee”) and further in view of Mori et al (U.S. Patent Publication No. 2021/0022586 A1, hereinafter “Mori”).
Regarding claim 6, the Yeung and Lee combination teaches the endoscope control system according to claim 1.
Additionally, Lee teaches wherein the one or more processors are configured to classify the lumen image as the second type (Col. 9, Line 18 – 25: The image of the inside of the body, i.e., the endoscopic video, may be input to the trained artificial neural network model 200, the image may be classified and output as the air situation image, the water situation image, and/or the suction situation image, and the endoscopic device 100 may be controlled to drive the air unit 410, the water unit 420, and/or the suction unit 430 according to the output classification result.).
Lee is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic device (as taught by Lee) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Lee performs self-cleaning actions based on a classification of the images collected by the endoscope which substantially reduces the physical burden to the endoscopist and improves endoscopic diagnosis (See Col. 14, Line 1 - 8).
Yeung does not explicitly teach wherein the lumen image includes a diverticulum.
However, Mori does teach wherein the lumen image includes a diverticulum (¶ 0028: To be specific, the lesion information acquisition part 32 automatically detects a tumor, a polyp, bleeding, diverticulum…).
Mori is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscope observation assistance apparatus (as taught by Mori) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Mori uses an alarm sound and vibration to notify a doctor of a detected lesion which can suppress the continuation of the endoscopic inspection without the lesion confirmation by the doctor. Additionally, this prompts the doctor to move backward to the imaging area before the lesion disappearance which reconfirms the presence or status of a lesion, improving accuracy of the diagnosis by a doctor. (See ¶ 0051).
This motivation for the combination of Yeung, Lee, and Mori is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Yeung et al (U.S. Patent Publication No. 2018/0296281 A1, hereinafter “Yeung”) in view of Lee et al (U.S. Patent No. 12635848 B2, hereinafter “Lee”) and further in view of Kamijo et al (U.S. Patent Publication No. 2025/0134346 A1, hereinafter “Kamijo”).
Regarding claim 12, the Yeung and Lee combination teaches the endoscope control system according to claim 11.
Yeung does not explicitly teach wherein the one or more processors are configured to determine a completion of the determination of the series of insertion operation details of the endoscope using the algorithm based on the lumen image.
However, Kamijo does teach wherein the one or more processors are configured to determine a completion of the determination of the series of insertion operation details of the endoscope using the algorithm based on the lumen image (¶ 0084: The endoscopic examination assistance device according to Supplementary Note 1 or 2, in which, in a case where a position indicated by the imaging unit position information has passed a position indicated by the lesion position information, the notification control means causes the notification device to give notification that a lesion detected in the insertion step has been passed.).
Kamijo is considered to be analogous art as it pertains to endoscopic image analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the automated steering system (as taught by Yeung) and the endoscopic examination assistance device (as taught by Kamijo) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kamijo provides a notification according to a positional relationship between a lesion position information of the lesion detected and the imaging unit position, thus reducing the possibility of overlooking a lesion (See ¶ 0058).
This motivation for the combination of Yeung, Lee, and Kamijo is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Allowable Subject Matter
Claims 15 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 and overcame all claim objections associated with each claim.
Regarding claim 15, the prior art shows that it was known at the time of filing to determine the insertion operation detail for inserting a distal end of the endoscope into one of a plurality of lumen candidate regions, and
However, the prior art neither alone or in combination appears to teach or suggest determining the insertion operation detail for inserting the distal end into another lumen candidate region when the lumen candidate region into which the distal end is inserted is not the lumen.
Conclusion
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/ANDREW B. JONES/Examiner, Art Unit 2667
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