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 .
Status of Claims
Applicant' s arguments, filed 08/28/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 08/28/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicant canceled claims 8, 10, and 11 in the response filed on 08/28/2025.
Claims 1-7, 9, 12 and 13 are the current claims hereby under examination.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed 10/16/2025 has been considered.
Claim Objections
Claim 13 is objected to because of the following informalities: “performing a process” in line 21 should read “perform a process”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7, 9, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Hane (WO 2018135018 – cited by Applicant) in view of Nakae (US Patent Pub. No. 20180242904). US Patent Pub. No. 20190335981 (previously cited) is being used as the English translation of Hane.
Regarding Claim 1, Hane discloses an endoscope system (an endoscope system that is a force estimation system 10 [0061]; The force estimation systems 10 and 10A according to the present embodiment are applied to an endoscope system including an endoscope insertion portion that is the flexible tubular portion 12. [0071]; the endoscope system 10′ [0072]; figs 1 & 3) comprising:
an endoscope (an endoscope insertion portion that is a flexible tubular portion 12 inserted into a lumen of an object O [0061]; The endoscope 16 is provided with an elongated endoscope insertion portion 12′ that is the flexible tubular portion 12 [0074]; figs 1 & 3);
and a processor comprising hardware (force estimation system 10 includes a force estimation system 14 [0061]; All or part of the components of the force estimation systems 14 and 14A excluding the sensor 56 as described above may not be configured as a hardware circuit. That is, the computer processor (not illustrated) and the memory provided in the housing 102 are accommodated, a software program for causing the computer processor to function as all or part of the components is prepared in the memory, and a processor executes that program. [0309]; fig 1), the processor being configured to:
acquire insertion shape information of an insertion section of the endoscope during an examination of a subject, wherein the examination uses the endoscope and the insertion shape information indicates a shape of the insertion section (as the shape sensor 66, the entire shape can be detected by arranging a plurality of bending sensors in the endoscope insertion portion 12′. [0089]; A shape calculation unit 100 actually calculates and outputs the curvature of the bent shape of the endoscope insertion portion 12′ based on the shape signal from the light detection unit 98. [0107]; in the case where the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A are configured by software, a software program for performing an operation as illustrated in FIG. 22 is stored in a memory (not illustrated), and when a processor (not illustrated) executes the program, the processor can function as the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A. [0313]);
acquire examination state information of the examination during the examination, wherein the examination state information is different from the insertion shape information, and the examination state information indicates a state of at least one of the subject, the examination, and the endoscope (a force estimation system 10A that detects a force applied to a flexible tubular portion 12 based on a bending moment applied to the flexible tubular portion 12. In this case, a force estimation system 14A estimates a first bending moment applied to the flexible tubular portion 12 based on a shape and a bending stiffness at a plurality of longitudinal positions of the flexible tubular portion 12, and calculates force information based on the estimated first bending moment. [0064]; the endoscope system 10′ includes an endoscope 16 that captures an image of an observation object with an imaging unit provided at the distal end of the endoscope insertion portion, an image processing device 18 (video processor) [0072]; the force estimation system 14 includes a coordinate and deformation state measurement circuit 44, a mechanical characteristic memory circuit 46, a force calculation circuit 48, an object influence determination circuit 50, an information presentation device or driving feedback circuit 52 [0081]; the force estimation system 14A includes a coordinate and shape measurement circuit 44A, a bending stiffness memory circuit 46A, [0082]; figs 1-3);
classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, wherein the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination (object influence determination circuit 50 can include a determination criteria memory circuit 62...determination criteria memory circuit 62 stores, as the determination criteria, the degree of damage given to the object O, such as pain, breakage, or perforation given to the object O as the influence of the force information on the object O [0280]; fig 19A; Examiner notes insertion shape information and examination state information are used to determine force information. Therefore, the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination);
and generate operation guide information for guiding an insertion operation of the insertion section according to the degree of pain (object influence determination circuit 50 serving as the object influence determination unit can further include an operation information generation circuit 120 serving as an operation information generation unit that generates operation information (operation method, operation procedure, avoidance/precautions) of the flexible tubular portion 12 so as to avoid the influence on the object O [0485]).
Hane fails to disclose the processor being configured to run a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, wherein the machine learning model is trained using training data that includes pre-collected insertion shape information and pre- collected examination state information as training inputs, and pre-collected corresponding pain information as training labels.
However, Nakae teaches a method of pain estimation using a machine learning model (For example, the method of estimation may use regression analysis or machine learning. [0092]). The machine learning model is constructed using known variable data and their corresponding pain level (For example, the method of estimation may use regression analysis or machine learning. For example, the estimation unit 14A may estimate a hypothetical pain level and brainwave data of the subject of measurement 99 upon application of a stimulation at a stimulation amount that is not actually applied to the subject of measurement 99, by applying regression analysis on a pain level reported by the subject of measurement 99 and brainwave data measured from the subject of measurement 99, when stimulations at a plurality of stimulation amounts are applied individually to the subject of measurement 99. As another example, the estimation unit 14A may estimate a pain level and brainwave data for a stimulation amount that is not actually applied to the subject of measurement 99, by using a model constructed by machine learning using a pain level and brainwave data obtained by applying a stimulation to a living body that is different from the subject of measurement 99 as training data. [0092]).
Hane is considered analogous art to the present invention because it is directed towards the same field of endeavor. Nakae is considered analogous art to the present invention because it is reasonably pertinent to a problem faced by the inventors.
Hane discloses classifying the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject. Nakae teaches using a machine learning model to classify variable data into a degree of pain of a subject, wherein pre-collected variable data is used as inputs and pre-collected corresponding pain information is used as labels train the machine learning model. Therefore, it would have been obvious to one having ordinary skill in the art at the time of the effective date to have modified the endoscope system of Hane such that the processor being configured to run a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, wherein the machine learning model is trained using training data that includes pre-collected insertion shape information and pre- collected examination state information as training inputs, and pre-collected corresponding pain information as training labels because it would allow for a more dynamic and robust evaluation of the degree of pain of the subject. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, D.).
Regarding Claim 2, Hane in view of Nakae teaches the invention as discussed above in claim 1. Hane further discloses the insertion shape information indicates a plurality of curvatures of the insertion section at a plurality of positions (shape measurement circuit 44A is configured to detect positions (coordinates) and deformation states (stretching, bending, twisting, shape, and the like) at a plurality of positions of the endoscope insertion portion 12′, which is the flexible tubular portion 12 [0085]).
Regarding Claim 3, Hane in view of Nakae teaches the invention as discussed above in claim 1. Hane further discloses the processor is configured to use the acquired insertion shape information to generate an insertion shape image showing the insertion shape of the insertion section of the endoscope (The shape is preferably expressed by a curvature. The curvature represents the degree of bending at a specific point of the endoscope insertion portion 12′, but may be represented by a specific range of bending amount, that is, a bending angle, instead of the curvature. [0118]; For example, in FIG. 10, a plurality of dots represent the coordinates detected by the position sensor 64, and connecting these with a curve or the like is an example of interpolation. Similarly, if the shape is known and the coordinates of at least one dot are known, the coordinates and the shape at any position from that point can be obtained by calculation. [0120]; Examiner notes that the interpolation of the plurality of dots constitutes an image). The modified endoscope system of Hane in view of Nakae teaches the processor is configured to input the generated insertion shape image into the machine learning model to run the machine learning model (Examiner notes the generated insertion shape image would be variable data that has a corresponding pain level and therefore, be used as an input for the machine learning model).
Regarding Claim 4, Hane in view of Nakae teaches the invention as discussed above in claim 1. Hane further discloses the examination state information is analysis information indicating results of an analysis processing on an endoscopic image showing a position of the endoscope relative to the subject (an image processing device 18 (video processor) that performs image processing on the image capturing result, and a monitor 20 that is a display unit connected to the image processing device 18 and displays the observation image that has been captured and image-processed. Here, the observation object is an affected part, a lesioned part, or the like in the object O (for example, the body cavity (lumen)). [0072]).
Regarding Claim 5, Hane in view of Nakae teaches the invention as discussed above in claim 2. Hane further discloses the examination state information is predefined rigidity information of the insertion section of the endoscope (a force estimation system 14A estimates a first bending moment applied to the flexible tubular portion 12 based on a shape and a bending stiffness at a plurality of longitudinal positions of the flexible tubular portion 12 [0064]; the bending stiffness memory circuit 46A is a semiconductor memory that stores the bending stiffness of each segment. The bending stiffness is an index indicating the bending difficulty of each segment of the endoscope insertion portion 12′, which is one of the mechanical characteristics of each segment of the endoscope insertion portion 12′. [0135]).
Regarding Claim 7, Hane in view of Nakae teaches the invention as discussed above in claim 2. Hane further discloses the examination state information is insertion length information indicating an insertion length of the insertion section inserted into the subject (division method for determining the length or the like of the segment is assumed to correspond to the calculation accuracy required to calculate the force information [0130]).
Regarding Claim 9, Hane in view of Nakae teaches the invention as discussed above in claim 1. Hane further discloses the processor is further configured to generate pain level information and perform display processing of the pain level information (object influence determination circuit 50 can include a determination criteria memory circuit 62...determination criteria memory circuit 62 stores, as the determination criteria, the degree of damage given to the object O, such as pain, breakage, or perforation given to the object O as the influence of the force information on the object O [0280]; the information presentation device processes at least one of the force information obtained from the force calculation circuit 48 or 48A and the influence determination result obtained from the object influence determination circuit 50, or presentation information to be presented to the operator as appropriate based on the information, and provides the presentation information to the operator [0286]; fig 19A).
Regarding Claim 12, Hane discloses a method for determining a degree of pain of a subject undergoing an examination that uses an endoscope (an endoscope system that is a force estimation system 10 [0061]; object influence determination circuit 50 determines the influence of the force information calculated by the force calculation circuit 48 or 48 A on the object O based on the determination criteria stored in the determination criteria memory circuit 62 [0278]; Examiner notes that determining the influence of the force information is a step and therefore constitutes a method), the method comprising:
acquiring, by a processor, insertion shape information of an insertion section of the endoscope during the examination (as the shape sensor 66, the entire shape can be detected by arranging a plurality of bending sensors in the endoscope insertion portion 12′. [0089]; A shape calculation unit 100 actually calculates and outputs the curvature of the bent shape of the endoscope insertion portion 12′ based on the shape signal from the light detection unit 98. [0107]; in the case where the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A are configured by software, a software program for performing an operation as illustrated in FIG. 22 is stored in a memory (not illustrated), and when a processor (not illustrated) executes the program, the processor can function as the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A. [0313]);
acquiring, by the processor, examination state information of the examination during the examination (a force estimation system 10A that detects a force applied to a flexible tubular portion 12 based on a bending moment applied to the flexible tubular portion 12. In this case, a force estimation system 14A estimates a first bending moment applied to the flexible tubular portion 12 based on a shape and a bending stiffness at a plurality of longitudinal positions of the flexible tubular portion 12, and calculates force information based on the estimated first bending moment. [0064]; the endoscope system 10′ includes an endoscope 16 that captures an image of an observation object with an imaging unit provided at the distal end of the endoscope insertion portion, an image processing device 18 (video processor) [0072]; the force estimation system 14 includes a coordinate and deformation state measurement circuit 44, a mechanical characteristic memory circuit 46, a force calculation circuit 48, an object influence determination circuit 50, an information presentation device or driving feedback circuit 52 [0081]; the force estimation system 14A includes a coordinate and shape measurement circuit 44A, a bending stiffness memory circuit 46A, [0082]; force estimation system 10 includes a force estimation system 14 [0061]; All or part of the components of the force estimation systems 14 and 14A excluding the sensor 56 as described above may not be configured as a hardware circuit. That is, the computer processor (not illustrated) and the memory provided in the housing 102 are accommodated, a software program for causing the computer processor to function as all or part of the components is prepared in the memory, and a processor executes that program. [0309]; figs 1-3);
classifying the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, wherein the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination (object influence determination circuit 50 can include a determination criteria memory circuit 62...determination criteria memory circuit 62 stores, as the determination criteria, the degree of damage given to the object O, such as pain, breakage, or perforation given to the object O as the influence of the force information on the object O [0280]; fig 19A; Examiner notes insertion shape information and examination state information are used to determine force information. Therefore, the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination),
and generating operation guide information for guiding an insertion operation of the insertion section according to the degree of pain (object influence determination circuit 50 serving as the object influence determination unit can further include an operation information generation circuit 120 serving as an operation information generation unit that generates operation information (operation method, operation procedure, avoidance/precautions) of the flexible tubular portion 12 so as to avoid the influence on the object O [0485]).
Hane fails to disclose running, by the processor, a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, and the machine learning model is trained using training data that includes pre-collected insertion shape information and pre-collected examination state information as training inputs, and pre-collected corresponding pain information as training labels.
However, Nakae teaches a method of pain estimation using a machine learning model (For example, the method of estimation may use regression analysis or machine learning. [0092]). The machine learning model is constructed using known variable data and their corresponding pain level (For example, the method of estimation may use regression analysis or machine learning. For example, the estimation unit 14A may estimate a hypothetical pain level and brainwave data of the subject of measurement 99 upon application of a stimulation at a stimulation amount that is not actually applied to the subject of measurement 99, by applying regression analysis on a pain level reported by the subject of measurement 99 and brainwave data measured from the subject of measurement 99, when stimulations at a plurality of stimulation amounts are applied individually to the subject of measurement 99. As another example, the estimation unit 14A may estimate a pain level and brainwave data for a stimulation amount that is not actually applied to the subject of measurement 99, by using a model constructed by machine learning using a pain level and brainwave data obtained by applying a stimulation to a living body that is different from the subject of measurement 99 as training data. [0092]).
Hane discloses classifying the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject. Nakae teaches using a machine learning model to classify variable data into a degree of pain of a subject, wherein pre-collected variable data is used as inputs and pre-collected corresponding pain information is used as labels train the machine learning model. Therefore, it would have been obvious to one having ordinary skill in the art at the time of the effective date to have modified the method of Hane such that it includes running, by the processor, a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, and the machine learning model is trained using training data that includes pre-collected insertion shape information and pre-collected examination state information as training inputs, and pre-collected corresponding pain information as training labels because it would allow for a more dynamic and robust evaluation of the degree of pain of the subject. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, D.).
Regarding Claim 13, Hane discloses a non-transitory recording medium recording a program (the computer processor (not illustrated) and the memory provided in the housing 102 are accommodated, a software program for causing the computer processor to function as all or part of the components is prepared in the memory, and a processor executes that program [0309]), the program causing a computer to:
perform a process for acquiring insertion shape information of an insertion section of an endoscope during an examination of a subject, wherein the examination uses the endoscope and the insertion shape information indicates a shape of the insertion section (as the shape sensor 66, the entire shape can be detected by arranging a plurality of bending sensors in the endoscope insertion portion 12′. [0089]; A shape calculation unit 100 actually calculates and outputs the curvature of the bent shape of the endoscope insertion portion 12′ based on the shape signal from the light detection unit 98. [0107]; in the case where the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A are configured by software, a software program for performing an operation as illustrated in FIG. 22 is stored in a memory (not illustrated), and when a processor (not illustrated) executes the program, the processor can function as the coordinate and shape measurement circuit 44A excluding the sensor 56 and the force calculation circuit 48A. [0313]);
perform a process for acquiring examination state information of the examination during the examination (a force estimation system 10A that detects a force applied to a flexible tubular portion 12 based on a bending moment applied to the flexible tubular portion 12. In this case, a force estimation system 14A estimates a first bending moment applied to the flexible tubular portion 12 based on a shape and a bending stiffness at a plurality of longitudinal positions of the flexible tubular portion 12, and calculates force information based on the estimated first bending moment. [0064]; the endoscope system 10′ includes an endoscope 16 that captures an image of an observation object with an imaging unit provided at the distal end of the endoscope insertion portion, an image processing device 18 (video processor) [0072]; the force estimation system 14 includes a coordinate and deformation state measurement circuit 44, a mechanical characteristic memory circuit 46, a force calculation circuit 48, an object influence determination circuit 50, an information presentation device or driving feedback circuit 52 [0081]; the force estimation system 14A includes a coordinate and shape measurement circuit 44A, a bending stiffness memory circuit 46A, [0082]; figs 1-3);
perform a process to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, wherein the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination (object influence determination circuit 50 can include a determination criteria memory circuit 62...determination criteria memory circuit 62 stores, as the determination criteria, the degree of damage given to the object O, such as pain, breakage, or perforation given to the object O as the influence of the force information on the object O [0280]; fig 19A; Examiner notes insertion shape information and examination state information are used to determine force information. Therefore, the classified degree of pain corresponds to the acquired insertion shape information and the acquired examination state information during the examination);
and performing a process for generating operation guide information for guiding an insertion operation of the insertion section according to the degree of pain (object influence determination circuit 50 serving as the object influence determination unit can further include an operation information generation circuit 120 serving as an operation information generation unit that generates operation information (operation method, operation procedure, avoidance/precautions) of the flexible tubular portion 12 so as to avoid the influence on the object O [0485]).
Hane fails to disclose the program causes the computer perform a process for running a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, and the machine learning model is trained using training data that includes pre-collected insertion shape information and pre-collected examination state information as training inputs, and pre-collected corresponding pain information as training labels.
However, Nakae teaches a method of pain estimation using a machine learning model (For example, the method of estimation may use regression analysis or machine learning. [0092]). The machine learning model is constructed using known variable data and their corresponding pain level (For example, the method of estimation may use regression analysis or machine learning. For example, the estimation unit 14A may estimate a hypothetical pain level and brainwave data of the subject of measurement 99 upon application of a stimulation at a stimulation amount that is not actually applied to the subject of measurement 99, by applying regression analysis on a pain level reported by the subject of measurement 99 and brainwave data measured from the subject of measurement 99, when stimulations at a plurality of stimulation amounts are applied individually to the subject of measurement 99. As another example, the estimation unit 14A may estimate a pain level and brainwave data for a stimulation amount that is not actually applied to the subject of measurement 99, by using a model constructed by machine learning using a pain level and brainwave data obtained by applying a stimulation to a living body that is different from the subject of measurement 99 as training data. [0092]).
Hane discloses classifying the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject. Nakae teaches using a machine learning model to classify variable data into a degree of pain of a subject, wherein pre-collected variable data is used as inputs and pre-collected corresponding pain information is used as labels train the machine learning model. Therefore, it would have been obvious to one having ordinary skill in the art at the time of the effective date to have modified the non-transitory recording medium of Hane such that the program causes the computer perform a process for running a machine learning model to classify the acquired insertion shape information and the acquired examination state information into a degree of pain of the subject, and the machine learning model is trained using training data that includes pre-collected insertion shape information and pre-collected examination state information as training inputs, and pre-collected corresponding pain information as training labels because it would allow for a more dynamic and robust evaluation of the degree of pain of the subject. Applying a known technique to a known device (method or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, D.).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hane (WO 2018135018 – cited by Applicant) in view of Nakae (US Patent Pub. No. 20180242904) as applied to claim 2 above, and further in view of Uesugi et al. (US Patent Pub. No. 20070255165 – previously cited) hereinafter Uesugi. US Patent Pub. No. 20190335981 is being used as the English translation of Hane.
Regarding Claim 6, Hane in view of Nakae teaches the invention as discussed above in claim 2. Hane in view of Nakae fails to teach the examination state information is gas-feeding information indicating a detection result of an operation state of a gas-feeding pump being used during the examination.
However, Uesugi teaches acquiring gas-feeding information indicating a detection result of an operation state of a gas-feeding pump being used during an endoscopic examination (air port could be connected to a gas source such as a pump or compressed gas source [0149]; an endoscope connected to the air supply apparatus [abstract]).
Uesugi is considered prior art to the present invention because it is directed towards the same field of endeavor.
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to have modified the endoscope system of Hane in view of Nakae such that it includes a gas-feeding unit and gas-feeding information indicating a detection result of an operation state of a gas-feeding pump being used during the examination is acquired, as taught by Uesugi, because gas is used in a lot of endoscopic procedures for "adequate distension of the GI lumen... and for careful visualization of the mucosa (abstract)" as evidenced by Lo et al. (The use of carbon dioxide in gastrointestinal endoscopy – previously cited). The combination of familiar elements is likely to be obvious when it does no more than yield predictable results. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395 — 97 (2007) (see MPEP § 2143, A.).
The modified endoscopy system of Hane in view of Nakae and further in view of Uesugi would be configured to acquire examination state information, wherein the examination state information is gas-feeding information indicating a detection result of an operation state of a gas-feeding pump being used during the examination.
Response to Arguments
Applicant’s arguments, see pages 9-11 of Remarks, filed 08/28/2025, with respect to the 35 U.S.C. 112(a) and 112(b) rejections have been fully considered and are persuasive. The 35 U.S.C. 112(a) and 112(b) rejections have been withdrawn.
Applicant’s arguments, see pages 11-14 of Remarks, filed 08/28/2025, with respect to the 35 U.S.C. 101 rejections have been fully considered and are persuasive. The 35 U.S.C. 101 rejections have been withdrawn.
Applicant's arguments, see pages 14-17 of Remarks, filed 08/28/2025, with respect to the 35 U.S.C. 102 and 103 rejections have been fully considered but are not persuasive and are moot in view of the new grounds of rejections.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANKI M BAVA whose telephone number is (571)272-0416. The examiner can normally be reached Monday-Friday 9:00-6:00 ET.
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/JANKI M BAVA/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791