CTFR 18/396,864 CTFR 95852 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment Claims 1, 11-12 has been amended. Claim 13 has been newly added. Claims 1-13 are still pending for consideration. Terminal Disclaimer 14-23 AIA The terminal disclaimer filed on Feb 02, 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 18/396,841 has been reviewed and is accepted. The terminal disclaimer has been recorded. Applicant’s representative has filed a Terminal disclaimer to overcome the double patenting rejection, therefore, the double patenting rejection is now withdrawn. Response to Arguments Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1, 4-6, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. (US 5282030 A) in view of Paul (US 20170363701 A1), and further in view of Hirano (US 20220044047 A1) . Regarding claim 1, Nishimura et al. teaches an image processing device comprising: at least one memory configured to store instructions ( see col. 5, lines 41-42; “the outputs of the memories (1) 36a to (3) 36c are connected to an image processor 104”) ; and at least one processor configured to execute the instructions to: acquire k-space data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope ( see col. lines 60-66; “The CPU 121 comprises a ROM 121a containing a series of image processing programs which is described later, a Fourier transformation division 121b for applying two-dimensional Fourier transformation to endoscopic image signals decomposed into a plurality of color signals”, see also col. 6, lines 11-14; “an image of an object region acquired by an electronic endoscope 1 is processed by an image processor 104, then the processed results are output to a monitor 106” Note: Fourier transform image data corresponds to frequency space/ k-space data) . However, Nishimura et al. does not teach select, from the k-space data, partial data to be asymmetric with respect to at least one of a k-x axis or a k-y axis in a k-space of the k-space data, the partial data being used as input data to a lesion determination model so as to reduce an amount of input data without substantially reducing an accuracy of a lesion determination result, determine an attention point to be noticed in the examination target based on the lesion determination result output by the lesion determination model; and display the endoscopic image and at least one of determination information indicating the determination regarding the attention point, additional processing information indicating a processing result based on the determination information, and a frame surrounding a suspected region. In the same field of endeavor, Paul teaches select, from the k-space data, partial data to be asymmetric with respect to at least one of a k-x axis or a k-y axis in a k-space of the k-space data ( see para [0016]; “not all line sections, into which a k-space line was divided, are measured, but rather at least one side of the k-space center is completely scanned in the read-out direction. Missing portions of the k-space line can be obtained using the Hermitian symmetry”) , so as to reduce an amount of input data without substantially reducing an accuracy of a lesion determination result ( “While in the case of a k-space line that extends symmetrically around the k-space center, theoretically only half of this k-space line thus must actually be completely scanned. In practice more k-space data are acquired than the amount theoretically required, in order to be able to compensate for imperfections, for instance phase errors. An undersampling along a k-space line in the read-out direction is frequently also referred to as “partial Fourier” or asymmetric echo. Undersampling in the read-out direction permits the total recording time for the magnetic resonance data to be reduced further” ). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence Paul in order to improve robustness/speed on analyzing endoscopic images for lesion (see Abstract). However, the combination of Nishimura et al. and Paul as a whole does not teach the partial data being used as input data to a lesion determination model, determine an attention point to be noticed in the examination target based on the lesion determination result output by the lesion determination model; and display the endoscopic image and at least one of determination information indicating the determination regarding the attention point, additional processing information indicating a processing result based on the determination information, and a frame surrounding a suspected region. In the same field of endeavor, Hirano teach the partial data being used as input data to a lesion determination model ( see para [0104]; “by inputting some or all of the spectral values extracted from each of the sub-bands, the spectral values being extracted by the extracting function 355, to a classifier, the diagnosis aiding function 356 outputs an estimation result, such as the name of the disease, as diagnosis aiding information”), determine an attention point to be noticed in the examination target based on the lesion determination result output by the lesion determination model ( see para [0103]; “The spectral values extracted by the extracting function 355 will serve as feature quantities representing the features of the target tissue included in the ROI to be processed…the diagnosis aiding function 356 performs a process of determining (estimating) the name of the disease based on the feature quantities extracted by the extracting function 355…the spectral values being extracted by the extracting function 355, to a classifier, the diagnosis aiding function 356 outputs an estimation result, such as the name of the disease, as diagnosis aiding information”) ; and display the endoscopic image and at least one of determination information indicating the determination regarding the attention point, additional processing information indicating a processing result based on the determination information, and a frame surrounding a suspected region ( see para [0104]; “The diagnosis aiding function 356 outputs the diagnosis aiding information to the display 32”, see also para [0035]; “The display 22 displays various types of information. For example, the display 22 displays the result of processing performed by the processing circuitry 25”) . Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano in order to determine similarity of a spectral value at each point of a frequency space (see Abstract). Regarding claim 4, the rejection of claim 1 is incorporated herein. Hirano in the combination further teach wherein the at least one processor is configured to execute the instructions to select the partial data to be asymmetric with respect to at least one of a first axis and/or a second axis in a frequency domain which expresses the data by the first axis and the second axis( see Figs 12-13; disclose arbitrary target area mapping non-symmetric regions, Abstract; “to designate a target area in the frequency space represented by the second frequency component data based on the result of the determination”, see also para [0097]; “the sub-band setting function 354 divides the frequency band of the power spectrum acquired by the second acquiring function 351 into a plurality of the frequency ranges (sub-bands). It is assumed herein that the sub-band range into which the frequency band is to be divided is set in advance” and para [0099]; “The extracting function 355 extracts the spectral values of the frequency components designated as the effective frequencies as feature quantities, from the power spectrum acquired by the second acquiring function 351, based on the results of the process performed by the designating function 353. The extracting function 355 also extracts, for each of the sub-bands, the feature quantities of the frequency components designated as the effective frequencies, based on the sub-bands set by the sub-band setting function 354”). Regarding claim 5, the rejection of claim 1 is incorporated herein. Hirano in the combination further teach wherein the at least one processor is configured to execute the instructions to determine regarding the attention point, based on the partial data and a model into which the partial data is inputted, and wherein the model is a machine learning model which learned a relation between the partial data to be inputted to the model and35 a determination result regarding the attention point in the endoscopic image used for generation of the partial data ( see para [0104]; “by inputting some or all of the spectral values extracted from each of the sub-bands, the spectral values being extracted by the extracting function 355, to a classifier, the diagnosis aiding function 356 outputs an estimation result, such as the name of the disease, as diagnosis aiding information. The diagnosis aiding function 356 outputs the diagnosis aiding information to the display 32, for example. [0105] The classifier is not limited to a particular classifier, and any known technology may be used. For example, a support vector machine (SVM) classifier, a logistic regression classifier, a naive bayes classifier, or a decision tree classifier may be used as the classifier”). Regarding claim 6, the rejection of claim 1 is incorporated herein. Nishimura et al. in the combination further teach wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying two dimensional Fourier transform to the endoscopic image (see col. lines 60-66; “The CPU 121 comprises a ROM 121a containing a series of image processing programs which is described later, a Fourier transformation division 121b for applying two-dimensional Fourier transformation to endoscopic image signals decomposed into a plurality of color signals”); and wherein the at least one processor is configured to execute the instructions to generate the partial data in a selected partial range in at least one of the axes to which the Fourier transform is applied ( see col. 11, lines 28-45; “processed by two-dimensional discrete Fourier transformation at a step S51 in FIG. 10. Then, filtering having, for example, the band-pass characteristic shown in FIG. 15 is applied. A frequency band which is thought to contain a greatest number of characteristic components of endoscopic image is set as a band to be passed. Next, two-dimensional discrete Fourier inverse transformation is performed at a step S53 in FIG. 10. The image of 128.times.128 area (hereafter, characteristic image) including the center of the image) . Regarding claim 11, the scope of claim 11 is fully encompassed by the scope of claim 1, accordingly, the claim analysis of claim 1 is equally applicable here. Regarding claim 12, the scope of claim 12 is fully encompassed by the scope of claim 1, accordingly, the claim analysis of claim 1 is equally applicable here ( see also para [0018]; “a non-transitory, computer-readable data storage medium encoded with programming instructions that, when the storage medium is loaded into a control computer of a magnetic resonance apparatus” of Paul). Regarding claim 13, the rejection of claim 1 is incorporated herein. Hirano in the combination further teach wherein the partial data is asymmetric at least with respect to the k-x axis in the k-space of the k-space data ( see Figs 12-13; disclose arbitrary target area mapping non-symmetric regions, Abstract; “to designate a target area in the frequency space represented by the second frequency component data based on the result of the determination”, see also para [0097]; “the sub-band setting function 354 divides the frequency band of the power spectrum acquired by the second acquiring function 351 into a plurality of the frequency ranges (sub-bands). It is assumed herein that the sub-band range into which the frequency band is to be divided is set in advance” and para [0099]; “The extracting function 355 extracts the spectral values of the frequency components designated as the effective frequencies as feature quantities, from the power spectrum acquired by the second acquiring function 351, based on the results of the process performed by the designating function 353. The extracting function 355 also extracts, for each of the sub-bands, the feature quantities of the frequency components designated as the effective frequencies, based on the sub-bands set by the sub-band setting function 354”) . 07-21-aia AIA Claim s 2-3 rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. and Paul in view of Hirano as applied in claim 1 above and further in view of Ng et al. (US 20220028547 A1) . Regarding claim 2, the rejection of claim 1 is incorporated herein. The combination of Nishimura et al. Paul, and Hirano as a whole does not teach wherein the determination information includes information indicating a degree of an inflammation, and the additional processing information includes at least one of a name of a lesion and a degree of the lesion. In the same field of endeavor Ng et al. teach wherein the determination information includes information indicating a degree of an inflammation ( see Abstract; “methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients”), and the additional processing information includes at least one of a name of a lesion and a degree of the lesion ( see para [0132]; “the model 414 is configured to identify locations of potential malignancies in images. In some implementations, potential malignancies include polyps”, see also para [0136]; “determine one or more measurements of inflammation or other physical characteristic related to IBD”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and method for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) of Ng et al. in order to predict disease progression and treatment outcomes (see para [0104]). Regarding claim 3, the rejection of claim 1 is incorporated herein. Ng et al. in the combination further teach wherein the additional processing information includes at least one of a name of a lesion in the attention point and a degree of the lesion ( see para [0132]; “the model 414 is configured to identify locations of potential malignancies in images. In some implementations, potential malignancies include polyps”, see also para [0136]; “determine one or more measurements of inflammation or other physical characteristic related to IBD”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and method for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) of Ng et al. in order to predict disease progression and treatment outcomes (see para [0104]) . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. and Paul in view of Hirano as applied in claim 1 above and further in view of Kim (US 20180060997 A1) . Regarding claim 7, the rejection of claim 1 is incorporated herein. Nishimura et al. in the combination further teach and wherein the at least one processor is configured to execute the instructions to generate the partial data in a selected partial range in the axis to which the Fourier transform is applied ( see col. 11, lines 28-45; “processed by two-dimensional discrete Fourier transformation at a step S51 in FIG. 10. Then, filtering having, for example, the band-pass characteristic shown in FIG. 15 is applied. A frequency band which is thought to contain a greatest number of characteristic components of endoscopic image is set as a band to be passed. Next, two-dimensional discrete Fourier inverse transformation is performed at a step S53 in FIG. 10. The image of 128.times.128 area (hereafter, characteristic image) including the center of the image) . However, the combination of Nishimura et al. Paul, and Hirano as a whole does not teach wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying one-dimensional Fourier transform to the endoscopic image. In the same field of endeavor Kim et al. teach wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying one-dimensional Fourier transform to the endoscopic image ( see Abstract; “An image processing apparatus configured to perform a two-dimensional (2D) fast Fourier transform (FFT) with respect to image data includes a first core and a second core, each of the first core and the second core including a plurality of processors configured to perform a one-dimensional (1D) FFT”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and an image processing apparatus to perform a two-dimensional (2D) fast Fourier transform (FFT) with respect to image data of Kim et al. in order to reduce the computational amount and time for performing a Fourier transform (see Abstract) . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. and Paul in view of Hirano as applied in claim 1 above and further in view of de Boer et al. (US 20080097709 A1) herein after Boer . Regarding claim 8, the rejection of claim 1 is incorporated herein. The combination of Nishimura et al. Paul, and Hirano as a whole does not teach wherein the at least one processor is configured to execute the instructions to acquire the data that represents an absolute value or a phase into which a complex number for each frequency is converted, the complex number for each frequency being obtained by applying the Fourier transform to the endoscopic image. In the same field of endeavor Boer teaches wherein the at least one processor is configured to execute the instructions to acquire the data that represents an absolute value or a phase into which a complex number for each frequency is converted, the complex number for each frequency being obtained by applying the Fourier transform to the endoscopic image ( see para [0074]; “the signal may be reconstructed in the Fourier domain by adding the complex spectral components for each wavelength band to compose the Fourier transform of the LCI or OCT signal. Alterations of the phase for each Fourier component may be needed”, see also para [0075]; “As a result, the complex signal in the real domain (quadrature signal) is then reconstructed into axial reflectivity information by computing the amplitude of the real portion of the quadrature signal”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and method for increasing the sensitivity in the detection of optical coherence tomography and low coherence interferometry ("LCI") of Boer raise the SNR without appreciably increasing power requirements (see para [0074]) . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. and Paul in view of Hirano as applied in claim 1 above and further in view of Yoda et al. (US 20140269190 A1) . Regarding claim 9, the rejection of claim 1 is incorporated herein. The combination of Nishimura et al. Paul, and Hirano as a whole does not wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying logarithmic conversion to a value for each frequency, the value being obtained by applying the Fourier transform to the endoscopic image. In the same field of endeavor Yoda et al. teaches wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying logarithmic conversion to a value for each frequency, the value being obtained by applying the Fourier transform to the endoscopic image ( see para [0012]; “Calculate a logarithm of the power Pow[k] and adopt the logarithm as a gray value q of a kth pixel of an output line image.….Although not an essential process, this logarithmic conversion is normally performed”, see also para [0016]; “A beamspace method using DFT involves performing a Fourier transform by multiplying a delayed input signal vector X [t] by a Butler matrix B and using a Fourier coefficient corresponding to a low-frequency portion of the product as an input X [t] of the DCMP method”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and an object information acquiring apparatus converting acoustic wave into an electrical signal of Yoda et al. in order to facilitate visualization of an output image (see para [0012]) . 07-21-aia AIA Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nishimura et al. and Paul in view of Hirano as applied in claim 1 above and further in view of Kyperountas (US 20200305682 A1) . Regarding claim 10, the rejection of claim 1 is incorporated herein. The combination of Nishimura et al. Paul, and Hirano as a whole does not teach wherein the at least one processor is configured to further execute the instructions to determine a coping method based on information regarding a result of the determination and a model into which the information regarding the result of the determination is inputted, wherein the model is a machine learning model which learned relation between information regarding a result of the determination to be inputted to the model and the coping method according to the result of the determination. In the same field of endeavor Kyperountas teaches wherein the at least one processor is configured to further execute the instructions to determine a coping method based on information regarding a result of the determination ( see Fig. 4 steps 440-470, para [0054]; “At block 440, a recommended action may be generated during a live procedure…. If it is determined that the confidence score indicative of a likelihood that the first response action is a correct action is greater than or equal to the threshold, such as an automated action threshold, the process flow 400 may proceed to block 460, at which the recommended action may be automatically implemented. If it is determined that the confidence score indicative of a likelihood that the first response action is a correct action is less than the threshold, such as the automated action threshold, the process flow 400 may proceed to block 470, at which manual approval of the recommended action may be requested”) and a model into which the information regarding the result of the determination is inputted ( see Fig. 5, steps 510, 550, and 560, para [0035]; “the endoscopic device control system may determine a response action that corresponds to the detected condition using the trained model and/or a remote server may determine a response action that corresponds to the detected condition by executing one or more neural networks”) , wherein the model is a machine learning model which learned relation between information regarding a result of the determination to be inputted to the model and the coping method according to the result of the determination( see para [0051]; “a remote server may use the data captured by the automated control system as inputs to train a training model for use in generating automated actions. [0052] At block 420, a training model may be generated using learning data. For example, the automated control system and/or remote server may generate a training model using the learning data. In some embodiments, neural networks may be used to generate a training model and/or implement a trained model” Note; action/recommendation implies a “coping method”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method to provide an endoscopic image processor permitting accurate observation of fine patterns in the mucosal surface of a living body of Nishimura et al. in view of the use of a method and magnetic resonance apparatus for recording magnetic resonance data using a bSSFP sequence of Paul and a medical information processing apparatus of Hirano and method for automated endoscopic device control systems of Kyperountas in order to get actionable guidance in procedure (see para [0054]). Conclusion 07-40 AIA 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WINTA GEBRESLASSIE/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/396,864 Page 2 Art Unit: 2677 Application/Control Number: 18/396,864 Page 3 Art Unit: 2677 Application/Control Number: 18/396,864 Page 4 Art Unit: 2677 Application/Control Number: 18/396,864 Page 5 Art Unit: 2677 Application/Control Number: 18/396,864 Page 6 Art Unit: 2677 Application/Control Number: 18/396,864 Page 7 Art Unit: 2677 Application/Control Number: 18/396,864 Page 8 Art Unit: 2677 Application/Control Number: 18/396,864 Page 9 Art Unit: 2677 Application/Control Number: 18/396,864 Page 10 Art Unit: 2677 Application/Control Number: 18/396,864 Page 11 Art Unit: 2677 Application/Control Number: 18/396,864 Page 12 Art Unit: 2677 Application/Control Number: 18/396,864 Page 13 Art Unit: 2677 Application/Control Number: 18/396,864 Page 14 Art Unit: 2677 Application/Control Number: 18/396,864 Page 15 Art Unit: 2677 Application/Control Number: 18/396,864 Page 16 Art Unit: 2677 Application/Control Number: 18/396,864 Page 17 Art Unit: 2677 Application/Control Number: 18/396,864 Page 18 Art Unit: 2677
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