Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1, 3, 9, 10, 11 has been amended.
Claims 12-13 have been newly added.
Claims 1-13 are still pending for consideration.
Response to Arguments
Regarding Double Patenting, applicant states “Applicant respectfully traverses all of these rejections. However, since the above rejections are provisional rejections, based upon a pending patent application, Applicant elects to defer addressing the merits of the provisional rejections until the cited pending application issues. Such deferral of addressing the merits of the rejections is clearly contemplated by MPEP § 804(I)(B), which states that a "provisional" double patenting rejection is designed simply to make Applicant aware of a potential problem”.
Response: a provisional nonstatutory patenting rejection is still a rejection requiring a complete reply. MPEP § 804 states that “A complete response to a nonstatutory double patenting (NSDP) rejection is either a reply by applicant showing that the claims subject to the rejection are patentably distinct from the reference claims, or the filing of a terminal disclaimer in accordance with 37 CFR 1.321 in the pending application(s) with a reply to the Office action (see MPEP § 1490 for a discussion of terminal disclaimers). Such a response is required even when the nonstatutory double patenting rejection is provisional”.
Accordingly, Applicant’s statement that it elects to defer addressing the merits of the provisional rejection does not overcome the rejection. Therefore, the provisional nonstatutory double patenting rejection is maintained.
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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-12 of copending Application No. 18/396,841 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the invention variously defined by the claims of the instant application is anticipated and/or is an obvious variant of the invention as stipulated by the claims of the 18/288,689 application. The nominal differences are bold and/or underlined for distinction. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Below is a table showing the conflicting claims:
18288689
18396841
1.(Currently Amended) An image processing device comprising: at least one memory configured to store instructions; 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 a photographing unit provided in an endoscope;
select, from the k-space data, partial data to be asymmetric with respect to at least one of a k-x axis and/or a k-y axis in a k-space of the k-space data, to reduce an amount of data to be processed while suppressing deterioration of accuracy of a determination regarding an attention point and make the determination regarding the attention point to be noticed in the examination target based on the partial data
1.An image processing device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to:
acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope;
select partial data that is a part of the data; determine an attention point to be noticed in the examination target based on the partial data; and determine a state of a lesion based on the attention point.
2. (Currently Amended) The image processing device according to claim 1, 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.
3. The image processing device according to claim 1, 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.
3. (Currently Amended) The image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to make the determination 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; and a determination result regarding the attention point in the endoscopic image used for generation of the partial data.
4. The image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to determine the attention point and the state of lesion, 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 and a determination result regarding the attention point and the state of the lesion in the endoscopic image used for generation of the partial data.
4. (Currently Amended) The image processing device according to claim 1, 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, and wherein the selection means is configured 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.
5. (Currently Amended) The image processing device according to claim 1, 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, and wherein the selection means is configured 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.
6. (Currently Amended) The image processing device according to claim 1, 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.
7. (Currently Amended) The image processing device according to claim 1, 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.
8. (Currently Amended) The image processing device according to claim 1, wherein the at least one processor is configured to further execute the instructions to display information regarding a result of the determination and the endoscopic image on a display device.
9. (Currently Amended) The image processing device according to claim 1, 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.
10. (Original) An image processing method executed by a computer, the image processing method comprising: acquiring k-space data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope; select, from the k-space data, partial data to be asymmetric with respect to at least one of a k-x axis and/or a k-y axis in a k-space of the k-space data, to reduce an amount of data to be processed while suppressing deterioration of accuracy of a determination regarding an attention point and make the determination regarding the attention point to be noticed in the examination target based on the partial data
11. (Currently Amended) A non-transitory computer readable storage medium storing a program executed by a computer, the program causing the computer to: acquire k-space data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by a photographing unit provided in an endoscope; select, from the k-space data, partial data to be asymmetric with respect to at least one of a k-x axis and/or a k-y axis in a k-space of the k-space data, to reduce an amount of data to be processed while suppressing deterioration of accuracy of a determination regarding an attention point and make the determination regarding the attention point to be noticed in the examination target based on the partial data
5. The image processing device according to claim 1, 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, 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.
6. The image processing device according to claim 1, 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, 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.
7. The image processing device according to claim 1, 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.
8. The image processing device according to claim 1, 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.
9. The image processing device according to claim 1, wherein the at least one processor is configured to further execute the instructions to display information regarding a result of the determination and the endoscopic image on a display device.
10. The image processing device according to claim 1, 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.
11. An image processing method executed by a computer, the image processing method comprising: acquiring data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope; selecting partial data that is a part of the data; determine an attention point to be noticed in the examination target based on the partial 5 data; and determine a state of a lesion based on the attention point.
12. A non-transitory computer readable storage medium storing a program executed by a computer, the program causing the computer to: acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope; select partial data that is a part of the data; and determine an attention point to be noticed in the examination target based on the partial data; and is determine a state of a lesion based on the attention point.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-4, 8, and 10-11 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 a photographing unit provided in 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); select partial data that is a part of the data (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” Note: the system keeps (passes) only the coefficients whose spatial frequencies fall inside a chosen range (the band) and suppresses those outside that range. Choosing a frequency range (the band-pass) is functionally the same as selecting a subset/partial 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 and/or a k-y axis in a k-space of the k-space data, to reduce an amount of data to be processed while suppressing deterioration of accuracy of a determination regarding an attention point and make the determination regarding the attention point to be noticed in the examination target based on the partial data.
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 and/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”), to reduce an amount of data to be processed while suppressing deterioration of accuracy of a determination regarding an attention point (see para [0016]; “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 and make the determination regarding the attention point to be noticed in the examination target based on the partial data.
In the same field of endeavor, Hirano teaches and make the determination regarding the attention point to be noticed in the examination target based on the partial data (see para [0114]; “The extracting function 355 then extracts the spectral values of the effective frequencies from each of the sub-bands of the power spectrum acquired at Step S21…..The diagnosis aiding function 356 determines (estimates) a disease or the like of the subject, whose image is being processed, based on the spectral values in each of the sub-bands”, see also para [0117]; “it is possible to designate an area of frequency components corresponding to the target tissue in the frequency space represented by the power spectrum, as a target area, ….it is possible to extract the data representing a feature of a target tissue efficiently, by extracting spectral values from the designated target area”). 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 2, 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 3, 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 make the determination 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 and 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 4, 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 8, the rejection of claim 1 is incorporated herein.
Hirano in the combination further teach wherein the at least one processor is configured to further execute the instructions to display information regarding a result of the determination and the endoscopic image on a display device (see 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”, see also para [0104]; “The diagnosis aiding function 356 outputs the diagnosis aiding information to the display 32”).
Regarding claim 10, the scope of claim 10 is fully encompassed by the scope of claim 1, accordingly, the claim analysis of claim 1 is equally applicable here.
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 (see also para [0018]; “The present invention also encompasses 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 12, the rejection of claim 1 is incorporated herein.
Paul in the combination further teach wherein the partial data includes frequency components on one side of the k-x axis and excludes frequency components on an opposite side of the k-x axis (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 …..An undersampling along a k-space line in the read-out direction is frequently also referred to as “partial Fourier” or asymmetric echo”, see also para [0033]; “perform an undersampling along the k-space line 1 and to entirely omit the scanning of the line section 15, since due to the Hermitian symmetry in the read-out direction 7 in k-space”).
Regarding claim 13, the rejection of claim 1 is incorporated herein.
Hirano in the combination further teach wherein the partial data is 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”), and wherein the determination regarding the attention point is made based on a lesion determination result output by the lesion determination model (see para [0115]; “The diagnosis aiding function 356 determines (estimates) a disease or the like of the subject, whose image is being processed, based on the spectral values in each of the sub-bands”).
Claim 5 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 5, 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).
Claim 6 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 6, 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]).
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 Yoda et al. (US 20140269190 A1).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Nishimura et al. in the combination further teach the value being obtained by applying the Fourier transform to the endoscopic 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 logarithmic conversion to a value for each frequency,
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 (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”). 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]).
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 Kyperountas (US 20200305682 A1).
Regarding claim 9, the rejection of claim 1 is incorporated herein. The combination of Nishimura et al. and Tajbakhsh et al. 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
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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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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