CTNF 18/817,148 CTNF 101461 DETAILED ACTION 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. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. Information Disclosure Statement The information disclosure statements (IDS) submitted on 12/06/2024 and 09/19/2025 are being considered by the examiner. Claim Objections 07-29-01 AIA Claim s 1 and 6-12 are objected to because of the following informalities: In claim 1, line 3, the term “wherein the processor” should be changed to “wherein the processor : ” in order to prevent a sentence run on that is grammatically incorrect. In claim 1, line 6, the term “CT image, and” should be changed to “CT image , ; and” in order to prevent a sentence run on that is grammatically incorrect. In claim 6, line 2, the term “wherein the processor” should be changed to “wherein the processor : ” in order to prevent a sentence run on that is grammatically incorrect. In claim 6, line 4, the term “second information, and” should be changed to “second information , ; and” in order to prevent a sentence run on that is grammatically incorrect. In claim 7, line 2, the term “wherein the processor” should be changed to “wherein the processor : ” in order to prevent a sentence run on that is grammatically incorrect. In claim 7, line 8, the term “the non-contrast CT image, and” should be changed to “the non-contrast CT image , ; and” in order to prevent a sentence run on that is grammatically incorrect. In claim 1, line 4, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 1 and in subsequent limitations in the dependent claims. In claim 7, line 4, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 7. In claim 8, line 1, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 8. In claim 9, line 2, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 9. In claim 10, line 2, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 10. In claim 11, line 3, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 11. In claim 12, line 4, the term “a non-contrast CT image” should be changed to “a non-contrast CT Computed Tomography (CT) image” in order to avoid a clarity issues with the abbreviation “non-contrast CT image” in subsequent limitations in claim 12. In claim 11, line 1, the term “a non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute:” should be changed to “a non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute: , wherein when the program is executed by the computer, causes: ” in order to ensure the computer is carrying out the function of the program. In claim 12, line 1, the term “a non-transitory computer-readable storage medium that stores a learning program causing a computer to execute:” should be changed to “a non-transitory computer-readable storage medium that stores a learning program causing a computer to execute: , wherein when the program is executed by the computer, causes: ” in order to ensure the computer is carrying out the function of the program . Appropriate correction is required. Remarks Claims 1, 7-12 incudes the phrase “in a case in which” when reciting a conditional statement. In view of the broadest reasonable interpretation of the claims, MPEP 2111, these limitations may be interpreted in the sense that the limitations occur when the conditional statement occurs, but also introduces the possibility that the conditional statement may not occur. If the condition for performing a conditional statement is not satisfied, the functionality recited by the statement need not be carried out in order for the claimed functionality to be performed. Since the claim fails to recite any specific limitations regarding the possibility that the conditional statement may not occur, the broadest reasonable interpretation of the claim allows for the possibility wherein no functionality is achieved when the conditional statement is not achieved. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim s 1, 7-12, and associated dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 1 and 7-12 recite the limitation "second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image" in lines 7-8, lines 7-8, lines 3-4, lines 5-6, lines 5-6, lines 6-7, and lines 6-7 , respectively. The office finds the term “the other” rendering the claim indefinite. It is not clear what the applicant refers to as “the other”, whether or not it is a new infarction region or large vessel occlusion region, the same infarction region or large vessel occlusion region previously mentioned in the claims, an infarction region or large vessel occlusion region in the same non-contrast CT image, or an infarction region or large vessel occlusion region in a different non-contrast CT image. For purpose of examination the examiner is interpreting the limitation as “second information representing the infarction region or the large vessel occlusion region in the non-contrast CT image”. The office respectfully requests the Applicant to amend claims 1 and 7-12 in order to clarify the claimed invention. Double Patenting 08-33 AIA The non-statutory 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 non-statutory 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 non-statutory 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp. Claims 1-8, 10, and 12 are rejection on the ground of non-statutory double patenting as being unpatentable over claims 1-9 and 11, and 13 of Co-Pending Application No. 18/817,161. Claims 9 and 11 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 10 and 12 of Co-Pending Application No. 18/817,161 in view of AKAHORI (US 20200104996 A1). Although the claims 1-12 of this Application No. 18/817,148 and claims at issue are not identical, they are not patentably distinct from each other because the instant application and the conflicting Patent are claiming common subject matter, as follows This Application No. 18/817,148 Co-Pending Application No. 18/817,161 Claim 1 : An information processing apparatus comprising: at least one processor, wherein the processor acquires a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, and derives second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Claim 1: acquires a non-contrast CT image of a head of a patient Claim 1: the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Claim 7: A learning device comprising: at least one processor, wherein the processor acquires training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image, and trains a neural network through machine learning using the training data to construct a discriminative model that outputs the second information in a case in which the non-contrast CT image and the first information are input. Claim 8: A discriminative model that, in a case in which a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image are input, outputs second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image. Claim 9: An information processing method comprising: acquiring a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image; and deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Claim 10: A learning method comprising: acquiring training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image; and training a neural network through machine learning using the training data to construct a discriminative model that outputs the second information in a case in which the non-contrast CT image and the first information are input. Claim 11: A non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute: a procedure of acquiring a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image; and a procedure of deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Claim 12: A non-transitory computer-readable storage medium that stores a learning program causing a computer to execute: a procedure of acquiring training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image; and a procedure of training a neural network through machine learning using the training data to construct a discriminative model that outputs the second information in a case in which the non-contrast CT image and the first information are input. Claim 1 : An information processing apparatus comprising: at least one processor, wherein the processor acquires at least one of first information representing any one of an infarction region or a large vessel occlusion region in a non-contrast CT image of a head of a patient, information representing an anatomical region of a brain, or clinical information (wherein acquires a non-contrast CT image of a head of a patient is acquired as part of acquiring first information representing any one of an infarction region or a large vessel occlusion region in a non-contrast CT image of a head of a patient) , acquires second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image, and derives third information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information (wherein the first information includes a non-contrast CT image) . Claim 2: further acquires the non-contrast CT image Claim 3: wherein the processor derives the third information by using a discriminative model that has been trained to output the third information in a case in which at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, the non-contrast CT image, and the second information are input (wherein the second information is third information and wherein first information is second information) . Claim 8: A learning device comprising: at least one processor, wherein the processor acquires i) a non-contrast CT image of a head of a patient with cerebral infarction, ii) at least one of first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, information representing an anatomical region of a brain, or clinical information, and iii) training data including input data consisting of second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image (wherein acquires training data including data consisting of a non-contrast CT image of a head of a patient with cerebral infarction is acquires i) a non-contrast CT image of a head of a patient with cerebral infarction), and correct answer data consisting of third information representing the other of the infarction region and the large vessel occlusion region in the non-contrast CT image (wherein second information is third information) , and trains a neural network through machine learning using the training data to construct a discriminative model that outputs the third information in a case in which at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, the non-contrast CT image, and the second information are input (wherein the second information is third information) . Claim 9: A discriminative model that, in a case in which i) a non-contrast CT image of a head of a patient, ii) at least one of first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, information representing an anatomical region of a brain, or clinical information, and iii) second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image are input, outputs third information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image (wherein second information is third information) . Claim 10: An information processing method comprising: acquiring at least one of first information representing any one of an infarction region or a large vessel occlusion region in a non-contrast CT image of a head of a patient, information representing an anatomical region of a brain, or clinical information (wherein a non-contrast CT image of a head of a patient is acquired as part of acquiring a large vessel occlusion region in a non-contrast CT image of a head of a patient) ; acquiring second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image; and deriving third information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information. Claim 11: A learning method comprising: acquiring i) a non-contrast CT image of a head of a patient with cerebral infarction, ii) at least one of first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, information representing an anatomical region of a brain, or clinical information, and iii) training data including input data consisting of second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image, and correct answer data consisting of third information representing the other of the infarction region and the large vessel occlusion region in the non-contrast CT image; and training a neural network through machine learning using the training data to construct a discriminative model that outputs the third information in a case in which at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, the non-contrast CT image, and the second information are input (wherein second information is third information and wherein first information is second information) . Claim 12: A non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute: a procedure of acquiring at least one of first information representing any one of an infarction region or a large vessel occlusion region in a non-contrast CT image of a head of a patient, information representing an anatomical region of a brain, or clinical information (wherein a non-contrast CT image of a head of a patient is acquired as part of acquiring a large vessel occlusion region in a non-contrast CT image of a head of a patient) ; a procedure of acquiring second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image; and a procedure of deriving third information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information (wherein second information is third information and wherein first information is second information) . Claim 13: A non-transitory computer-readable storage medium that stores a learning program causing a computer to execute: a procedure of acquiring i) a non-contrast CT image of a head of a patient with cerebral infarction, ii) at least one of first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image, information representing an anatomical region of a brain, or clinical information, and iii) training data including input data consisting of second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the non-contrast CT image, and correct answer data consisting of third information representing the other of the infarction region and the large vessel occlusion region in the non-contrast CT image (wherein the second information is third information) ; and a procedure of training a neural network through machine learning using the training data to construct a discriminative model that outputs the third information in a case in which at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, the non-contrast CT image, and the second information are input (wherein second information is third information and wherein first information is second information) . Although Co-Pending Application 18/817,161 claim 10 teaches deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and, Co-Pending Application 18/817,161 claim 10 as stated in the table above with respect to claim 9, fails to clearly disclose the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. However, AKAHORI (US 20200104996 A1) explicitly teaches the first information (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) by using a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that has been trained to output the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data consisting of CT images is input into the model, and thus the CT images are the first information and non-contrast CT image).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of the Co-Pending Application 18/817,161 claim 10 of having deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and with the teachings of AKAHORI (US 20200104996 A1) of the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Wherein having the Co-Pending Application 18/817,161 claim 10 having information representing an anatomical region of a brain, the information representing the anatomical region of the brain. The motivation behind the modification would have been to obtain an infarction region identification apparatus that enhances the accuracy of identifying and detecting infarction regions in CT images. Although Co-Pending Application 18/817,161 claim 12 teaches a procedure of deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and, Co-Pending Application 18/817,161 claim 12 as stated in the table above with respect to claim 9, fails to clearly disclose the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. However, AKAHORI (US 20200104996 A1) explicitly teaches the first information (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) by using a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that has been trained to output the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data consisting of CT images is input into the model, and thus the CT images are the first information and non-contrast CT image).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of the Co-Pending Application 18/817,161 claim 12 of having a procedure of deriving second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on the non-contrast CT image and with the teachings of AKAHORI (US 20200104996 A1) of the first information by using a discriminative model that has been trained to output the second information in a case in which the non-contrast CT image and the first information are input. Wherein having the Co-Pending Application 18/817,161 claim 12 having information representing an anatomical region of a brain, the information representing the anatomical region of the brain. The motivation behind the modification would have been to obtain an infarction region identification apparatus that enhances the accuracy of identifying and detecting infarction regions in CT images. The further limitations of the dependent claims are similar as indicated below: This Application No. 18/817,148 Co-Pending Application No. 18/817,161 Claim 2 : wherein the processor derives the second information further based on information on symmetrical regions with respect to a midline of a brain in at least the non-contrast CT image out of the non-contrast CT image and the first information. Claim 3: wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image out of the non-contrast CT image and the first information with respect to the midline of the brain. Claim 4: wherein the processor derives the second information further based on at least one of information representing an anatomical region of a brain or clinical information. Claim 5: wherein the processor acquires the first information by extracting any one of the infarction region or the large vessel occlusion region from the non-contrast CT image. Claim 6: wherein the processor derives quantitative information for at least one of the first information or the second information, and displays the quantitative information. Claim 4 : wherein the processor derives the third information further based on information on symmetrical regions with respect to a midline of the brain in at least the non-contrast CT image out of the first information, the non-contrast CT image, and the second information (wherein second information is third information) . Claim 5: wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image out of the first information, the non-contrast CT image, and the second information, with respect to the midline of the brain (wherein the first information is second information) . Claim 1: derives third information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image based on at least one of the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information (wherein second information is third information) . Claim 6: wherein the processor acquires the first information by extracting any one of the infarction region or the large vessel occlusion region from the non-contrast CT image Claim 7: wherein the processor derives quantitative information for at least one of the first information, the second information, or the third information, and displays the quantitative information. Claims 2-6 contain the same limitations as Co-Pending Application No. 18/817,161 claims 4, 5, 1, and 6-7, respectively. Therefore, given that claims 2-6 depend from independent claim 1 and claims 4, 5, 1, and 6-7 depend from independent claim 1, claims 2-6 are rejected for the same reasons set forth in the rejection of the independent claim above. Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 4-5, and 7-12 are rejected under 35 U.S.C. 102( a)(1)/(a)(2 ) as being anticipated by AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI . Regarding claim 1, AKAHORI explicitly teaches an information processing apparatus comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]) : at least one processor (Fig. 1. Paragraph [0095]-AKAHORI discloses the various processors include a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process.) , wherein the processor (Fig. 1. Paragraph [0095]-AKAHORI discloses the various processors include a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process.) acquires a non-contrast CT image of a head of a patient (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) , and derives second information representing the other of the infarction region (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the second information is result R2).) or the large vessel occlusion region in the non-contrast CT image (Fig. 5. Paragraph [0059]-AKAHORI discloses the first CNN 31 and the second CNN 32 are trained, using a data set of a plurality of CT images of the brain including a thrombus region and the thrombus region specified in the CT images as training data, so as to output a discrimination result R1 of the thrombus region for each pixel included in the input CT image (wherein a thrombus region is a large vessel occlusion region).) based on the non-contrast CT image (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and the first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) by using a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that has been trained to output the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33.) in a case in which the non-contrast CT image and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) . Regarding claim 4, AKAHORI explicitly teaches the information processing apparatus according to claim 1, AKAHORI further explicitly teaches wherein the processor derives the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the second information is result R2).) further based on at least one of information representing an anatomical region of a brain (Fig. 12. Paragraph [0071]-AKAHORI discloses the learning unit 22-2 trains a discriminator 23-2 which discriminates an anatomic part of thrombus in the input CT image B0, using the correct information of an anatomic part of thrombus specified in the CT image Bt1 as training data (wherein the anatomic part is the anatomical region of the brain).) or clinical information (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the CT image is clinical information).) . Regarding claim 5, AKAHORI explicitly teaches the information processing apparatus according to claim 1, AKAHORI further explicitly teaches wherein the processor acquires the first information by extracting any one of the infarction region or the large vessel occlusion region from the non-contrast CT image (Fig. 2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein the thrombus region is the large vessel occlusion region).) . Regarding claim 7, AKAHORI explicitly teaches a learning device comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]) : at least one processor (Fig. 1. Paragraph [0095]-AKAHORI discloses the various processors include a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process.) , wherein the processor (Fig. 1. Paragraph [0095]-AKAHORI discloses the various processors include a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process.) acquires training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) , and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image (Fig. 5 and 8. Paragraph [0079]-AKAHORI discloses the first CNN 31 is trained using not only data indicating a correct thrombus region and data indicating a correct infarction region but also data indicating the correct information of an anatomic part of thrombus and data of information indicating whether or not an infarction region is present (wherein a thrombus region is a large vessel occlusion region).) , and trains a neural network through machine learning using the training data to construct a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0053]-AKAHORI discloses the learning unit 22 trains the discriminator 23 that discriminate the infarction region in the input CT image B0, using the data set of the CT image Bi1 and the infarction region A2 specified in the CT image Bi1 as training data, as illustrated in FIG. 4. Further in paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that outputs the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) . Regarding claim 8, AKAHORI explicitly teaches a discriminative model that (Fig. 2, #23 called discriminator. Paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) , in a case in which a non-contrast CT image of a head of a patient (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image are input (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.), outputs second information representing the other of the infarction region (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the second information is a discrimination result).) or the large vessel occlusion region in the non-contrast CT image (Fig. 5. Paragraph [0059]-AKAHORI discloses the first CNN 31 and the second CNN 32 are trained, using a data set of a plurality of CT images of the brain including a thrombus region and the thrombus region specified in the CT images as training data, so as to output a discrimination result R1 of the thrombus region for each pixel included in the input CT image (wherein a thrombus region is a large vessel occlusion region).) . Regarding claim 9, AKAHORI explicitly teaches an information processing method comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]) : acquiring a non-contrast CT image of a head of a patient (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) ; and deriving second information representing the other of the infarction region (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the third information is result R2).) or the large vessel occlusion region in the non-contrast CT image (Fig. 5. Paragraph [0059]-AKAHORI discloses the first CNN 31 and the second CNN 32 are trained, using a data set of a plurality of CT images of the brain including a thrombus region and the thrombus region specified in the CT images as training data, so as to output a discrimination result R1 of the thrombus region for each pixel included in the input CT image (wherein a thrombus region is a large vessel occlusion region).) based on the non-contrast CT image (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and the first information (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) by using a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that has been trained to output the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data consisting of CT images is input into the model, and thus the CT images are the first information and non-contrast CT image).) . Regarding claim 10, AKAHORI explicitly teaches a learning method comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]) : acquiring training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data consisting of CT images is input into the mode).) . and first information representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 5. Paragraph [0059]-AKAHORI discloses the first CNN 31 and the second CNN 32 are trained, using a data set of a plurality of CT images of the brain including a thrombus region and the thrombus region specified in the CT images as training data, so as to output a discrimination result R1 of the thrombus region for each pixel included in the input CT image (wherein a thrombus region is a large vessel occlusion region).), and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image (Fig. 5 and 8. Paragraph [0079]-AKAHORI discloses the first CNN 31 is trained using not only data indicating a correct thrombus region and data indicating a correct infarction region but also data indicating the correct information of an anatomic part of thrombus and data of information indicating whether or not an infarction region is present (wherein a thrombus region is a large vessel occlusion region).); and training a neural network through machine learning using the training data to construct a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0053]-AKAHORI discloses the learning unit 22 trains the discriminator 23 that discriminate the infarction region in the input CT image B0, using the data set of the CT image Bi1 and the infarction region A2 specified in the CT image Bi1 as training data, as illustrated in FIG. 4. Further in paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that outputs the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training CT images are input into the model).) and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the CT images are first information and wherein training data is input into the model).). Regarding claim 11, AKAHORI explicitly teaches a non-transitory computer-readable storage medium that stores an information processing program causing a computer to execute (Fig. 1, #3 called image storage server. Paragraph [0045]-AKAHORI discloses the image storage server 3 is a computer that stores and manages various types of data and comprises a high-capacity external storage device and database management software. The image storage server 3 performs communication with other apparatuses through the wired or wireless network 4 to transmit and receive, for example, image data. Specifically, the image storage server 3 acquires various types of data including image data of the CT image generated by the three-dimensional imaging apparatus 2 through the network, stores the acquired data in a recording medium, such as a high- capacity external storage device, and manages the data. Further in paragraph [0013]-AKAHORI discloses a non-transitory computer readable medium for storing a program that causes a computer to perform the learning method.) : a procedure of acquiring a non-contrast CT image of a head of a patient (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.); and a procedure of deriving second information representing the other of the infarction region (Fig. 5. Paragraph [0060]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the third information is result R2).) or the large vessel occlusion region in the non-contrast CT image (Fig. 5. Paragraph [0059]-AKAHORI discloses the first CNN 31 and the second CNN 32 are trained, using a data set of a plurality of CT images of the brain including a thrombus region and the thrombus region specified in the CT images as training data, so as to output a discrimination result R1 of the thrombus region for each pixel included in the input CT image (wherein a thrombus region is a large vessel occlusion region).) based on the non-contrast CT image (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training CT images are input into the model).) and the first information (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the CT images are first information and wherein training data is input into the model).) by using a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0053]-AKAHORI discloses the learning unit 22 trains the discriminator 23 that discriminate the infarction region in the input CT image B0, using the data set of the CT image Bi1 and the infarction region A2 specified in the CT image Bi1 as training data, as illustrated in FIG. 4. Further in paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that has been trained to output the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image information (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training data is input into the model).) and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the CT images are first information and wherein training data is input into the model).). Regarding claim 12, AKAHORI explicitly teaches a non-transitory computer-readable storage medium that stores a learning program causing a computer to execute (Fig. 1, #3 called image storage server. Paragraph [0045]-AKAHORI discloses the image storage server 3 is a computer that stores and manages various types of data and comprises a high-capacity external storage device and database management software. The image storage server 3 performs communication with other apparatuses through the wired or wireless network 4 to transmit and receive, for example, image data. Specifically, the image storage server 3 acquires various types of data including image data of the CT image generated by the three-dimensional imaging apparatus 2 through the network, stores the acquired data in a recording medium, such as a high-capacity external storage device, and manages the data. Further in paragraph [0013]-AKAHORI discloses a non-transitory computer readable medium for storing a program that causes a computer to perform the learning method.) : a procedure of acquiring training data including input data consisting of a non-contrast CT image of a head of a patient with cerebral infarction (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) and first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing any one of an infarction region (Fig. 4, illustrates an infarction region #A2. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires a CT image Bt1 of the brain of the subject that has developed cerebral thrombosis and a CT image Bi1 of the brain of the subject that has developed cerebral infarction from the image storage server 3 in order to train the discriminator 23 which will be described below.) or a large vessel occlusion region in the non-contrast CT image (Fig. 3 and 12. Paragraph [0052]-AKAHORI discloses the image acquisition unit 21 acquires the CT image B0, from which disease regions are to be extracted, from the image storage server 3 in order to extract the disease regions such as a thrombus region and an infarction region (wherein a thrombus region is a large vessel occlusion region). Further in paragraph [0071]- AKAHORI discloses in the CT image Bt1 illustrated in FIG. 12, a region represented by an arrow indicates middle cerebral artery trunk occlusion. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.) , and correct answer data consisting of second information representing the other of the infarction region or the large vessel occlusion region in the non-contrast CT image (Fig. 5 and 8. Paragraph [0079]-AKAHORI discloses the first CNN 31 is trained using not only data indicating a correct thrombus region and data indicating a correct infarction region but also data indicating the correct information of an anatomic part of thrombus and data of information indicating whether or not an infarction region is present (wherein a thrombus region is a large vessel occlusion region).) ; and a procedure of training a neural network through machine learning using the training data () to construct a discriminative model (Fig. 2, #23 called discriminator. Paragraph [0053]-AKAHORI discloses the learning unit 22 trains the discriminator 23 that discriminate the infarction region in the input CT image B0, using the data set of the CT image Bi1 and the infarction region A2 specified in the CT image Bi1 as training data, as illustrated in FIG. 4. Further in paragraph [0054]-AKAHORI discloses the discriminator 23 discriminates a disease region in the CT image B0 of the brain. In this embodiment, the thrombus region and the infarction region are used as the disease regions. In this embodiment, it is assumed that the discriminator 23 includes a plurality of convolutional neural networks (hereinafter, referred to as CNNs) which are one of multi-layer neural networks that have a plurality of processing layers hierarchically connected to each other and are subjected to deep learning.) that outputs the second information (Fig. 5. Paragraph [0060]-AKAHORI discloses the discrimination result R2 of the infarction region for each pixel of the CT image B0 is output from the output layer 33b of the third CNN 33 (wherein the infarction region is second information).) in a case in which the non-contrast CT image (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the training CT images are input into the model).) and the first information are input (Fig. 5. Paragraph [0050]-AKAHORI discloses the first CNN 31 and the third CNN 33 are trained, using a data set of a plurality of CT images of the brain including an infarction region and the infarction region specified in the CT images as training data, so as to output a discrimination result R2 of the infarction region for each pixel included in the input CT image (wherein the CT images are first information and wherein training data is input into the model).) . 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. 07-21-aia AIA Claim s 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI, in view of STRAKA et al. (US 20200359981 A1), hereinafter referenced as STRAKA . Regarding claim 2, AKAHORI explicitly teaches the information processing apparatus according to claim 1, AKAHORI fails to explicitly teach wherein the processor derives the second information further based on information on symmetrical regions with respect to a midline of a brain in at least the non-contrast CT image out of the non-contrast CT image and the first information. However, STRAKA explicitly teaches wherein the processor derives the second information further based on information on symmetrical regions with respect to a midline of a brain in at least the non-contrast CT image (Fig. 3, illustrates symmetrical regions with respect to the midline of the brain. Paragraph [0073]-STRAKA discloses the brain 304 can be divided into a number of regions with the regions of the first hemisphere 306 having a counterpart region in the second hemisphere 308. For example, the first hemisphere 306 can include a first region 314 and the second hemisphere 308 can have a first counterpart region 316 that corresponds to and is substantially symmetrically located with respect to the first region 314. In addition, the first hemisphere 306 can include a second region 318 and the second hemisphere 308 can include a second counterpart region 320 that corresponds to and is substantially symmetrically located with respect to the second region 318. Further, the first hemisphere 306 can include a third region 322 and the second hemisphere 308 can include a third counterpart region 324 that corresponds to and is substantially symmetrically locate with respect to the third region 322. Further in paragraph [0088]-STRAKA discloses at operation 502, the process 500 can include obtaining image data corresponding to one or more images indicating blood vessels located in a brain of an individual. The image data can be generated by an imaging device, such as a computed tomography (CT) imaging device.) out of the non-contrast CT image (Fig. 5. Paragraph [0088]-STRAKA discloses at operation 502, the process 500 can include obtaining image data corresponding to one or more images indicating blood vessels located in a brain of an individual. The image data can be generated by an imaging device, such as a computed tomography (CT) imaging device.) and the first information (Fig. 4. Paragraph [0085]-STRAKA discloses the possible LVO can be indicated on an image of the brain of the patient (wherein the LVO is first information).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of AKAHORI of an information processing apparatus comprising: at least one processor, wherein the processor acquires a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image with the teachings of STRAKA of wherein the processor derives the second information further based on information on symmetrical regions with respect to a midline of a brain in at least the non-contrast CT image out of the non-contrast CT image and the first information. Wherein having AKAHORI’s infarction region identification apparatus wherein the processor derives the second information further based on information on symmetrical regions with respect to a midline of a brain in at least the non-contrast CT image out of the non-contrast CT image and the first information. The motivation behind the modification would have been to obtain an infarction region identification apparatus that enhances the accuracy of identifying and detecting infarction regions in CT images. Since both AKAHORI and STRAKA relate to analyzing brain CT scans to identify information in blood vessels, wherein AKAHORI is to provide a technique that discriminates a disease region with high accuracy, using a limited amount of data, even in an image in which it is difficult to prepare a large amount of data indicating a correct disease region, while STRAKA the treatment of individuals can be prioritized based on conditions indicated by the images. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and STRAKA et al. (US 20200359981 A1), Paragraph [0029]. Regarding claim 6, AKAHORI explicitly teaches the information processing apparatus according to claim 1, AKAHORI further explicitly teaches wherein the processor (Fig. 1. Paragraph [0095]-AKAHORI discloses the various processors include a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process.) AKAHORI fails to explicitly teach derives quantitative information for at least one of the first information or the second information, and displays the quantitative information. However, STRAKA explicitly teaches derives quantitative information for at least one of the first information or the second information (Fig. 6. Paragraph [0109]-STRAKA discloses the probabilities and/or severities related to abnormalities of blood vessels located in the brain of an individual can be displayed using different colors in the one or more images. For example, a first range of probabilities of an abnormality and/or levels of severity of an abnormality with respect to blood vessels included in a brain of an individual can be shown as a first color in the one or more images and a second range of probabilities of an abnormality and/or levels of severity of an abnormality with respect to blood vessels included in a brain of an individual can be shown as a second color in the one or more images (wherein the quantitative information is a range of probabilities of an abnormality and/or levels of severity of an abnormality). Further in paragraph [0109]-STRAKA discloses the one or more images can highlight locations within the brain of the individual where an occlusion may be located by displaying an arrow, circle, box, or other arbitrarily shaped outline (wherein the first information and second information are the highlighted locations where an occlusion may be located).) , and displays the quantitative information (Fig. 6. Paragraph [0109]-STRAKA discloses the probabilities and/or severities related to abnormalities of blood vessels located in the brain of an individual can be displayed using different colors in the one or more images. For example, a first range of probabilities of an abnormality and/or levels of severity of an abnormality with respect to blood vessels included in a brain of an individual can be shown as a first color in the one or more images and a second range of probabilities of an abnormality and/or levels of severity of an abnormality with respect to blood vessels included in a brain of an individual can be shown as a second color in the one or more images (wherein displays the quantitative information is displaying the probabilities/severities with different colors).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of AKAHORI of an information processing apparatus comprising: at least one processor, wherein the processor acquires a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image with the teachings of STRAKA of derives quantitative information for at least one of the first information or the second information, and displays the quantitative information. Wherein having AKAHORI’s infarction region identification apparatus derives quantitative information for at least one of the first information or the second information, and displays the quantitative information. The motivation behind the modification would have been to obtain an infarction region identification apparatus that enhances the accuracy of identifying and detecting infarction regions in CT images. Since both AKAHORI and STRAKA relate to analyzing brain CT scans to identify information in blood vessels, wherein AKAHORI is to provide a technique that discriminates a disease region with high accuracy, using a limited amount of data, even in an image in which it is difficult to prepare a large amount of data indicating a correct disease region, while STRAKA the treatment of individuals can be prioritized based on conditions indicated by the images. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and STRAKA et al. (US 20200359981 A1), Paragraph [0029] . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI, in view of STRAKA et al. (US 20200359981 A1), hereinafter referenced as STRAKA, and further in view of (US 20210098115 A1), hereinafter referenced as SHIN . Regarding claim 3, AKAHORI in view of STRAKA explicitly teach the information processing apparatus according to claim 2, AKAHORI in view of STRAKA fail to explicitly teach wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image out of the non-contrast CT image and the first information with respect to the midline of the brain. However, SHIN explicitly teaches wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image (Fig. 1. Paragraph [0053]-SHIN discloses in the pre-processing step, the pre-processing unit 20 may perform a step of detecting a position of a skull in the brain CT image based on the image processing, an alignment step of aligning the image with respect to a rotation degree and a center point, and a horizontal inverting step in accordance with a lesion-side (wherein inversion information/information on the symmetrical regions is horizontal inversion in accordance with a lesion-side and wherein the brain CT is the non-contrast CT image).) out of the non-contrast CT image (Fig. 1. Paragraph [0053]-SHIN discloses in the pre-processing step, the pre-processing unit 20 may perform a step of detecting a position of a skull in the brain CT image based on the image processing, an alignment step of aligning the image with respect to a rotation degree and a center point, and a horizontal inverting step in accordance with a lesion-side (wherein the brain CT is the non-contrast CT image).) and the first information with respect to the midline of the brain (Fig. 1. Paragraph [0054]-SHIN discloses the pre-processed image is standardized as an image centered such that a left brain is a lesion and a center vertical line of the image is a symmetry line of the brain (wherein first information is the image).) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of AKAHORI in view of STRAKA of an information processing apparatus comprising: at least one processor, wherein the processor acquires a non-contrast CT image of a head of a patient and first information representing any one of an infarction region or a large vessel occlusion region in the non-contrast CT image with the teachings of SHIN of wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image out of the non-contrast CT image and the first information with respect to the midline of the brain. Wherein having AKAHORI’s infarction region identification apparatus wherein the information on the symmetrical regions is inversion information obtained by inverting at least the non-contrast CT image out of the non-contrast CT image and the first information with respect to the midline of the brain. The motivation behind the modification would have been to obtain an infarction region identification apparatus that enhances the accuracy of identifying and detecting infarction regions in CT images. Since both AKAHORI and SHIN relate to analyzing brain CT scans of stroke patients, wherein AKAHORI is to provide a technique that discriminates a disease region with high accuracy, using a limited amount of data, even in an image in which it is difficult to prepare a large amount of data indicating a correct disease region, while SHIN the test method based on the CT has an advantage of being able to test quickly, so that it is considered as a test method suitable for a characteristic of the stroke disease for which prompt response is essential. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and SHIN et al. (US 20210098115 A1), Paragraph [0007]. Conclusion Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure. AKAHORI et al. (US 20200104995 A1) – Provided are a disease region extraction apparatus, a disease region extraction method, and a disease region extraction program that can extract an infarction region even in an image in which it is difficult to prepare a large amount of data indicating a correct infarction region. A disease region extraction apparatus includes: an image acquisition unit that acquires a first image obtained by capturing an image of a subject that has developed a disease; an estimated image derivation unit that estimates a second image, whose type is different from the type of the first image, from the first image to derive an estimated image; and a disease region extraction unit that extracts a disease region from the estimated image…Abstract, Fig. 2. KIM et al. (US 10898152 B1) - Provided is a stroke diagnosis apparatus based on AI that includes: an image obtainer obtaining a non-contrast CT image related to the brain of at least one patient; a preprocessor pre-processing the non-contrast CT image and determining whether the at least one patient is in a non-hemorrhage state or a hemorrhage state on the basis of the pre-processed image; an image processor normalizing the pre-processed image and dividing and extracting an ROI (Region of Interest) using a preset standard mask template; and a determiner determining whether there is a problem with a cerebral large vessel of the at least one patient using the divided and extracted ROI… Abstract, Fig. 5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN N WOLFSON whose telephone number is (571)272-1898. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ETHAN N WOLFSON/Examiner, Art Unit 2673 /CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673 Application/Control Number: 18/817,148 Page 2 Art Unit: 2673 Application/Control Number: 18/817,148 Page 3 Art Unit: 2673 Application/Control Number: 18/817,148 Page 4 Art Unit: 2673 Application/Control Number: 18/817,148 Page 5 Art Unit: 2673 Application/Control Number: 18/817,148 Page 6 Art Unit: 2673 Application/Control Number: 18/817,148 Page 7 Art Unit: 2673 Application/Control Number: 18/817,148 Page 8 Art Unit: 2673 Application/Control Number: 18/817,148 Page 9 Art Unit: 2673 Application/Control Number: 18/817,148 Page 10 Art Unit: 2673 Application/Control Number: 18/817,148 Page 11 Art Unit: 2673 Application/Control Number: 18/817,148 Page 12 Art Unit: 2673 Application/Control Number: 18/817,148 Page 13 Art Unit: 2673 Application/Control Number: 18/817,148 Page 14 Art Unit: 2673 Application/Control Number: 18/817,148 Page 15 Art Unit: 2673 Application/Control Number: 18/817,148 Page 16 Art Unit: 2673 Application/Control Number: 18/817,148 Page 17 Art Unit: 2673 Application/Control Number: 18/817,148 Page 18 Art Unit: 2673 Application/Control Number: 18/817,148 Page 19 Art Unit: 2673 Application/Control Number: 18/817,148 Page 20 Art Unit: 2673 Application/Control Number: 18/817,148 Page 21 Art Unit: 2673 Application/Control Number: 18/817,148 Page 22 Art Unit: 2673 Application/Control Number: 18/817,148 Page 23 Art Unit: 2673 Application/Control Number: 18/817,148 Page 24 Art Unit: 2673 Application/Control Number: 18/817,148 Page 25 Art Unit: 2673 Application/Control Number: 18/817,148 Page 26 Art Unit: 2673 Application/Control Number: 18/817,148 Page 27 Art Unit: 2673 Application/Control Number: 18/817,148 Page 28 Art Unit: 2673 Application/Control Number: 18/817,148 Page 29 Art Unit: 2673 Application/Control Number: 18/817,148 Page 30 Art Unit: 2673 Application/Control Number: 18/817,148 Page 31 Art Unit: 2673 Application/Control Number: 18/817,148 Page 32 Art Unit: 2673 Application/Control Number: 18/817,148 Page 33 Art Unit: 2673 Application/Control Number: 18/817,148 Page 34 Art Unit: 2673 Application/Control Number: 18/817,148 Page 35 Art Unit: 2673 Application/Control Number: 18/817,148 Page 36 Art Unit: 2673 Application/Control Number: 18/817,148 Page 37 Art Unit: 2673 Application/Control Number: 18/817,148 Page 38 Art Unit: 2673 Application/Control Number: 18/817,148 Page 39 Art Unit: 2673 Application/Control Number: 18/817,148 Page 40 Art Unit: 2673 Application/Control Number: 18/817,148 Page 41 Art Unit: 2673 Application/Control Number: 18/817,148 Page 42 Art Unit: 2673 Application/Control Number: 18/817,148 Page 43 Art Unit: 2673 Application/Control Number: 18/817,148 Page 44 Art Unit: 2673 Application/Control Number: 18/817,148 Page 45 Art Unit: 2673 Application/Control Number: 18/817,148 Page 46 Art Unit: 2673 Application/Control Number: 18/817,148 Page 47 Art Unit: 2673 Application/Control Number: 18/817,148 Page 48 Art Unit: 2673 Application/Control Number: 18/817,148 Page 49 Art Unit: 2673 Application/Control Number: 18/817,148 Page 50 Art Unit: 2673
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