DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
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.
Terminal Disclaimer
The terminal disclaimer filed on 08/12/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of Patent of co-pending application #18817148 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
Applicant’s remarks, filed 08/12/2026, regarding the objections made to the claims and the 112(b) rejections submitted in the non-final office action dated 05/21/2026 are withdrawn due to the amendments made to the claims. It is also noted that additional claim objections have been made to the claims and some claim objections have not been obviated, as noted in the claim objections section of this office action.
Response to Arguments
Applicant’s arguments see remarks, filed 08/12/2026, with respect to the claims 1-13 have been fully considered but are moot because the arguments do not apply to the current combinations of references being used in the current rejection.
Claim Objections
Claims 1, 6, and 12-13 are objected to because of the following informalities:
In claim 1, line 4, the term “acquires at least one” should be changed to “acquirein order to avoid a grammatical issue.
In claim 1, line 9, the term “acquires second information” should be changed to “acquirein order to avoid a grammatical issue.
In claim 1, line 12, the term “derives third information” should be changed to “derivein order to avoid a grammatical issue.
In claim 6, line 5, the term “candidate of the other of the infarction region” should be changed to “candidate of the in order to maintain consistency throughout to the claims and avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph issues.
In claim 12, line 1-3, the term “a non-transitory computer-readable storage medium that stores a learning program executable by a computer to perform a process comprising:” should be changed to “a non-transitory computer-readable storage medium that stores a learning program executable by a computer configured to perform a process comprising:” in order to ensure the computer is carrying out the function of the program and avoid a grammatical issue.
In claim 12, line 13, the term “a procedure of deriving third information representing second region” should be changed to “a procedure of deriving third information representing the second region” in order to avoid a typographical issue.
In claim 13, line 1-3, the term “a non-transitory computer-readable storage medium that stores a learning program executable by a computer to perform a process comprising:” should be changed to “a non-transitory computer-readable storage medium that stores a learning program executable by a computer configured to perform a process comprising:” in order to ensure the computer is carrying out the function of the program and avoid a grammatical issue.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, 10-13 along with their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for the information derivation unit 22 acquires at least one of first information representing any one of an infarction region or a large vessel occlusion region in a CT image G0, information representing an anatomical region of a brain, or clinical information, acquires second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the CT image G0, and derives third information representing the other of the infarction region or the large vessel occlusion region in the CT image G0 based on at least one of the CT image G0, the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information, as described in Paragraph [0031] in the specification, does not reasonably provide enablement for explicitly disclosing the claim language “acquires at least one selected from the group consisting of: i) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in a non-contrast Computed Tomography (CT) image of a head of a patient; ii) information representing an anatomical region of a brain; and iii) clinical information;”, as claimed in claim 1, “acquire i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, ii) at least one selected from the group consisting of: (a) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in the non- contrast CT image;” as claimed in claim 8, “acquiring at least one selected from the group consisting of: i) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in a non-contrast Computed Tomography(CT) image of a head of a patient; ii) information representing an anatomical region of a brain; and iii) clinical information;” as claimed in claim 10, “acquiring i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, ii) at least one selected from the group consisting of: (a) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in the non-contrast CT image; (b) information representing an anatomical region of a brain; and (c) clinical information, and” as claimed in claim 11, “a procedure of acquiring at least one selected from the group consisting of: i) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in a non-contrast Computed Tomography (CT) image of a head of a patient; ii) information representing an anatomical region of a brain; and iii) clinical information;” as claimed in claim 12, and “a procedure of acquiring i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, ii) at least one selected from the group consisting of: (a) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in the non- contrast CT image; (b) information representing an anatomical region of a brain; and (c) clinical information, and” as claimed in claim 13.
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, silent/ not taught by the specification is at least one selected from the group consisting of:.
Claims 1, 10, and 12, along with their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for the information derivation unit 22 acquires at least one of first information representing any one of an infarction region or a large vessel occlusion region in a CT image G0, information representing an anatomical region of a brain, or clinical information, acquires second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the CT image G0, and derives third information representing the other of the infarction region or the large vessel occlusion region in the CT image G0 based on at least one of the CT image G0, the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information, as described in Paragraph [0031] in the specification, does not reasonably provide enablement for explicitly disclosing the claim language “derives third information representing the second region based on the second information, and at least one selected from the group consisting of: i) the first information; ii) the information representing the anatomical region of the brain, or; and iii) the clinical information.”, as claimed in claim 1, “deriving third information representing the second region based on the second information, and at least one selected from the group consisting of: i) the first information; ii) the information representing the anatomical region of the brain; and iii) the clinical information.” as claimed in claim 10, and “a procedure of deriving third information representing second region based on the second information, and at least one selected from the group consisting of: i) the first information; ii) the information representing the anatomical region of the brain; and iii) the clinical information” as claimed in claim 12.
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, silent/ not taught by the specification is at least one selected from the group consisting of:.
Claims 3, 8-9, 11 and 13 along with their dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for the information derivation unit 22 acquires at least one of first information representing any one of an infarction region or a large vessel occlusion region in a CT image G0, information representing an anatomical region of a brain, or clinical information, acquires second information representing a candidate of the other of the infarction region or the large vessel occlusion region in the CT image G0, and derives third information representing the other of the infarction region or the large vessel occlusion region in the CT image G0 based on at least one of the CT image G0, the first information, the information representing the anatomical region of the brain, or the clinical information, and the second information. The input data 101 consists of the non-contrast CT image 103, a mask image 104 representing an infarction region in the non-contrast CT image 103, a mask image 105 representing a large vessel occlusion region candidate in the non-contrast CT image 103, and at least one information (referred to as additional information) 106 of the information representing the anatomical region or the clinical information, as described in Paragraph [0031 and 0080] in the specification, does not reasonably provide enablement for explicitly disclosing the claim language “in response to receiving, as inputs, the non-contrast CT image, the second information, and at least one selected from the group consisting of: i) the first information; ii) the information representing the anatomical region of the brain; and iii) the clinical information”, as claimed in claim 3, “in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information, (b) the information representing the anatomical region of the brain, and (c) the clinical information, (ii) the non-contrast CT image, and (iii) the second information.” as claimed in claim 8, “in response to receiving, as inputs, i) a non-contrast Computed Tomography (CT) image of a head of a patient, ii) at least one selected from the group consisting of: (a) first information representing a first region, wherein the first region is any one of an infarction region or a large vessel occlusion region in the non-contrast CT image; (b) information representing an anatomical region of a brain; and (c) clinical information, and ” as claimed in claim 9, “in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information; (b) the information representing the anatomical region of the brain; and (c) the clinical information,” as claimed in claim 11, and “in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information, (b) the information representing the anatomical region of the brain, and (c) the clinical information, (ii) the non-contrast CT image, and the second information.” as claimed in claim 13.
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, silent/ not taught by the specification is at least one selected from the group consisting of:.
Claims 7 along with its dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for the quantitative value derivation unit 24 derives a quantitative value for at least one of the infarction region or the large vessel occlusion region derived by the information derivation unit 22. The quantitative value is an example of quantitative information in the present disclosure. In the present embodiment, it is assumed that the quantitative value derivation unit 24 derives the quantitative values of both the infarction region and the large vessel occlusion region, but the quantitative value of any one of the infarction region or the large vessel occlusion region may be derived, as described in Paragraph [0052] in the specification, does not reasonably provide enablement for explicitly disclosing the claim language “derive quantitative information for at least one selected from the group consisting of the first information, the second information, and the third information” as claimed in claim 7.
The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims. The claimed subject matter, silent/ not taught by the specification is at least one selected from the group consisting of:.
The office respectfully requests the Applicant to indicate where in the specification teaches the limitation in claims 1, 3, and 7-13 or amend in order to overcome the rejection under 35 U.S.C. 112(a.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, and 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI, in view of KIM et al. (US 10898152 B1), hereinafter referenced as KIM.
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 at least one processor is configured to (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 at least one selected from the group consisting of (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]):
i) first information representing a first 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.),
wherein the first region is 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 a non-contrast Computed Tomography (CT) image of a head of a patient (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.);
ii) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and
iii) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
at least one selected from the group consisting of: i) the first information (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.); ii) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.), and iii) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
acquires second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and derives third information representing the second region based on the second information, and.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.); and
derives third information representing the second region based on the second information (Fig. 14. Col. 20, Line [56-60]-KIM discloses the mechanical thrombectomy determiner 45 may perform a step (S113) of detecting the volume of an affected part (Early ischemic sign, Frank hypodensity) according to ELVO (Emergent Large Vessel Occlusion) through AI (wherein the volume is third information and the ELVO is the second information).), and
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 at least one processor is configured to: acquires at least one selected from the group consisting of: i)first information representing a first region with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and derives third information representing the second region based on the second information, and.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and derives third information representing the second region based on the second information, and.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 2, AKAHORI in view of KIM explicitly teach the information processing apparatus according to claim 1,
AKAHORI further explicitly teaches wherein the at least one processor is further configured to (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.):
acquire 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
derive the third information further based on the non-contrast CT image (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).).
Regarding claim 3, AKAHORI in view of KIM explicitly teach the information processing apparatus according to claim 2,
AKAHORI further explicitly teaches wherein the at least one processor is configured to derive the third information 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 third 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 third information).) in response to receiving, as inputs, 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).), the second information (Figs. 4 and 6-7. 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 (wherein the infarction region is a candidate of the other of the infarction region and is thus second information.), and at least one selected from the group consisting of: i) 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).); ii) the information representing the anatomical region of the 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).); and iii) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).).
Regarding claim 6, AKAHORI in view of KIM explicitly teach the information processing apparatus according to claim 1,
AKAHORI further explicitly teaches wherein the processor is configured to acquire 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).), and
acquire the second information by extracting the candidate of the other 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 and wherein the disease regions are candidate regions).).
Regarding claim 8, 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 at least one processor is configured to (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.):
acquire i) a non-contrast Computed Tomography (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. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.), ii) at least one selected from the group consisting of: (a) first information representing a first region (Figs. 4 and 12, illustrate first information (i.e. a CT image). 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.), wherein the first region is 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.);
(b) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and
(c) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).), and
iii) training data including input data (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.) consisting of second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
and correct answer data consisting of third information representing the second region (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 second region).); and
train 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 third 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 third information).) in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information (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.), (b) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.), and (c) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).),
(ii) 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
(iii) the second information (Figs. 4 and 6-7. 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 (wherein the infarction region is a candidate of the other of the infarction region and is thus second information.).
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.),
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 a learning device comprising: at least one processor, wherein the at least one processor is configured to: acquire i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 9, AKAHORI explicitly teaches a discriminative model that, in response to receiving, as inputs, i) a non-contrast Computed Tomography (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.),
ii) at least one selected from the group consisting of: (a) first information representing a first 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.),
wherein the first region is 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.);
(b) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and
(c) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).), and
iii) second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
outputs third information representing the second region (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 third information).).
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image are input.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image are input (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.),
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 a discriminative model that, in response to receiving, as inputs, i) a non-contrast Computed Tomography (CT) image of a head of a patient with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image are input.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image are input.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 10, AKAHORI explicitly teaches an information processing method comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]):
acquiring at least one selected from the group consisting of: i) first information representing a first region (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]),
wherein the first region is 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 a non-contrast Computed Tomography(CT) image of a head of a patient (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.);
ii) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and
iii) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
acquiring second information representing a candidate of a second region (Figs. 4 and 6-7. 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 (wherein the infarction region is a candidate of the other of the infarction region and is thus second information.),
at least one selected from the group consisting of: i) the first information (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.); ii) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.), and iii) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and deriving third information representing the second region based on the second information, and.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS) (wherein the classification of a region is the first information).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.); and
deriving third information representing the second region based on the second information (Fig. 14. Col. 20, Line [56-60]-KIM discloses the mechanical thrombectomy determiner 45 may perform a step (S113) of detecting the volume of an affected part (Early ischemic sign, Frank hypodensity) according to ELVO (Emergent Large Vessel Occlusion) through AI (wherein the volume is third information and the ELVO is the second information).), and
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 method comprising: acquiring at least one selected from the group consisting of: i) first information representing a first region, with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and deriving third information representing the second region based on the second information, and.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and deriving third information representing the second region based on the second information, and.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 11, AKAHORI explicitly teaches a learning method comprising (Fig. 2, #1 called disease region discrimination apparatus. Paragraph [0047]):
acquiring i) a non-contrast Computed Tomography (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. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.), ii) at least one selected from the group consisting of: (a) first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing a first 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.),
wherein the first region is 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.);
(b) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.);
and (c) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).), and
iii) training data including input data (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.) consisting of second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
correct answer data consisting of third information representing the second region (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 second 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 third 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 third information).) in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information (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.); (b) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and (c) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).),
ii) 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 iii) the second information (Figs. 4 and 6-7. 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 (wherein the infarction region is a candidate of the other of the infarction region and is thus second information.).
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.),
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 a learning method comprising: acquiring i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 12, AKAHORI explicitly teaches a non-transitory computer-readable storage medium that stores an information processing program that is executable by a computer to perform a process comprising (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 at least one selected from the group consisting of: i) first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing a first 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.),
wherein the first region is 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 a non-contrast Computed Tomography (CT) image of a head of a patient (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.);
ii) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and
iii) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
a procedure of acquiring second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
at least one selected from the group consisting of: i) the first information (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.); ii) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.), and iii) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).);
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and a procedure of deriving third information representing second region based on the second information, and.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.); and
a procedure of deriving third information representing second region based on the second information (Fig. 14. Col. 20, Line [56-60]-KIM discloses the mechanical thrombectomy determiner 45 may perform a step (S113) of detecting the volume of an affected part (Early ischemic sign, Frank hypodensity) according to ELVO (Emergent Large Vessel Occlusion) through AI (wherein the volume is third information and the ELVO is the second information).), and
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 a non-transitory computer-readable storage medium that stores an information processing program that is executable by a computer to perform a process comprising: a procedure of acquiring at least one selected from the group consisting of: i) first information representing a first region, with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and a procedure of deriving third information representing second region based on the second information, and.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image; and a procedure of deriving third information representing second region based on the second information, and.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Regarding claim 13, AKAHORI explicitly teaches a non-transitory computer-readable storage medium that stores a learning program that is executable by a computer to perform a process comprising (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 i) a non-contrast Computed Tomography (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. Further in paragraph [0094]-AKAHORI discloses the non-contrast-enhanced CT images are used as the CT images B0, Bt1, and Bi1.),
ii) at least one selected from the group consisting of: (a) first information (Figs. 4 and 12, illustrate first information (i.e. a CT image). Paragraph [0052]) representing a first 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.),
wherein the first region is 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.);
(b) information representing an anatomical region of a brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.); and (c) clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).), and
iii) training data including input data (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.) consisting of second information representing a candidate of a second region (Figs. 6-7. Paragraph [0064]-AKAHORI discloses the display control unit 24 displays the brain image in which the thrombus region and the infarction region have been discriminated on the display unit 14. FIGS. 6 and 7 are diagrams illustrating the displayed CT image of the brain. In addition, FIGS. 6 and 7 illustrate a slice image in one tomographic plane of the CT image B0. The thrombus region A1 discriminated in the CT image B0 is displayed so as to be surrounded by a line as illustrated in FIG. 6 (wherein the thrombus region is a second region).),
correct answer data consisting of third information representing the second region (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 second 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 third 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 third information).) in response to receiving, as inputs, (i) at least one selected from the group consisting of: (a) the first information (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.), (b) the information representing the anatomical region of the brain (Figs. 11 and 15. Paragraph [0070]-AKAHORI discloses FIG. 11 is a diagram illustrating an anatomic part of the brain blood vessel. For example, as illustrated in FIG. 11, the anatomic part includes the internal carotid artery (ICA), the anterior cerebral artery (ACA), the middle cerebral artery (MCA), and the posterior cerebral artery (PCA). In addition, a known anatomic part can be used as the anatomic part of the brain blood vessel.), and (c) the clinical information (Fig. 4. Paragraph [0044]-AKAHORI discloses a diagnosis target part of a patient that is the subject is the brain and the three-dimensional imaging apparatus 2 is a CT apparatus (wherein a diagnosis target and image captured by the CT apparatus are clinical information, as a medical professional would use the information of a diagnosis target or a CT image in a clinical environment).),
(ii) 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
(iii) the second information (Figs. 4 and 6-7. 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 (wherein the infarction region is a candidate of the other of the infarction region and is thus second information.).
AKAHORI fails to explicitly teach wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
However, KIM explicitly teaches wherein the second region is the other of the infarction region (Fig. 4. Col. 12, Line [13-17]-KIM discloses the ischemia classifier 41 according to the present disclosure may independently perform determining whether there is an affected part in each divided region and classifying the shape in relation to Old infarct (0I)/Recent infarct (RI)/Frank hypodensity (FH)/Early ischemic sign (EIS).) or the large vessel occlusion region in the same non-contrast CT image (Fig. 4. Col. 14, Line [42-49]-KIM discloses when it is determined that a non-hemorrhage patient has ischemia (S70), the large vessel occlusion determiner 43 determines whether the patient has large vessel occlusion (S90). Whether the large vessel occlusion has been generated in the step (S90) may be determined on the basis of whether a dense MCA sign has been detected at an infra-ganglionic level.), and.
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 a non-transitory computer-readable storage medium that stores an information processing program that is executable by a computer to perform a process comprising: a procedure of acquiring i) a non-contrast Computed Tomography (CT) image of a head of a patient with cerebral infarction, with the teachings of KIM of wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
Wherein having AKAHORI’s infarction region identification apparatus having wherein the second region is the other of the infarction region or the large vessel occlusion region in the same non-contrast CT image, and.
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 KIM relate to analyzing brain CT scans to identify infarction and large vessel occlusion, 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 KIM may reduce scoring variability when estimating an ASPECT score on the basis of an image processing and deep learning technology. Please see AKAHORI (US 20200104996 A1), Paragraph [0006], and KIM et al. (US 10898152 B1), Col. 3, Line [34-37].
Claims 4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI, in view of KIM et al. (US 10898152 B1), hereinafter referenced as KIM, and further in view of STRAKA et al. (US 20200359981 A1), hereinafter referenced as STRAKA.
Regarding claim 4, AKAHORI in view of KIM explicitly teach the information processing apparatus according to claim 2,
AKAHORI in view of KIM fail to explicitly teach wherein the processor is configured to derive 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.
However, STRAKA explicitly teaches wherein the processor is configured to derive 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 (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 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).), 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 second information (Fig. 5. Paragraph [0090]-STRAKA discloses the process 500 can include determining first measures of density for a first group of the blood vessels located in a first region of a first hemisphere of the brain (wherein the first region is a candidate large vessel occlusion region).).
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 KIM of an information processing apparatus comprising: at least one processor, wherein the at least one processor is configured to: acquires at least one selected from the group consisting of: i)first information representing a first region with the teachings of STRAKA of wherein the processor is configured to derive 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 having AKAHORI’s infarction region identification apparatus wherein the processor is configured to derive 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.
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 7, AKAHORI in view of KIM explicitly teach the information processing apparatus according to claim 1,
AKAHORI further explicitly teaches wherein the at least one processor is configured to (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 in view of KIM fail to explicitly teach derive quantitative information for at least one selected from the group consisting of the first information, the second information, and the third information; and cause display of the quantitative information.
However, STRAKA explicitly teaches derive quantitative information for at least one selected from the group consisting of the first information, the second information, and the third 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, second information, and third information are the highlighted locations where an occlusion may be located).); and
cause display of 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 in view of KIM of an information processing apparatus comprising: at least one processor, wherein the at least one processor is configured to: acquires at least one selected from the group consisting of: i)first information representing a first region with the teachings of STRAKA of derive quantitative information for at least one selected from the group consisting of the first information, the second information, and the third information; and cause display of the quantitative information.
Wherein having AKAHORI’s infarction region identification apparatus having derive quantitative information for at least one selected from the group consisting of the first information, the second information, and the third information; and cause display of 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].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over AKAHORI (US 20200104996 A1), hereinafter referenced as AKAHORI, in view of KIM et al. (US 10898152 B1), hereinafter referenced as KIM, and further in view of STRAKA et al. (US 20200359981 A1), hereinafter referenced as STRAKA, and further in view of SHIN et al. (US 20210098115 A1), hereinafter referenced as SHIN.
Regarding claim 5, AKAHORI in view of KIM and further in view of STRAKA explicitly teach the information processing apparatus according to claim 4,
AKAHORI in view of KIM and further 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 first information, the non-contrast CT image, and the second 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 first information (Fig. 7. Paragraph [0078]-SHIN discloses the image processing unit 30 of the ASPECT score estimating system 10 according to the present invention searches the feature point based on a predetermined segmentation criterion and then geometrically connects the feature points to segment the lesion (wherein the lesion is a large vessel occlusion region).), 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 second information (Fig. 7. Paragraph [0078]-SHIN discloses the image processing unit 30 of the ASPECT score estimating system 10 according to the present invention searches the feature point based on a predetermined segmentation criterion and then geometrically connects the feature points to segment the lesion (wherein the lesion is a candidate of a large vessel occlusion region).), 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.).
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 KIM and further in view of STRAKA of an information processing apparatus comprising: at least one processor, wherein the at least one processor is configured to: acquires at least one selected from the group consisting of: i)first information representing a first region 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 first information, the non-contrast CT image, and the second information, with respect to the midline of the brain.
Wherein having AKAHORI’s infarction region identification apparatus 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.
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.
TAUBMANN et al. (US 20220395244 A1) – At least one example embodiment relates to a computer-implemented method for providing stroke information, the method comprising receiving computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising a plurality of brain regions, at least one brain region of the plurality of brain regions being affected by a stroke, receiving brain atlas data, generating registered imaging data based on the computed tomography imaging data and the brain atlas data, the registered imaging data being registered to the brain atlas data, generating the stroke information regarding the stroke based on a set of algorithms and the registered imaging data, and providing the stroke information…Abstract, Fig. 4.
YANG (US 20190298291 A1) – A brain imaging system includes a first imaging device, a second imaging device, a third imaging device and a central processor. The first imaging device captures a first brain image, and calculates a cerebral blood flow, a cerebral blood volume, a cerebral blood mean transit time and a first contrast agent time to peak. The second imaging device captures a second brain image, and calculates the cerebral blood flow, the cerebral blood volume, the cerebral blood mean transit time and a second contrast agent time to peak. The central processor generates an image of a vessel occlusion, infarction or ischemia region respectively according to the vessel occlusion, infarction or ischemia regions in the first brain image and in the second brain image. The third imaging device obtains a brain atrophy region according to the cerebral cortex volume calculated by the third imaging device…Abstract, Fig. 2.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
