CTNF 18/895,431 CTNF 99673 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below. Information Disclosure Statement The information disclosure statement (IDS) submitted on 12/25/2024 and 08/28/2025 are considered and attached. Priority This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of: JP2022-054510 , filed in Japan on 03/29/2022 . Receipt is acknowledged that application is a PCT/JP2023/002931 . Copies of certified papers required by 37 CFR 1.55 have been retrieved. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The limitation “ gradually change the size” in claim 12 is relative and/or subjective terms which render the claims indefinite. The terms not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For example, it is not clear what degree or rate of change constitutes “gradually” because no standard is given for determining such characteristic (e.g. number of size changes per time unit, percentage of a size change, etc.) The specification and claims do not provide any standards or guidelines for determining these characteristics, and therefore the examiner is left to make a subjective judgement. A claim that requires the exercise of subjective judgment without restriction renders the claim indefinite. In re Musgrave , 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); see also Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1373, 112 USPQ2d 1188 (Fed. Cir. 2014). Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim s 1 and 9-11 are rejected under 35 U.S.C. 102 as being anticipated by Osten et al. (US-20140297199-A1, hereinafter Osten) CLAIM 1 Regarding Claim 1, Osten teaches an information processing apparatus ( Osten, ¶ [0196-200]: “a processing system to perform the methods and operations” ) comprising: a processor ( Osten, ¶ [0199-200]: “computer-readable storage media including computer storage mechanisms (e.g., non-transitory media, such as CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations” ) configured to: acquire an organ image obtained by imaging an organ of a subject ( Osten, ¶ [0078-0082]: “a compound (e.g., a test compound or a reference compound) is administered to a non-human animal (e.g., a transgenic animal), and the animal is sacrificed … one or more tissues of the sacrificed animal can be harvested by any technique described herein or known in the art. In specific embodiments, the tissue is an entire organ of an animal (e.g., a brain and/or a liver) . The harvested tissue can be analyzed (e.g., imaged) using any technique described herein or known in the art. In a specific embodiment , the imaging technique used provides very high (e.g., single cell) resolution of the cells of the harvested tissue (e.g., an entire organ) …Automated microscopy (e.g., serial two-photon (STP) tomography) can be used for high-resolution imaging of a tissue of an animal treated with a test drug or a reference drug”. Osten discloses imaging a brain of animal ), the organ image being an image used in an evaluation test for evaluating a candidate substance for a drug ( Osten, ¶ [0093-0097]: “ a drug that affects brain function can be administered to a non-human animal (e.g., a mouse); the brain tissue (e.g., a whole brain) can be harvested by any technique known in the art and imaged at high resolution yielding a pharmacomap of the drug . Generation of detailed maps of drug-activated neurons (e.g., in the whole mouse brain) can be used to reliably link drug-evoked brain activation in a non-human animal model and drug-evoked clinical effects in humans … predict therapeutic effects of new test drugs ” ); detect an abnormal portion, in which an abnormality is estimated to occur, from the organ image ( Osten, ¶ [0175-0176, 0180-0181, 0183-0185]: “a test compound (e.g., a candidate drug) is administered on a transgenic animal (e.g., a mouse). A tissue (e.g., brain tissue) of the transgenic animal is harvested for analysis. The harvested tissue is imaged, and a computational analysis of the tissue images is performed to identify activated cells in the tissue . A multiple dimension, e.g., three-dimension (3D), data representation of the compound-evoked activation is generated. Statistical methods analyze the data representation of the compound-evoked activation to identify activated regions in the tissue … different machine learning algorithms , such as a convolutional neural network algorithm support vector machines, random forest classifiers, and boosting classifiers, can be used for automated detection of the activated cells .” Osten discloses identifying the drug-activated region in the brain images ); register the organ image with a reference image having positional information of a plurality of anatomical parts of the organ to define the anatomical parts in the organ image ( Osten, ¶ [0042-0043 and 0235-0236]: “0042: transgenic mouse brain with GFP labeling in all cells was imaged…, 0043: An internal alignment between the brain generated in FIG. 23 and MRI brain atlas …”, see figures 23-24; ¶ [0294]: “The negative binomial regression analysis reveals “hot-spots” of statistical differences between groups. Such areas are next anatomically identified, using of a reference atlas (e.g., the Allen Reference Atlas (Hawrylycz et al., PLoS computational biology 7, e1001065 (2011))) co-registered with the reference brain ”, see step H in figure 46 ); and output degree information ( Osten, ¶ [0174-0182]: “Computationally identified activation of the animal tissue is visualized in a multiple-dimension representation. From this multiple-dimension representation, a pharmacomap is generated . A pharmacomap of the test compound or a reference compound represents a unique pattern of compound-evoked activation in a non-human animal tissue in response to the test compound or reference compound …” ) indicating a degree of occurrence of the abnormal portion in each of the plurality of anatomical parts ( Osten, ¶ [0186]: “the pharmacomap information includes activated cell data, e.g., the number of activated cells per region, etc” ), wherein in detecting the abnormal portion ( Osten, ¶ [0246 and 0256]: “0256: The brains are imaged and computationally processed … identify areas of significant c-fos-GFP induction in drug versus control samples . Once such areas are determined, anatomical regions comprising the voxels with activated cells are marked up” Osten discloses the process to identify drug activated brain regions, the process are performed with 2 groups of mice ), all of the subjects constituting an administration group to which the candidate substance is administered ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …” Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ) and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) all of the subjects constituting a control group to which the candidate substance is not administered can be targeted ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …; 0246: control mice are injected i.p. with saline ”. Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ), and PNG media_image1.png 1145 709 media_image1.png Greyscale as the degree information, it is possible to output degree information generated for all of the subjects constituting the administration group, and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) degree information generated for all of the subjects constituting the control group ( Osten, ¶ [0262]: “c-fos-GFP pharmacomaps of saline and haloperidol (1 mg/kg) brains with 176,771 and 545,838 c-fos-GFP cells, respectively, are shown in FIG. 33.” Osten discloses generating pharmacomaps, which visualizes activated cells, for both two groups of mice, the one injected with testing drug and the one injected with saline, see fig. 33 below ). CLAIM 9 Regarding claim 9, Osten teaches an apparatus of Claim 1. In addition, Osten teaches the organ image is a section image ( Osten, ¶ [0049 and 0264]: “ The imaged brain was reconstructed as a series of 2D sections , typically 280 to 300 per one mouse brain as shown in FIG. 30.” ) obtained by imaging a tissue section of the organ. ( Osten, ¶ [0078-0082]: “ one or more tissues of the sacrificed animal can be harvested by any technique described herein or known in the art. In specific embodiments , the tissue is an entire organ of an animal (e.g., a brain and/or a liver ). The harvested tissue can be analyzed (e.g., imaged) using any technique described herein or known in the art. In a specific embodiment, the imaging technique used provides very high (e.g., single cell) resolution of the cells of the harvested tissue (e.g., an entire organ)” ) CLAIM 10 Regarding Claim 10, Osten teaches a method for operating an information processing apparatus ( Osten, ¶ [0196-200]: “a processing system to perform the methods and operations” ), the method comprising: acquiring an organ image obtained by imaging an organ of a subject ( Osten, ¶ [0078-0082]: “a compound (e.g., a test compound or a reference compound) is administered to a non-human animal (e.g., a transgenic animal), and the animal is sacrificed … one or more tissues of the sacrificed animal can be harvested by any technique described herein or known in the art. In specific embodiments, the tissue is an entire organ of an animal (e.g., a brain and/or a liver) . The harvested tissue can be analyzed (e.g., imaged) using any technique described herein or known in the art. In a specific embodiment , the imaging technique used provides very high (e.g., single cell) resolution of the cells of the harvested tissue (e.g., an entire organ) …Automated microscopy (e.g., serial two-photon (STP) tomography) can be used for high-resolution imaging of a tissue of an animal treated with a test drug or a reference drug”. Osten discloses imaging a brain of animal ), the organ image being an image used in an evaluation test for evaluating a candidate substance for a drug ( Osten, ¶ [0093-0097]: “ a drug that affects brain function can be administered to a non-human animal (e.g., a mouse); the brain tissue (e.g., a whole brain) can be harvested by any technique known in the art and imaged at high resolution yielding a pharmacomap of the drug . Generation of detailed maps of drug-activated neurons (e.g., in the whole mouse brain) can be used to reliably link drug-evoked brain activation in a non-human animal model and drug-evoked clinical effects in humans … predict therapeutic effects of new test drugs ” ); detecting an abnormal portion, in which an abnormality is estimated to occur, from the organ image ( Osten, ¶ [0175-0176, 0180-0181, 0183-0185]: “a test compound (e.g., a candidate drug) is administered on a transgenic animal (e.g., a mouse). A tissue (e.g., brain tissue) of the transgenic animal is harvested for analysis. The harvested tissue is imaged, and a computational analysis of the tissue images is performed to identify activated cells in the tissue . A multiple dimension, e.g., three-dimension (3D), data representation of the compound-evoked activation is generated. Statistical methods analyze the data representation of the compound-evoked activation to identify activated regions in the tissue … different machine learning algorithms , such as a convolutional neural network algorithm support vector machines, random forest classifiers, and boosting classifiers, can be used for automated detection of the activated cells .” Osten discloses identifying the drug-activated region in the brain images ); registering the organ image with a reference image having positional information of a plurality of anatomical parts of the organ to define the anatomical parts in the organ image ( Osten, ¶ [0042-0043 and 0235-0236]: “0042: transgenic mouse brain with GFP labeling in all cells was imaged…, 0043: An internal alignment between the brain generated in FIG. 23 and MRI brain atlas …”, see figures 23-24; ¶ [0294]: “The negative binomial regression analysis reveals “hot-spots” of statistical differences between groups. Such areas are next anatomically identified, using of a reference atlas (e.g., the Allen Reference Atlas (Hawrylycz et al., PLoS computational biology 7, e1001065 (2011))) co-registered with the reference brain ”, see step H in figure 46 ); and outputting degree information ( Osten, ¶ [0174-0182]: “Computationally identified activation of the animal tissue is visualized in a multiple-dimension representation. From this multiple-dimension representation, a pharmacomap is generated . A pharmacomap of the test compound or a reference compound represents a unique pattern of compound-evoked activation in a non-human animal tissue in response to the test compound or reference compound …” ) indicating a degree of occurrence of the abnormal portion in each of the plurality of anatomical parts ( Osten, ¶ [0186]: “the pharmacomap information includes activated cell data, e.g., the number of activated cells per region, etc” ), wherein in detecting the abnormal portion ( Osten, ¶ [0246 and 0256]: “0256: The brains are imaged and computationally processed … identify areas of significant c-fos-GFP induction in drug versus control samples . Once such areas are determined, anatomical regions comprising the voxels with activated cells are marked up” Osten discloses the process to identify drug activated brain regions, the process are performed with 2 groups of mice ), all of the subjects constituting an administration group to which the candidate substance is administered ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …” Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ) and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) all of the subjects constituting a control group to which the candidate substance is not administered can be targeted ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …; 0246: control mice are injected i.p. with saline ”. Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ), and as the degree information, it is possible to output degree information generated for all of the subjects constituting the administration group, and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) degree information generated for all of the subjects constituting the control group ( Osten, ¶ [0262]: “c-fos-GFP pharmacomaps of saline and haloperidol (1 mg/kg) brains with 176,771 and 545,838 c-fos-GFP cells, respectively, are shown in FIG. 33.” Osten discloses generating pharmacomaps, which visualizes activated PNG media_image1.png 1145 709 media_image1.png Greyscale cells, for both two groups of mice, the one injected with testing drug and the one injected with saline, see fig. 33 below ). CLAIM 11 Regarding Claim 11, Osten teaches a non-transitory computer-readable storage medium storing a program ( Osten, ¶ [0199-200]: “ computer-readable storage media including computer storage mechanisms (e.g., non-transitory media, such as CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations” ) for operating an information processing apparatus ( Osten, ¶ [0196-200]: “a processing system to perform the methods and operations” ), the program causing a computer to execute a process comprising: acquiring an organ image obtained by imaging an organ of a subject ( Osten, ¶ [0078-0082]: “a compound (e.g., a test compound or a reference compound) is administered to a non-human animal (e.g., a transgenic animal), and the animal is sacrificed … one or more tissues of the sacrificed animal can be harvested by any technique described herein or known in the art. In specific embodiments, the tissue is an entire organ of an animal (e.g., a brain and/or a liver) . The harvested tissue can be analyzed (e.g., imaged) using any technique described herein or known in the art. In a specific embodiment , the imaging technique used provides very high (e.g., single cell) resolution of the cells of the harvested tissue (e.g., an entire organ) …Automated microscopy (e.g., serial two-photon (STP) tomography) can be used for high-resolution imaging of a tissue of an animal treated with a test drug or a reference drug”. Osten discloses imaging a brain of animal ), the organ image being an image used in an evaluation test for evaluating a candidate substance for a drug ( Osten, ¶ [0093-0097]: “ a drug that affects brain function can be administered to a non-human animal (e.g., a mouse); the brain tissue (e.g., a whole brain) can be harvested by any technique known in the art and imaged at high resolution yielding a pharmacomap of the drug . Generation of detailed maps of drug-activated neurons (e.g., in the whole mouse brain) can be used to reliably link drug-evoked brain activation in a non-human animal model and drug-evoked clinical effects in humans … predict therapeutic effects of new test drugs ” ); detecting an abnormal portion, in which an abnormality is estimated to occur, from the organ image ( Osten, ¶ [0175-0176, 0180-0181, 0183-0185]: “a test compound (e.g., a candidate drug) is administered on a transgenic animal (e.g., a mouse). A tissue (e.g., brain tissue) of the transgenic animal is harvested for analysis. The harvested tissue is imaged, and a computational analysis of the tissue images is performed to identify activated cells in the tissue . A multiple dimension, e.g., three-dimension (3D), data representation of the compound-evoked activation is generated. Statistical methods analyze the data representation of the compound-evoked activation to identify activated regions in the tissue … different machine learning algorithms , such as a convolutional neural network algorithm support vector machines, random forest classifiers, and boosting classifiers, can be used for automated detection of the activated cells .” Osten discloses identifying the drug-activated region in the brain images ); registering the organ image with a reference image having positional information of a plurality of anatomical parts of the organ to define the anatomical parts in the organ image ( Osten, ¶ [0042-0043 and 0235-0236]: “0042: transgenic mouse brain with GFP labeling in all cells was imaged…, 0043: An internal alignment between the brain generated in FIG. 23 and MRI brain atlas …”, see figures 23-24; ¶ [0294]: “The negative binomial regression analysis reveals “hot-spots” of statistical differences between groups. Such areas are next anatomically identified, using of a reference atlas (e.g., the Allen Reference Atlas (Hawrylycz et al., PLoS computational biology 7, e1001065 (2011))) co-registered with the reference brain ”, see step H in figure 46 ); and outputing degree information ( Osten, ¶ [0174-0182]: “Computationally identified activation of the animal tissue is visualized in a multiple-dimension representation. From this multiple-dimension representation, a pharmacomap is generated . A pharmacomap of the test compound or a reference compound represents a unique pattern of compound-evoked activation in a non-human animal tissue in response to the test compound or reference compound …” ) indicating a degree of occurrence of the abnormal portion in each of the plurality of anatomical parts ( Osten, ¶ [0186]: “the pharmacomap information includes activated cell data, e.g., the number of activated cells per region, etc” ), wherein in detecting the abnormal portion ( Osten, ¶ [0246 and 0256]: “0256: The brains are imaged and computationally processed … identify areas of significant c-fos-GFP induction in drug versus control samples . Once such areas are determined, anatomical regions comprising the voxels with activated cells are marked up” Osten discloses the process to identify drug activated brain regions, the process are performed with 2 groups of mice ), all of the subjects constituting an administration group to which the candidate substance is administered ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …” Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ) and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) all of the subjects constituting a control group to which the candidate substance is not administered can be targeted ( Osten, ¶ [0240 and 0246]: “0240: two mice , one injected with saline and the other with haloperidol at 1 mg/kg (FIG. 27). Three hours later, the mice were euthanized, their brains imaged by whole-mount two-photon microscopy …; 0246: control mice are injected i.p. with saline ”. Osten discloses two groups of mice, one is injected with the testing drug, one is injected with saline ), and PNG media_image1.png 1145 709 media_image1.png Greyscale as the degree information, it is possible to output degree information generated for all of the subjects constituting the administration group, and/or ( ***The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. ) degree information generated for all of the subjects constituting the control group ( Osten, ¶ [0262]: “c-fos-GFP pharmacomaps of saline and haloperidol (1 mg/kg) brains with 176,771 and 545,838 c-fos-GFP cells, respectively, are shown in FIG. 33.” Osten discloses generating pharmacomaps, which visualizes activated cells, for both two groups of mice, the one injected with testing drug and the one injected with saline, see fig. 33 below ) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim (s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osten in view of Wang et al. (Wang, Shenzhi, et al. " Glancing at the patch: Anomaly localization with global and local feature comparison ." IEEE, published 2021, hereinafter Wang) . CLAIM 2 In regards to Claim 2, Osten teaches the apparatus of Claim 1. Osten does not explicitly disclose extracting a feature amount of a region of interest in the organ image; and determining whether or not the region of interest is the abnormal portion on the basis of a difference between the feature amount extracted from the region of interest and a reference feature amount . Wang is in the same field of art of anomaly detection. Further, Wang teaches extracting a feature amount of a region of interest in the organ image ( Wang, page 256, section 3.1 “Local Feature Extraction. We use Local-Net, a lightweight neural network, to embed image patches into local features ”; see “local feature” in annotated figure 2 below ); and determining whether or not the region of interest is the abnormal portion ( Wang, page 257, section 3.2. and 3.3: “With the scoring function to assign the anomaly score to a particular patch, we further propose a pipeline to aggregate the anomaly scores for different patches into an anomaly score map ” Wang teaches detecting and locating the anomaly in the image with an anomaly score map ) on the basis of a difference between ( Wang, page 257, section 3.2: “Inconsistency Anomaly Detection Head. Inconsistency anomaly detection head (IAD-head) is designed to detect the inconsistency between the local feature Zl and the global feature Zg … We assume that in normal images local and global features are consistent, while in abnormal images the situation is the contrary. Therefore, in the training process, lIAD is utilized as a loss to close the distance between Zl and Zg . During inference, lIAD serves as a scoring function to indicate the global-local inconsistency lying in the patch”, see equation (7) ) the feature amount extracted from the region of interest and a reference feature amount. ( Wang, page 256-257, section 3.1: “Global Feature Extraction. Another deep model, named Global-Net, is employed to extract the global feature from the surrounding of the patch ”; see “global PNG media_image2.png 765 1329 media_image2.png Greyscale feature” in annotated figure 2 below ) Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Osten by incorporating the anomaly detection framework that is taught by Wang, to make a system to detect abnormal image features that considers both the global and local information; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the performance of anomaly detection system ( Wang, page 261, section 5: “we propose an unsupervised anomaly localization approach with due consideration to both the global and the local information from an image. Two anomaly detection heads are introduced to sufficiently spot the discrepancy between global and local features. With the scoring function developed from such multi-head design, we achieve high-precision anomaly localization, significantly surpassing state-of-the-art alternatives ” ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 3 In regards to Claim 3, the combination of Osten and Wang teaches the apparatus of Claim 2. In addition, the combination of Osten and Wang teaches the reference feature amount is a feature amount extracted from a region around the region of interest. ( Wang, page 256-257, section 3.1: “Global Feature Extraction. Another deep model, named Global-Net, is employed to extract the global feature from the surrounding of the patch ”; see “global feature” in annotated figure 2 above ) CLAIM 12 07-21-aia AIA Claim (s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osten in view of Wang, and further in view of Tsai et al. (Tsai, Chin-Chia, Tsung-Hsuan Wu, and Shang-Hong Lai. " Multi-scale patch-based representation learning for image anomaly detection and segmentation ." IEEE, published January 2022, hereinafter Tsai) . In regards to Claim 12, the combination of Osten and Wang teaches the apparatus of Claim 2. In addition, the combination of Osten and Wang teaches determine whether or not the region of interest is the abnormal portion. ( Wang, page 257, section 3.2. and 3.3: “With the scoring function to assign the anomaly score to a particular patch, we further propose a pipeline to aggregate the anomaly scores for different patches into an anomaly score map ” Wang teaches detecting and locating the anomaly in the image with an anomaly score map ) The combination of Osten and Wang does not explicitly disclose gradually change the size of the region of interest to extract the feature amount . Tsai is in the same field of art of anomaly detection. Further, Tsai teaches gradually change the size of the region of interest to extract the feature amount. ( Tsai, pages 3068-3069, section 3.4, see reconstructed text with Fig. 4 below. Tsai teaches a patch-based anomaly detection system which compares neighboring patches like Wang. In addition, Tsai teaches three sizes for image patch 64x64, 32x32 and 16x16, which then yield three anomaly maps, respectively ) PNG media_image3.png 346 765 media_image3.png Greyscale PNG media_image4.png 718 1538 media_image4.png Greyscale Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Osten and Wang by incorporating the multi-scale patch-based representation learning method that is taught by Tsai, to make an anomaly detection system that learns image feature at different scales ; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to detecting abnormal regions regardless of their size ( Tsai, page 3072, section 5: “Our experimental results prove that considering the global and local context of an image at the same time leads to excel lent representation learning for image anomaly detection. Moreover, our multi-scale system is capable of detecting anomalous regions of different sizes. Our experimental results demonstrate that the proposed method achieves SOTA accuracy on the benchmark datasets for image anomaly detection and segmentation ” ). In addition, Wang also suggests improving his system by incorporating multi-scale learning ( Wang, page 261, section 5: “However, there still remains some future work worth exploration. On one hand , our approach uses a fixed patch size regardless of the anomaly type. To further improve the robustness under various anomaly scales, techniques such as score map averaging [7] and feature pyramid [24] could be considered .” ) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention . 07-21-aia AIA Claim (s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osten in view of Xu et al. (US-20220138957-A1, hereinafter Xu) . CLAIM 5 In regards to Claim 5, Osten teaches the apparatus of claim 1. In addition, Osten teaches displaying the defined anatomical part to be superimposed on the organ image. ( Osten, ¶ [0060]: “FIG. 41 demonstrates averaged voxelization results registered to the reference brain (D) from handling control (A), object control (B), and social stimulation (C) group, and a 3D overlay of the activated brain area and the reference brain (F) ”, see FIG. 41 ) Osten does not explicitly disclose performing control to display the defined anatomical part on a display to be superimposed on the organ image . (emphasis added) Xu is in the same field of art of biomedical image inspection. Further, Xu teaches perform control to display image data on a display ( Xu, ¶ [0085]: “computing system 163 may include one or more display devices. The one or more display devices may display an image (e.g., an image to be modified illustrated in FIGS. 4-15). A user (e.g., a doctor, a technician, an engineer, etc.) may perform, on the image through the one or more display devices, one or more modifications related to identifying a target region in the image”, ¶ [0305]: “The image to be modified displayed on the screen of the display device ” ) Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Osten by incorporating the display unit with a screen that is taught by Xu, to make a system to display image data; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need to improve the interaction between user and the system ( Xu, ¶ [0099]: “ the I/O 230 may enable user interaction with the processing device 110 . In some embodiments, the I/O 230 may include an input device and an output device. Exemplary input devices may include a keyboard, a mouse, a touch screen, a microphone, a trackball, or the like, or a combination thereof. Exemplary output devices may include a display device …” ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention. CLAIM 6 In regards to Claim 6, the combination of Osten and Xu teaches the apparatus of Claim 5. In addition, the combination of Osten and Xu teaches receive an instruction to correct the defined anatomical part. ( Xu, ¶ [0143-0146]: “the operation relating to identification of the target region may include adding, deleting, or adjusting the identification of the target region in the image to be modified . For example, the image to be modified may be the image to be segmented that includes no identification of the target region. The user may delineate an outline of the target region in the image to be segmented , and one or more modifications may be obtained …” ) CLAIM 7 In regards to Claim 7, the combination of Osten and Xu teaches the apparatus of Claim 6. In addition, the combination of Osten and Xu teaches output the degree information generated on the basis of the anatomical part corrected according to the correction instruction. ( Osten, ¶ [0174-0182]: “Computationally identified activation of the animal tissue is visualized in a multiple-dimension representation. From this multiple-dimension representation, a pharmacomap is generated . A pharmacomap of the test compound or a reference compound represents a unique pattern of compound-evoked activation in a non-human animal tissue in response to the test compound or reference compound …” ) CLAIM 8 07-21-aia AIA Claim (s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osten in view of Kamali-Zare et al. (US-20200143948-A1, hereinafter Kamali-Zare) . In regards to Claim 8, Osten teaches the apparatus of Claim 1. In addition, Osten teaches the degree information includes the number of the abnormal portions in each of the plurality of anatomical parts. ( Osten, ¶ [0186]: “the pharmacomap information includes activated cell data, e.g., the number of activated cells per region, etc” ) Osten does not explicitly disclose the degree information includes an area ratio of the abnormal portion in each of the plurality of anatomical parts . Kamali-Zare is in the same field of art of detecting abnormalities in brain image. Further, Kamali-Zare teaches the degree information includes an area ratio of the abnormal portion in each of the plurality of anatomical parts. ( Kamali-Zare, ¶ [0115]: “The one or more brain maps may comprise a qualitative abnormality map (such as a qualitative neurodegeneration map). The qualitative abnormality map may display whether brain tissue associated with a given voxel displays a microstructure consistent with a brain disorder (such as a neurodegenerative disorder), for each voxel of the plurality of voxels. The qualitative abnormality map may be a binary map, with each voxel assigned a microstructure consistent with a brain disorder displayed in the same color (such as gray or red) on the qualitative abnormality map … The qualitative abnormality map may be a percent abnormality map (such as a percent neurodegeneration (PND) map) that indicates a percentage of a subject's brain (or region of a subject's brain) that displays tissue microstructure consistent with a brain disorder (such as a neurodegenerative disorder)” Kamali-Zare teaches identifying abnormal brain region that is consistent with a disorder, and calculating a percentage of the abnormal region to the entire brain ) Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Osten by incorporating method to calculate an abnormal area ratio that is taught by Kamali-Zare, to make a system that not only detect abnormal region but also output a qualitative information about the detection and ; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes that such qualitative information would benefit the user’s interpretation of the detection result ( Kamali-Zare, ¶ [0005]: “Such approaches may leverage a deeper understanding of brain tissue microstructure to more reliably predict and interpret the health of the brain ” ). Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Pertinent Arts 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Mori et al. (US-20180314691-A1) which is directed to a system to: process brain image data, identify abnormalities in the image, output human-understandable sentence report regarding the identified abnormalities. The report indicates a degree information of the abnormalities . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHUT HUY (JEREMY) PHAM whose telephone number is (703)756-5797. The examiner can normally be reached Mo - Fr. 8:30am - 6pm ET. 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, O'Neal Mistry can be reached on (313)446-4912. 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. /NHUT HUY PHAM/Examiner, Art Unit 2674 /ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674 Application/Control Number: 18/895,431 Page 2 Art Unit: 2674 Application/Control Number: 18/895,431 Page 3 Art Unit: 2674 Application/Control Number: 18/895,431 Page 4 Art Unit: 2674 Application/Control Number: 18/895,431 Page 5 Art Unit: 2674 Application/Control Number: 18/895,431 Page 6 Art Unit: 2674 Application/Control Number: 18/895,431 Page 7 Art Unit: 2674 Application/Control Number: 18/895,431 Page 8 Art Unit: 2674 Application/Control Number: 18/895,431 Page 9 Art Unit: 2674 Application/Control Number: 18/895,431 Page 10 Art Unit: 2674 Application/Control Number: 18/895,431 Page 11 Art Unit: 2674 Application/Control Number: 18/895,431 Page 12 Art Unit: 2674 Application/Control Number: 18/895,431 Page 13 Art Unit: 2674 Application/Control Number: 18/895,431 Page 14 Art Unit: 2674 Application/Control Number: 18/895,431 Page 15 Art Unit: 2674 Application/Control Number: 18/895,431 Page 16 Art Unit: 2674 Application/Control Number: 18/895,431 Page 17 Art Unit: 2674 Application/Control Number: 18/895,431 Page 18 Art Unit: 2674 Application/Control Number: 18/895,431 Page 19 Art Unit: 2674 Application/Control Number: 18/895,431 Page 20 Art Unit: 2674 Application/Control Number: 18/895,431 Page 21 Art Unit: 2674 Application/Control Number: 18/895,431 Page 22 Art Unit: 2674 Application/Control Number: 18/895,431 Page 23 Art Unit: 2674 Application/Control Number: 18/895,431 Page 24 Art Unit: 2674 Application/Control Number: 18/895,431 Page 25 Art Unit: 2674 Application/Control Number: 18/895,431 Page 26 Art Unit: 2674 Application/Control Number: 18/895,431 Page 27 Art Unit: 2674
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