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 .
Response to Arguments
The reply filed on 25 June 2026 has been entered. Applicant’s arguments with respect to claims 1-19 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. Claims 1-19 are pending in this application and have been considered below.
Priority
Receipt is acknowledged that application claims priority to foreign application with application number JP 2023-064794 dated 12 March 2023. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDSs dated 8 April 2024, 23 May 2024 and 17 October 2024 that have been previously considered remain placed in the application file.
Specification - Title
The title of the invention has been amended. The objection to the title is withdrawn.
1st Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
goodness-of-fit, Claim 1, 5-7, 9-10, 12 and 14-17: paragraphs [0022], Specifically, the goodness of fit relating to the ground truth data is an indicator (level of reliability) representing how reliable the correct output (endocardial contour point) indicated by the ground truth data in each training data pair can be as the data indicating the actual region of the object.
Thus, a goodness-of-fit is a reliability indicator that a given pixel is part of an object (such as a heart). While the definition indicates an application as a heart object or medical object identifier, because there is no language in the independent claims restricting the application, an attempt has been made to use medical references. However, other references within image analysis may apply. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
2nd Claim Interpretation
Claim 1 has been amended. The interpretation of claim 1 under 35 USC 112(f) is withdrawn.
Claim Rejections - 35 USC § 112
Claim 9 has been amended. The rejection of claim 9 under 35 USC 112 (d) is withdrawn.
Claim Rejections - 35 USC § 101
Claims 1 and 12 have been amended. The rejection of claims 1-19 under 35 USC 101 is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-19 (all claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2025 0331796 A1, (Uemura et al.) in view of US Patent Publication 2019 0237186 A1, (El-Baz et al.). The references are listed in a PTO-892 from the Office Action in which they are first used. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
Claim 1
Regarding Claim 1, Uemura et al. teach an information processing apparatus for generating a learning model that performs, by using input image data obtained by imaging an object, estimation relating to the object rendered in the image data ("X-ray image as input and outputs information," paragraph [0046]), the information processing apparatus comprising one or more memories storing executable instructions ("The storage unit 12 includes a RAM (Random Access Memory), a flash memory, a hard disk, an SSD (Solid State Drive), etc.," paragraph [0048]); and
one or more processors configured to execute the executable instructions ("The control unit 11 includes one or more processors such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), an AI chip (AI semiconductor), etc.," paragraph [0047]) to:
acquire, as training data used for generating the learning model, learning image data obtained by imaging the object ("the information processing apparatus 10 inputs the frontal hip joint X-ray image to the learning model 12M, thereby acquiring information on the bone density of the proximal femur from the learning model 12M," paragraph [0046]) and ground truth data indicating information about the object in the learning image data ("The learning model 12Mb of this embodiment is generated by being trained using training data in which a training X-ray image (frontal hip joint X-ray image), a DRR image of a gluteus maximus muscle which is a ground truth," paragraph [0149]).
Uemura et al. is not relied upon to explicitly teach all of reliability of ground truth data.
However, El-Baz et al. teach acquire goodness of fit relating to the ground truth data, the goodness of fit being an indicator or reliability of the ground truth data ("Better segmentation is indicated is indicated by higher DSC values (ideal segmentation is indicated by a DSC value of one). If there is no overlap, DSC will be assigned a value of zero," paragraph [0155] where DSC is goodness of fit); and
perform training on the learning model based on the training data and the goodness of fit ("In addition to the LOSO approach, we have performed a four-fold cross-validation test where 75% of the data was used for training and the other 25% for testing, and a 10-fold cross-validation test where 90% of the data was used for training and the other 10% for testing, to further validate and justify the performance of the developed SNCAE classifier," paragraph [0167]) such that an influence, on training of the learning model, of ground truth data having a lower goodness of fit is reduced ("In each case, the constructed CDFs were used to train and test the SNCAE classifier using the same LOSO scenario. Without being bound by theory, the improved accuracy of the instant segmentation technique may result from more accurate and reliable segmentation of the 3D kidneys," paragraph [0163]).
Therefore, taking the teachings of Uemura et al. and El-Baz et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify X-ray image data improvements as taught by Uemura et al. to use Kidney image classification as taught by El-Baz et al., showing that Uemura et al. and El-Baz et al. are analogous art because both are medical image analysis. The suggestion/motivation for combination is that, “Current studies utilizing DCE-MRI, DW-MRI, and BOLD-MRI to study renal rejection share some limitations. For example, their methods employ a manual delineation of the kidney using a 2D region of interest (ROI), which makes this delineation subjective” as noted by the El-Baz et al. disclosure in paragraph [0010], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that medical images need classification of the images and this is difficult; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 2
Regarding claim 2, Uemura et al. teach the information processing apparatus according to claim 1, wherein
the learning model is a learning model for estimating spatial information about the object in the image data ("the learning model 12M of this embodiment is trained using training data that allows for spatial correspondence between abundant 3D data obtained by CT and an X-ray image with high accuracy," paragraph [0071]), and
the one or more processors are configure to acquire information about a region of the object as ground truth data in the training data ("12Ml is generated by preparing training data that associates a training DRR image with training (ground truth) bone density, and using this training data to machine-train the untrained learning model 12Ml," paragraph [0086]).
Claim 3
Regarding claim 3, Uemura et al. teach the information processing apparatus according to claim 1, wherein
the learning model is a learning model for estimating a position of a feature point of the object in the image data ("the learning model 12M of this embodiment is trained using training data that allows for spatial correspondence between abundant 3D data obtained by CT and an X-ray image with high accuracy," paragraph [0071]), and
the one or more processors are configured to acquire information about the position of the feature point of the object as ground truth data in the training data ("a difference ( error, loss related to muscle mass) between the muscle mass which is the ground truth and the muscle mass based on the DRR image of the muscle region generated by the learning model 12Mb can be fed back. Therefore, according to the training process of this embodiment, the learning model 12Mb is generated to predict a DRR image and muscle mass of a muscle region included in an X-ray image with high accuracy when the X-ray image is input," paragraph [0150] where a region with high accuracy teaches position of the feature point).
Claim 4
Regarding claim 4, Uemura et al. teach the information processing apparatus according to claim 1, wherein
the learning model is a learning model for estimating a contour of the object in the image data ("the control unit 11 calculates a correlation value between the contour of the region of interest in the X-ray image specified in step Slll and the contour of the region of interest in the pseudo DRR image," paragraph [0127]), and
the one or more processors are configured to acquire information about the contour of the object as ground truth data in the training data ("the control unit 11 specifies a projection condition in the pseudo DRR image maximizing the correlation value between the contour of the bone regions in the X-ray image and the contours of the pseudo DRR image by using the CMA-ES in the processing of steps S122 to S125," paragraph [0135] and "Then, the control unit 11 extracts a half-section image including the left proximal femur from the frontal hip joint X-ray image acquired in step S11 (S18), and stores, as training data, the extracted X-ray image (half-section image of the frontal hip joint X-ray image) and the DRR image of the region of interest generated in step S17 in association with each other in the training DB 12b (S19)," paragraph [0136]).
Claim 5
Regarding claim 5, Uemura et al. teach the information processing apparatus according to claim 1, wherein
the one or more processors are configured to calculate the goodness of fit, based on pixel values of a periphery of a position of the object in the learning image data, the position of the object being indicated by the ground truth data in the training data ("the control unit 11 reads a pair of an X-ray image (frontal hip joint X-ray image) and a CT image from the medical image DB 12a, performs a luminance value calibration process on the read CT image, and then classifies each pixel in the CT image as a bone region, a muscle region, and another region (musculoskeletal region)," paragraph [0101] where goodness of fit is interpreted as classifies each pixel as a region and "defining achievement of alignment between a contour in a DRR image (here, pseudo DRR image) of a target site (bone region, here, pelvis) generated from a 3D region of the target site in a CT image and a contour of a target site in an actual X-ray image," paragraph [0128]).
Claim 6
Regarding claim 6, Uemura et al. teach the information processing apparatus according to claim 5, wherein
the one or more processors are configured to calculate the goodness of fit, based on a luminance gradient indicated by the pixel values ("the control unit 11 detects a luminance gradient (edge) of the image based on the pixel value of each pixel, and specifies a capturing target in the X-ray image based on the detected luminance gradient," paragraph [0064]).
Claim 7
Regarding claim 7, Uemura et al. teach the information processing apparatus according to claim 3, wherein the one or more processors are configured to calculate the goodness of fit of an individual feature point of the ground truth data in the training data, based on a positional relationship between the individual feature point and a feature point other than the individual feature point ("the control unit 11 reads a pair of an X-ray image (frontal hip joint X-ray image) and a CT image from the medical image DB 12a, performs a luminance value calibration process on the read CT image, and then classifies each pixel in the CT image as a bone region, a muscle region, and another region (musculoskeletal region)," paragraph [0101] where goodness of fit is interpreted as classifies each pixel as a region and "defining achievement of alignment between a contour in a DRR image (here, pseudo DRR image) of a target site (bone region, here, pelvis) generated from a 3D region of the target site in a CT image and a contour of a target site in an actual X-ray image," paragraph [0128]).
Claim 8
Regarding claim 8, Uemura et al. teach the information processing apparatus according to claim 7, wherein the positional relationship is a curvature of a contour line of the object based on the feature points ( "defining achievement of alignment between a contour in a DRR image (here, pseudo DRR image) of a target site (bone region, here, pelvis) generated from a 3D region of the target site in a CT image and a contour of a target site in an actual X-ray image," paragraph [0128]).
Claim 9
Regarding claim 9, Uemura et al. teach the information processing apparatus according to claim 1, wherein the one or more processors are configured to calculate the goodness of fit, relating to the ground truth data based on medical information of a subject including the object or information indicating a cardiac phase ("the control unit 11 reads a pair of an X-ray image (frontal hip joint X-ray image) and a CT image from the medical image DB 12a, performs a luminance value calibration process on the read CT image, and then classifies each pixel in the CT image as a bone region, a muscle region, and another region (musculoskeletal region)," paragraph [0101] where goodness of fit is interpreted as classifies each pixel as a region).
Claim 10
Regarding claim 10, Uemura et al. teach the information processing apparatus according to claim 1, wherein the one or more processors are configured to perform training on the learning model by applying the goodness of fit to a difference between an estimated value relating to the object, which is estimated by the learning model, and a correct value relating to the object, which is indicated by the ground truth data ("the screen illustrated in FIG. 8 displays, as the test results for the bone density based on the frontal hip joint X-ray image, a predicted DRR image, a name of a target site in the predicted DRR image (left proximal femur in FIG. 8), bone density estimated from the predicted DRR image," paragraph [0076]).
Claim 11
Regarding claim 11, Uemura et al. teach the information processing apparatus according to claim 10, wherein the estimated value and the correct value are a pixel value of a pixel corresponding to the object ("Note that the control unit 11 may calculate the muscle mass for each pixel based on each pixel value in the predicted DRR image, and may calculate the muscle mass in the muscle region by integrating the muscle masses corresponding to each pixel. Furthermore, the control unit 11 may predict the muscle mass of the entire body of the subject based on the muscle mass in each muscle region. For example, by registering the muscle mass of each muscle of the subject, such as the gluteus maximus muscle, gluteus medius muscle, and hamstrings, in association with the muscle mass of the entire body of the subject, the muscle mass of the entire body of the subject can be predicted from the muscle mass of each muscle estimated from the predicted DRR image," paragraph [0105] where estimating is predicting).
Claim 12
Regarding claim 12, Uemura et al. teach an information processing apparatus, comprising:
one or more memories storing executable instructions ("The storage unit 12 includes a RAM (Random Access Memory), a flash memory, a hard disk, an SSD (Solid State Drive), etc.," paragraph [0048]); and
one or more processors configured to execute the executable instructions ("The control unit 11 includes one or more processors such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), an AI chip (AI semiconductor), etc.," paragraph [0047]) to:
acquire input image data obtained by imaging an object ("X-ray image as input and outputs information," paragraph [0046]);
acquire a learning model ("the information processing apparatus 10 inputs the frontal hip joint X-ray image to the learning model 12M, thereby acquiring information on the bone density of the proximal femur from the learning model 12M," paragraph [0046]); and
perform an estimation process relating to the object rendered in the input image data by using the input image data and the learning model ("Note that the control unit 11 may calculate the muscle mass for each pixel based on each pixel value in the predicted DRR image, and may calculate the muscle mass in the muscle region by integrating the muscle masses corresponding to each pixel. Furthermore, the control unit 11 may predict the muscle mass of the entire body of the subject based on the muscle mass in each muscle region. For example, by registering the muscle mass of each muscle of the subject, such as the gluteus maximus muscle, gluteus medius muscle, and hamstrings, in association with the muscle mass of the entire body of the subject, the muscle mass of the entire body of the subject can be predicted from the muscle mass of each muscle estimated from the predicted DRR image," paragraph [0105] where estimating is predicting),
wherein the learning model is a pre-trained model generated by:
acquiring, as training data used for generating the learning model, learning image data obtained by imaging the object ("the information processing apparatus 10 inputs the frontal hip joint X-ray image to the learning model 12M, thereby acquiring information on the bone density of the proximal femur from the learning model 12M," paragraph [0046]) and ground truth data indicating information about the object in the learning image data ("The learning model 12Mb of this embodiment is generated by being trained using training data in which a training X-ray image (frontal hip joint X-ray image), a DRR image of a gluteus maximus muscle which is a ground truth," paragraph [0149]).
Uemura et al. is not relied upon to explicitly teach all of reliability of ground truth data.
However, El-Baz et al. teach acquiring goodness of fit relating to the ground truth data, the goodness of fit being an indicator of reliability of the ground truth data ("Better segmentation is indicated is indicated by higher DSC values (ideal segmentation is indicated by a DSC value of one). If there is no overlap, DSC will be assigned a value of zero," paragraph [0155] where DSC is goodness of fit); and
performing training on the learning model by using the training data and the goodness of fit ("In addition to the LOSO approach, we have performed a four-fold cross-validation test where 75% of the data was used for training and the other 25% for testing, and a 10-fold cross-validation test where 90% of the data was used for training and the other 10% for testing, to further validate and justify the performance of the developed SNCAE classifier," paragraph [0167]) such that an influence, on training of the learning model, of ground truth data having a lower goodness of fit is reduced ("In each case, the constructed CDFs were used to train and test the SNCAE classifier using the same LOSO scenario. Without being bound by theory, the improved accuracy of the instant segmentation technique may result from more accurate and reliable segmentation of the 3D kidneys," paragraph [0163]).
Uemura et al. and El-Baz et al. are combined as per claim 1.
Claim 13
Regarding claim 13, Uemura et al. teach the information processing apparatus according to claim 12, wherein the one or more processors are further configured to display an estimation result of the estimation process (" The control unit 11 stores test results including the calculated muscle density and muscle mass of each muscle in, for example, the electronic medical record data (S45), generates a test result screen illustrated in FIG. 17, and outputs the test result screen to the display unit 15 (S46)," paragraph [0106]).
Claim 14
Regarding claim 14, Uemura et al. teach the information processing apparatus according to claim 13, wherein the one or more processors are further configured to acquire goodness of fit relating to the input image data ("the control unit 11 reads a pair of an X-ray image (frontal hip joint X-ray image) and a CT image from the medical image DB 12a, performs a luminance value calibration process on the read CT image, and then classifies each pixel in the CT image as a bone region, a muscle region, and another region (musculoskeletal region)," paragraph [0101] where goodness of fit is interpreted as classifies each pixel as a region and "defining achievement of alignment between a contour in a DRR image (here, pseudo DRR image) of a target site (bone region, here, pelvis) generated from a 3D region of the target site in a CT image and a contour of a target site in an actual X-ray image," paragraph [0128]), and
the one or more processors are further configured to display the goodness of fit relating to the input image data (" The control unit 11 stores test results including the calculated muscle density and muscle mass of each muscle in, for example, the electronic medical record data (S45), generates a test result screen illustrated in FIG. 17, and outputs the test result screen to the display unit 15 (S46)," paragraph [0106]).
Claim 15
Regarding claim 15, Uemura et al. teach the information processing apparatus according to claim 12, wherein the learning model is generated by learning based on a result obtained by applying the goodness of fit to a difference between an estimation result and the ground truth data ("the control unit 11 reads a pair of an X-ray image (frontal hip joint X-ray image) and a CT image from the medical image DB 12a, performs a luminance value calibration process on the read CT image, and then classifies each pixel in the CT image as a bone region, a muscle region, and another region (musculoskeletal region)," paragraph [0101] where goodness of fit is interpreted as classifies each pixel as a region and "defining achievement of alignment between a contour in a DRR image (here, pseudo DRR image) of a target site (bone region, here, pelvis) generated from a 3D region of the target site in a CT image and a contour of a target site in an actual X-ray image," paragraph [0128]).
Claim 16
Regarding claim 16, Uemura et al. teach an information processing method for generating a learning model that performs, by using input image data obtained by imaging an object, estimation relating to the object rendered in the image data, ("the information processing apparatus 10 performs machine learning in advance to learn predetermined training data, and prepares a learning model 12M," paragraph [0046]) the information processing method comprising:
acquiring, as training data used for generating the learning model, learning image data obtained by imaging the object ("the information processing apparatus 10 inputs the frontal hip joint X-ray image to the learning model 12M, thereby acquiring information on the bone density of the proximal femur from the learning model 12M," paragraph [0046]) and ground truth data indicating information about the object in the learning image data "The learning model 12Mb of this embodiment is generated by being trained using training data in which a training X-ray image (frontal hip joint X-ray image), a DRR image of a gluteus maximus muscle which is a ground truth," paragraph [0149]).
Uemura et al. is not relied upon to explicitly teach all of reliability of ground truth data.
However, El-Baz et al. teach acquiring goodness of fit relating to the ground truth data, the goodness of fit being an indicator of reliability of the ground truth data ("Better segmentation is indicated is indicated by higher DSC values (ideal segmentation is indicated by a DSC value of one). If there is no overlap, DSC will be assigned a value of zero," paragraph [0155] where DSC is goodness of fit); and
performing training on the learning model, by using the training data and the goodness of fit ("In addition to the LOSO approach, we have performed a four-fold cross-validation test where 75% of the data was used for training and the other 25% for testing, and a 10-fold cross-validation test where 90% of the data was used for training and the other 10% for testing, to further validate and justify the performance of the developed SNCAE classifier," paragraph [0167]) such that an influence, on training of the learning model, of ground truth data having a lower goodness of fit is reduced ("In each case, the constructed CDFs were used to train and test the SNCAE classifier using the same LOSO scenario. Without being bound by theory, the improved accuracy of the instant segmentation technique may result from more accurate and reliable segmentation of the 3D kidneys," paragraph [0163]).
Uemura et al. and El-Baz et al. are combined as per claim 1.
Claim 17
Regarding claim 17, Uemura et al. teach an information processing method ("the information processing apparatus 10 performs machine learning in advance to learn predetermined training data, and prepares a learning model 12M," paragraph [0046]) , comprising:
acquiring input image data obtained by imaging an object ("X-ray image as input and outputs information," paragraph [0046]);
acquiring a learning model; and
performing an estimation process relating to the object rendered in the input image data by using the input image data and the learning model ("Note that the control unit 11 may calculate the muscle mass for each pixel based on each pixel value in the predicted DRR image, and may calculate the muscle mass in the muscle region by integrating the muscle masses corresponding to each pixel. Furthermore, the control unit 11 may predict the muscle mass of the entire body of the subject based on the muscle mass in each muscle region. For example, by registering the muscle mass of each muscle of the subject, such as the gluteus maximus muscle, gluteus medius muscle, and hamstrings, in association with the muscle mass of the entire body of the subject, the muscle mass of the entire body of the subject can be predicted from the muscle mass of each muscle estimated from the predicted DRR image," paragraph [0105] where estimating is predicting),
wherein the learning model is a pre-trained model generated by:
acquiring, as training data used for generating the learning model, learning image data obtained by imaging the object ("the information processing apparatus 10 inputs the frontal hip joint X-ray image to the learning model 12M, thereby acquiring information on the bone density of the proximal femur from the learning model 12M," paragraph [0046]) and ground truth data indicating information about the object in the learning image data ("The learning model 12Mb of this embodiment is generated by being trained using training data in which a training X-ray image (frontal hip joint X-ray image), a DRR image of a gluteus maximus muscle which is a ground truth," paragraph [0149]).
Uemura et al. is not relied upon to explicitly teach all of reliability of ground truth data.
However, El-Baz et al. teach acquiring goodness of fit relating to the ground truth data, the goodness of fit being an indicator of reliability of the ground truth data ("Better segmentation is indicated is indicated by higher DSC values (ideal segmentation is indicated by a DSC value of one). If there is no overlap, DSC will be assigned a value of zero," paragraph [0155] where DSC is goodness of fit); and
performing training on the learning model by using the training data and the goodness of fit ("In addition to the LOSO approach, we have performed a four-fold cross-validation test where 75% of the data was used for training and the other 25% for testing, and a 10-fold cross-validation test where 90% of the data was used for training and the other 10% for testing, to further validate and justify the performance of the developed SNCAE classifier," paragraph [0167]) such that an influence, on training of the learning model, of ground truth data having a lower goodness of fit is reduced ("In each case, the constructed CDFs were used to train and test the SNCAE classifier using the same LOSO scenario. Without being bound by theory, the improved accuracy of the instant segmentation technique may result from more accurate and reliable segmentation of the 3D kidneys," paragraph [0163]).
Uemura et al. and El-Baz et al. are combined as per claim 1.
Claim 18
Regarding claim 18, Uemura et al. teach a non-transitory computer-readable storage medium with an executable program stored thereon, that when executed, instructs a processor to perform the method of claim 16 ("information processing apparatus 10 is an apparatus capable of processing various types of information and transmitting and receiving information, and is, for example, a personal computer, a server computer, a workstation, etc. The information processing apparatus 10 is installed and used in medical institutions, testing institutions, research institutions, etc," paragraph [0045]).
Claim 19
Regarding claim 19, Uemura et al. teach a non-transitory computer-readable storage medium with an executable program stored thereon, that when executed, instructs a processor to perform the method of claim 17 ("information processing apparatus 10 is an apparatus capable of processing various types of information and transmitting and receiving information, and is, for example, a personal computer, a server computer, a workstation, etc. The information processing apparatus 10 is installed and used in medical institutions, testing institutions, research institutions, etc," paragraph [0045]).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2022 0237801 A1 to Kaufman et al. discloses generating a multiclass image segmentation model(s) can include receiving multiple single-class image datasets, receiving a target mask for each of the single-class image datasets, receiving a condition of an object associated with each of the single-class image datasets, and generating the multiclass image segmentation model(s) based on the single-class image datasets, the target masks, and the identification of the target objects.
US Patent 11,538,163 B1 to Li et al. discloses detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Heath E. Wells/Examiner, Art Unit 2664
Date: 9 September 2026
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