Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendments filed on January 19th, 2026 have been entered and made of record. By these amendments, claims 1-3, 6 and 7 were amended. Claims 1-7 remain pending and rejected.
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, 2 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (Pub. No.: US 2021/0303935 A1), hereinafter Ma, in view of Narukiyo et al. (Pub. No.: US 2023/0289964 A1), hereinafter Narukiyo and further in view of Otomaru et al. (Pub. No.: US 2022/0230346 A1), hereinafter Otomaru.
Regarding claim 1, Ma discloses an image processing apparatus (Paragraph 15 teaches that the invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor.) comprising:
At least one processor (Paragraph 38 teaches that as shown in FIG. 1, terminal 100 may comprise one or more processors 102 (indicated by 102a, 102b, . . . , 102n in the drawing).);
wherein the processor receives a normal medical image in which a lesion has not occurred in a target organ (Paragraph 83 teaches that at 410, a first medical image is obtained. In some embodiments, the obtaining the first medical image includes receiving the first medical image via an upload by a user and paragraph 85 teaches that in some embodiments, the first medical image comprises a representation of a target organ. The target organ may be identified by a computer and/or an indication of the target organ may be provided by a user (e.g., via a user interface provided on a terminal). As an example, the target organ is an organ in the body of a patient (e.g., a human, an animal, a living being, etc.). Examples of a target organ include a brain, a heart, a lung, etc. Additionally, paragraph 56 teaches that according to various embodiments, the classification results (e.g., obtained from processing the first medical image) comprise a set of one or more classifications. The set of one or more classifications may be configured by a user (e.g., the user that uploads the medical image), an administrator, etc. In some embodiments, the set of one or more classifications is preset based at least in part on a mapping of a set of one or more classifications to a type of medical image, a type of target organ, or a type of analysis/diagnosis that is desired (which may be selected by the user via an input to a user interface). ... As an example, in the case of a medical image of a lung, the classification results may include, but are not limited to: diseased lung image, normal lung image, etc. The classification results (e.g., the set of one or more classifications associated with a particular target organ) can be according to actual need, preferences, etc.),
receives relationship information indicating a difference in size of regions in the target organ (Paragraph 30 teaches that according to various embodiments, a medical image is processed in connection with providing information that classifies the medical image. The medical image may be processed to obtain target data such as information pertaining to a particular region of the medical image and paragraph 68 teaches that in some embodiments, quantitative analysis is performed with respect to the target region. Results of the quantitative analysis may be provided to a physician or care provider. For example, the results may be provided via a user interface (e.g., displayed on a display module). In some embodiments, the quantitative analysis includes or is provided with corresponding recommendations for adopting a corresponding treatment regimen. The aforementioned first proportion may be the size of the target region as a proportion of the target organ (e.g., a ratio of the size of the target region to the size of the target organ). Various other proportions may be determined based on the target region relative to the target organ (e.g., using one or more other characteristics such as length, volume, mass, color, etc.). Lastly, paragraph 86 teaches that in some embodiments, the first medical image corresponds to the image of a target organ obtained with a medical imaging technology. ... Various medical imaging technologies may be used for capturing medical images corresponding to different target organs.). However, Ma fails to disclose that the regions are a plurality of partial regions.
Narukiyo discloses that the regions are a plurality of partial regions (Paragraph 110 teaches that the processing circuitry 150 determines a plurality of correction parameters in accordance with a plurality of partial regions forming the first region by the correction parameter determination function 150f and paragraph 115 teaches that at Step S125, the processing circuitry 150 determines the correction parameters in accordance with the partial regions forming the first region by the correction parameter determination function 150f.). Since Ma teaches an image processing apparatus that can receive regional target related information of a particular target organ and Narukiyo teaches an image processing apparatus that can process information in accordance to a plurality of partial regions, it would have been obvious to a person having ordinary skill in the art to combine the features together so that any information related to a particular region of a target organ could be further processed to receive more specific information related to partial regions of that main region.
Therefore, it would have been obvious to one of ordinary skill in the art before the effecting filing date of the claimed invention to have modified Ma to incorporate the teachings of Narukiyo, so that the combined features together would allow for more accurate and detailed processing information related to specific partial regions of a targeted organ.
Furthermore, Ma in view of Narukiyo disclose and generates an output medical image in which an indirect finding associated with occurrence of a lesion exists in the target organ based on the relationship information (FIG. 4 and paragraph 75 of Narukiyo teach that as an example, as illustrated in FIG. 4 and FIG. 6, the processing circuitry 150 performs the processing at Step S140 on the input image 1 and the corrected image 4 generated at Step S130 by the second inference function 150d, performs the inference about the second region in the input image 1 based on the learned model, and generates the mask image 5, for example, as an inference result. Additionally, paragraph 44 of Ma teaches that various embodiments provide a method for processing a medical image. In some embodiments, a medical image is processed in connection with performing a quantitative analysis on the medical image. For example, the medical image may be processed to determine one or more target regions (e.g., a lesion region and/or an organ region) and paragraph 77 of Ma teaches that in some embodiments, the method for processing the medical image includes, after obtaining a second medical image including the target organ, using a third machine learning model to process the first medical image and the second medical image and to obtain the deformation relationship between the first medical image and the second medical image. ... In some embodiments, the processing of the medical image includes processing the second medical image based at least in part on the deformation relationship to obtain a registered image corresponding to the second medical image.). However, Ma in view of Narukiyo fail to disclose changing a contour of a plurality of patrial regions of the target organ in the normal medical image.
Otomaru discloses changing a contour of a plurality of patrial regions of the target organ in the normal medical image (Paragraph 82 teaches that in step S304, the learned model obtaining unit 43 performs learning data augmentation processing (data augmentation) in the normalized space coordinate-transformed in step S303. The learned model obtaining unit 43 functions as a deformation unit, and generates a deformed image and a deformed contour by adding deformations (variation values) to the normalized image and the normalized contour in a state in which the positions of the feature points in the normalized space are fixed to the same positions. The learned model obtaining unit 43 then obtains a learned model based on the deformed image and the deformed contour. Based on learning data (augmented learning data) generated by adding the deformed image and the deformed contour to the learning data, the learned model obtaining unit 43 obtains the learned model.). Since Ma in view of Narukiyo teach an image processing apparatus that can receive normal medical images without lesions and analyze deformation (changing) relationship information between a plurality of target regions of a target organ and Otomaru is an imaging processing apparatus that has the functionality to deform and change the contours of different areas withing a medical image, it would have been obvious to a person having ordinary skill in the art to combine the features together so that together, a normal medical image consisting of a target organ and/or region(s) could then be deformed and adjusted based off of relationship information associated with a possible occurrence of a legion within said target organ and/or region(s).
Therefore, it would have been obvious to one of ordinary skill in the art before the effecting filing date of the claimed invention to have modified Ma in view of Narukiyo to incorporate the teachings of Otomaru, so that the combined features together would help improve the overall accuracy and recognition of lesion detections within medical images, containing a target organ, for machine learning by providing the capabilities of generating more images of deformed (contour changed) organ regions for training purposes.
Regarding claim 2, Ma in view of Narukiyo and Otomaru disclose everything claimed as applied above (see claim 1), in addition, Ma in view of Narukiyo and Otomaru disclose wherein the relationship information is a numerical value indicating the difference in size of the plurality of partial regions (Paragraph 83 of Narukiyo teaches that at Step S140, the processing circuitry 150 may generate a likelihood image in which each pixel shows a value representing a probability of being the pancreatic cancer region as the mask image 5 by the second inference function 150d. In this case, each element of the mask image 5 will have a continuous value of 0 or more and 1 or less. Embodiments are not limited to this example, and each element of the mask image 5 may be other than a value of 0 or more and 1 or less. Additionally, paragraph 99 of Narukiyo teaches that as another example, when the processing circuitry 150 determines the correction parameter by the correction parameter determination function 150f, the processing circuitry 150 determines the value of the correction parameter in accordance with the size of the first region inferred at Step S120 by the correction parameter determination function 150f.).
Regarding claim 5, Ma in view of Narukiyo and Otomaru discloses everything claimed as applied above (see claim 1), in addition, Ma in view of Narukiyo and Otomaru disclose wherein the target organ is a pancreas (Paragraph 43 of Narukiyo teaches that when extracting a certain region from a medical image by machine learning or the like, two-step extraction may be performed. There is a method that infers a region related to an object of interest in a first stage of extraction and then infers a target region with high accuracy in a second stage of extraction, for example. When inferring a pancreatic cancer region, for example, a pancreas region may be inferred from an image in the first stage of extraction, and a pancreatic cancer region may be inferred in the second stage of extraction.),
And the plurality of partial regions include a head part, a body part, and a tail part (Paragraph 119 of Narukiyo teaches that the processing circuitry 150 determines a value obtained by multiplying a variance value indicating the accuracy of the inference about the second region at Step S140 of each of the pancreas head region, the pancreas body region, and the pancreas tail region by the constant blurring amount σ as the correction parameter for each partial region by the correction parameter determination function 150f.).
Regarding claim 6, the method steps correspond to and are rejected similarly to the apparatus steps of claim 1.
Regarding claim 7, the non-transitory computer-readable storage medium corresponds to and is rejected similarly to the apparatus of claim 1.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Narukiyo and Otomaru as applied to claim 1 above, and further in view of Douglas et al. (U.S. Patent: #10,956,635 B1), hereinafter Douglas.
Regarding claim 3, Ma in view of Narukiyo and Otomaru discloses everything claimed as applied above (see claim 1), in addition, Ma in view of Narukiyo and Otomaru discloses wherein the relationship information is data being created to satisfy the difference in size of the plurality of partial regions (Paragraph 101 of Narukiyo teaches that as an example, for the same size of the input image 1, a smaller volume of the pancreas, which is the size of the first region, gives a smaller total number of pixels in the pancreas region and a greater influence of pixel loss in the pancreas region. Thus, the processing circuitry 150 determines the correction parameter in such a manner that a smaller volume of the pancreas gives a larger blurring amount σ′ of the Gaussian filter by the correction parameter determination function 150f. Additionally, paragraph 104 of Narukiyo teaches that as another example, the processing circuitry 150 may determine the value of the blurring amount σ based on the upper limit of the size of region loss in the first region image 3, which is the estimated pancreas mask (the value considered to cover the region loss), by the correction parameter determination function 150f. The size of the region loss can be calculated by comparing the correct answer image 2, which is the actual region boundary, and the first region image 3 with each other, for example.). However, Ma in view of Narukiyo fail to disclose wherein the relationship information is data in which a discriminable value is defined for each of a region of the target organ and the plurality of partial regions in the medical image.
Douglas discloses wherein the relationship information is data in which a discriminable value is defined for each of a region of the target organ and the plurality of partial regions in the normal medical image (FIG. 26, Steps 2606-2610 and Col. 29 Lines 8-16 teach that step 2606 is to perform a discrimination process to determine if generated volume appears real or appears fake (e.g., examine all 2D slices in dataset via axial analysis, sagittal analysis, coronal analysis). Step 2610 is if the discrimination process determines that dataset appears fake to then modify voxel(s). Then, step 2606 is repeated. Step 2608 is to complete the dataset if the discrimination process determines that the generated volume appears real.). Since Ma in view of Narukiyo and Otomaru teach an image processing apparatus that can account for informational data related to a region of a target organ and a difference in size of a plurality of partial regions and Douglas teaches a method of a discrimination process to help determine if data appears to be fake or not, it would have been obvious to a person having ordinary skill in the art to combine the features together so that any relationship information related to the regions around the targeted organ could then be used in a discrimination process for helping to determine whether or not a particular medical image is real or fake.
Therefore, it would have been obvious to one of ordinary skill in the art before the effecting filing date of the claimed invention to have modified Ma in view of Narukiyo and Otomaru to incorporate the teachings of Douglas, so that the combined features together would allow for more accurate and defined relationship information data to be used for helping in determining whether the generated medical image was real or fake.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Narukiyo and Otomaru as applied to claim 1 above, and further in view of Lenga (Pub. No.: US 2025/0182273 A1).
Regarding claim 4, Ma in view of Narukiyo and Otomaru disclose everything claimed as applied above (see claim 1), in addition, Ma in view of Narukiyo and Otomaru disclose wherein the target organ is a pancreas (Paragraph 43 of Narukiyo teaches that when extracting a certain region from a medical image by machine learning or the like, two-step extraction may be performed. There is a method that infers a region related to an object of interest in a first stage of extraction and then infers a target region with high accuracy in a second stage of extraction, for example. When inferring a pancreatic cancer region, for example, a pancreas region may be inferred from an image in the first stage of extraction, and a pancreatic cancer region may be inferred in the second stage of extraction.). However, Ma in view of Narukiyo and Otomaru fail to disclose and the plurality of partial regions include a pancreatic duct and a pancreatic parenchyma.
Lenga discloses and the plurality of partial regions include a pancreatic duct and a pancreatic parenchyma (Paragraph 151 teaches that besides cystic lesions within the main pancreatic duct, cystic lesions in other regions of the pancreas may also be detected and characterized and paragraph 156 teaches that the identification and classification of possible cysts, lesions or cystic lesions in the pancreas and/or along the centerline of the pancreas provides important information about a number of different parameters, including, but not limited to, the number of lesions in the pancreas, their location within the tissue, including their location in and within a pancreatic subregion (e.g. coordinates, size).). Since Ma in view of Narukiyo and Otomaru teach an image processing apparatus that can process lesion information related to a plurality of partial regions of a targeted organ (such as a pancreas) and Lenga teaches a function for detecting lesions related to a pancreas including the main pancreatic duct and other surrounding regions and tissues of the pancreas (such as the pancreatic parenchyma), it would have been obvious to a person having ordinary skill in the art to combine the features together so that any processed information related to a plurality of partial regions around the pancreas, could also then include information related to the pancreatic duct and/or the pancreatic parenchyma.
Therefore, it would have been obvious to one of ordinary skill in the art before the effecting filing date of the claimed invention to have modified Ma in view of Narukiyo and Otomaru to incorporate the teachings of Lenga, so that the combined features together would allow for more detailed and accurate lesion information associated with partial regions around the pancreas, including specifically, lesion information related to the pancreatic duct and/or the pancreatic parenchyma.
Response to Arguments
Applicant’s arguments with respect to independent claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The prior art of Otomaru has been incorporated into the rejection of independent claims 1, 6 and 7 and therefore teaches the newly amended claim language, specifically in regards to “changing a contour of a plurality of patrial regions of the target organ in the normal medical image” (see claim 1 above).
Furthermore, in response to applicant's argument that Ma fails to disclose or suggest relationship information that explicitly represents relative size differences among multiple partial regions of the target organ and using such relationship information as a basis for modifying a normal medical image in which a lesion has not occurred, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. In this case, paragraphs 56, 68, 83 and 85 of Ma appear to be capable of performing the intended use of the claim (see claim 1 above) due to Ma having the capabilities to perform a quantitative analysis that can compare and acquire a ratio of the size of a target region to the size of the target organ and other proportions and characteristics, such as the length, volume, mass, etc. which all relate to size relationship information between a target region and a target organ (Paragraph 68). Additionally, Ma teaches that a user can obtain a first medical image (Paragraph 83) which can consist of classifications that may consist of a normal lung image (Paragraph 56), thus teaching that a normal medical image without lesions can be received and used, if necessary.
Furthermore, in response to applicant’s arguments that Narukiyo does not disclose or suggest that the correction parameters represent relative size relationships among the partial regions or a target organ, or that such correction parameters are used to change a contour of the partial regions in order to generate an output medical image, when viewed together in combination with the other prior arts of Ma and Otomaru, it appears that Narukiyo teaches the functionality of a processor having the capabilities to determine parameters that relate to a plurality of partial regions within a first region (target region) and thus when viewed in combination with Ma and Otomaru, provides the capabilities of being able to utilize the data related to the partial regions of a target region, while Ma teaches size relationships of the different regions and outputting a registered image and Otomaru teaches providing contour changes to the different regions (see claim 1 above).
In regards to any additional arguments regarding the dependent claims 2-5, for the virtue of their dependency are moot because the independent claim(s) are not allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yao et al. (Pub. No.: US 2022/0245810 A1) teaches a method and device for constructing data sets to use for enhanced medical images and train multi-task prediction models.
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 George Renze whose telephone number is (703)756-5811. The examiner can normally be reached Monday-Friday 9:00am - 6:00pm EST.
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, Xiao Wu can be reached at (571) 272-7761. 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.
/G.R./Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
Prosecution Insights