DETAILED ACTION
Notice of AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Request for Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17© has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 04/13/2026 has been entered.
Response to Arguments
3. Applicant’s remarks received on 04/13/2026 with respect to the amended independent claims have been acknowledged and are moot in view of a new ground of rejected necessitated by the corresponding amendment. Currently claims 1-22 are rejected.
Response to Amendment
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding claims 1-22 under the broadest reasonable interpretation, the terms of the claims are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skilled in the art. See MPEP 2111.
The claimed invention generates mask image of an input image based on first inference of an organ on the input image, determine a correction parameter, smooths the boundary of the mask image, and perform second inference to obtain tumor area of the organ by analyzing the input image and the mask image with smoothed boundary.
The claims are directed mental process and do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
The claims do not have any limitations that are indicative of integration of the judicial exception into a practical application such as improvements to functioning of a computer or a technical field, using any particular machine, effect a transformation of a particular article to a different state or thing, or apply the judicial exception in any meaningful way beyond generally linking the use to a particular technological environment. Therefore, the claims as a whole do not amount to significantly more than the judicial exception.
A patent may be obtained for “any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof," 35 U.S.C. § 101, but “laws of nature, natural phenomena, and abstract ideas are not patentable.”
Step 1
Claims 1-22 are directed to one of the four statutory categories of eligible subject matter (process): thus, the claim pass Step 1 of the Subject Matter Eligibility Test.
Step 2A, prong 1 analysis
Claims 1-22 direct to a judicial exception in terms of mental processes which can be performed by human being with aids of simple tools like pen, ruler and paper or a computer.
In claims 1-22, a trained professional receives an input organ image and a mask image with sharp boundary on the input image indicating an organ, he/she re-draw the boundary of the organ by smoothing it out. Now looking at both the input image and the mask image with the corrected boundary, the professional is able to identify a tumor associated with the organ on the input image. The inference process can be performed by observation and aids of a pencil to draw smooth boundary around the organ.
Step 2A, prong 2 analysis
Other than retrieving information, defining information, or classify information, the claims do not have any limitations that are indicative of integration of the judicial exception into a practical application such as improvements to functioning of a computer or a technical field, using any particular machine, effect a transformation of a particular article to a different state or thing, or apply the judicial exception in any meaningful way beyond generally linking the use to a particular technological environment.
Step 2B
Further, the claims do not include other additional elements that are beyond what is well-understood, routine, conventional activities in the field and sufficient to amount to significantly more than the judicial exception.
Conclusion:
The claims do not include additional elements amount to significantly more in terms of improving functionalities of a computer/device itself, improving another technology or technical field, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine claim to a particular useful application or by use of a particular machine that is unconventional. In conclusion, the claims 1-11 and 15-25 do not comply with the current standards for patent eligible subject matter under 35 USC § 101.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claims 1-20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al (Deep Pancreas Segmentation with Uncertain Regions of Shadowed Sets, May 2020) and in further view of Wang et al (Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images, October 2021) and Yuan et al (Automatic liver and tumor segmentation based on deep learning and globally optimized refinement, 2021).
Regarding claim 1 (currently amended), Zheng et al teaches: An image processing apparatus comprising processing circuitry configured to acquire an input image, infer a first region image about a first region included in the input image, generate a corrected mask image in which the first portion is corrected using the acquired mask image by executing correction processing, and perform inference about a second region included in the input image based on the input image and the corrected image [page 46: 2, 2.1, 2.2, page 47: 2.3, fig. 1 (Probability map shows the first region image corrected through re-weighting for boundaries of pancreas. And the uncertain region is the second region estimated based on the first corrected region image.)].
Zheng et al does not takes the input image and the corrected mask as inputs to a learned model for a second portion. In the same field of endeavor, Wang et al teaches: acquire a mask image, which indicates a first portion in the input image, wherein the mask image is generated from a first inference using a medical imaging result of a subject [page 2: DBAC (P attention map/soft mask); page 3: p04 (feature map Q)], generate a corrected mask image, in which the first portion is corrected using the acquired mask image by executing correction processing [page 3: p03, p04], and perform a second inference to obtain a second portion in the input image by inputting the input image and the corrected acquired mask image to a learned model [page 4: p01 (Concatenated input comprises input image and corrected mask Q.); figs. 3 and 4].
Therefore, given Wang et al’s prescription, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of the two to apply the corrected mask to further refine a region from an input image as suggested by Wang et al’s cascade approach to Zheng et al’s setting for precise segmentation.
Zheng et al in view of Wang et al does not explicitly disclose reducing sharpness of a boundary of the first region. In the same field of endeavor, Yuan et al teaches: acquire a mask image, which indicates a first portion in the input image [page 306: p02]; generate a corrected mask image by applying correction processing to reduce sharpness of a boundary of the first portion in the acquired mask image [page 311: 2.4 (get more smooth, precise and connective segmentation result…)]. Therefore, given Yuan et al’s prescription on two stage liver mask and tumor inference generation by applying correction processing to smooth boundary of masked liver image, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to generate corrected mask image by reducing sharpness of boundary and use processed mask for tumor region of interest restriction with narrowed search range and improved accuracy.
Regarding claim 2 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al and Wang et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to generate the corrected mask image with a shape or a distribution of the first portion corrected using the acquired mask image [Zheng: fig. 1 (Probability map shows a shape of the first region corrected.); Wang: page 2: B].
Regarding claim 3 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the correction processing executed by the processing circuitry is processing to mitigate an influence of an inference error of the mask image on the second inference about the second portion [page 47: 2.3 (weights for each pixel are corrected.)].
Regarding claim 4 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the correction processing executed by the processing circuitry is processing to increase an ambiguity of a shape of the first portion in the acquired mask image or an ambiguity of a distribution of the first portion in the acquired mask image [page 46: p3 (U-net involves a combination of down sampling to shrink an image to clarify a boundary and up sampling to re-enlarge the image, which leads to blurred boundaries.)].
Regarding claim 5 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the correction processing executed by the processing circuitry is processing to smooth the acquired mask image [page 49: p03 (Smoothing boundaries of pancreas)].
Regarding claim 6 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to: acquire a correction parameter for use in the correction processing and generate the corrected image using the correction parameter [page 46: 2 (ground truth labeling)].
Regarding claim 7 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to: determine a correction parameter for use in the correction processing and generate the corrected mask image using the correction parameter [page 47: 2.3, 2.4 (weights)].
Regarding claim 8 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 7, wherein the correction parameter is a value associated with the learned model for use in the second inference about the second portion [page 47: 2.3, 2.4].
Regarding claim 9 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to perform learning to generate a first learned model for use in the first inference about the first portion [page 46: 2.1].
Regarding claim 10 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 7, wherein the processing circuitry is further configured to determine the correction parameter based on a number of pixels forming the first portion [page 46: p3, p7].
Regarding claim 11 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 7, wherein the processing circuitry is further configured to determine a respective plurality of the correction parameters in accordance with a plurality of partial portions forming the first portion [page 46: p3 (Confident and uncertain regions are forming the first region.)].
Regarding claim 12 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 11, wherein the processing circuitry is further configured to determine the respective plurality of correction parameters for the plurality of partial portions in accordance with a prior probability of accuracy of the second inference about the second portion [fig. 1: Weights are correction parameters.].
Regarding claim 13 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing device according to claim 1, wherein the correction processing executed by the processing circuitry is processing to expand the first portion in the acquired mask image [page 47: 2.3 (Defining uncertain boundary expands the first region.)].
Regarding claim 14 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the first portion is an organ and the second portion is a tumor or lesion [fig. 5].
Regarding claim 15 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to infer a second portion image, which is an image of the second portion [page 47: 2.3, fig. 3 (The uncertain/second region is further inferred based on the probability maps of pancreas.)].
Regarding claim 16 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to infer a presence or an absence of the second portion [page 47: 2.3 (Defining uncertain region’s presence or absence)].
Regarding claim 17 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Zheng et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is configured to perform a first correction to clarify a boundary of the first portion and then performs second correction to blur the boundary of the first portion to generate the corrected image [page 46: p3 (U-net involves a combination of down sampling to shrink an image to clarify a boundary and up sampling to re-enlarge the image, which leads to blurred boundaries.)].
Claim 18 (currently amended) has been analyzed and rejected with regard to claim 1.
Claim 19 (currently amended) has been analyzed and rejected with regard to claim 1 and in accordance with Zheng et al’s further teaching on: A non-transitory computer readable medium storing instructions that cause a computer to execute a procedure comprising [page 46: 2.1 (Neural network is stored and executed on a computer.)].
Regarding claim 20 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Wang et al further teaches: The image processing apparatus according to claim 1 wherein the acquired mask image is acquired based on the first inference about the first portion on the input image as the medical imaging result of a subject [page 2: DBAC (attention map), page 3: DDMAC (feature map), page 7: conclusion].
Regarding claim 22 (New), the rationale applied to the rejection of claim 1 has been incorporated herein. Yuan et al further teaches: The image processing apparatus according to claim 1, wherein the processing circuitry is further configured to generate the corrected mask by applying the correction processing to (a) increase an ambiguity of a shape of the first portion in the mask image, (b) increase an ambiguity of distribution of the first portion in the mask image, (c) smooth the mask image, or (d) blur the boundary of the first portion in the mask image [page 311: 2.4].
7. Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al (Deep Pancreas Segmentation with Uncertain Regions of Shadowed Sets, May 2020), Wang et al (Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images, October 2021), and Yuan et al (Automatic liver and tumor segmentation based on deep learning and globally optimized refinement, 2021); and in further view of Han et al (Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography, April, 2021).
Regarding claim 21 (previously presented), the rationale applied to the rejection of claim 1 has been incorporated herein. Yuan et al further teaches smoothing mask image with first portion is liver and the second portion is tumor and smooth the first portion of mask image to narrow down search of tumor of [page 310: p01]. For a redundant teaching in the same field of endeavor, Han et al teaches: The image processing apparatus according to claim 1, wherein the correction processing executed by the processing circuitry is processing to (a) increase an ambiguity of a shape of the first portion in the mask image. (b) increase an ambiguity of distribution of the first portion in the mask image, or (c) smooth the mask image [page 10: p03], wherein the first portion is an organ of the subject and the second portion is a tumor or lesion [page 7: fig. 2], and wherein the corrected first portion in the corrected mask image is used as information to narrow down a search range of the second portion to be inferred [page 10: 3.4]. Therefore, it would have been obvious for an ordinary skilled in the art before the effective filing date of the claimed invention to combine the teaching of all to smooth a mask to obtain a ROI for more accurate and narrowing inference.
Contact
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAN ZHANG whose telephone number is (571)270-3751. The examiner can normally be reached on Mon-Fri 9:00-5:00.
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/Fan Zhang/
Patent Examiner, Art Unit 2682
Prosecution Insights