Detailed Action
1. Claims 1-11 are pending in this Application.
Notice of Pre-AIA or AIA Status
2. 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 Election /Restriction
3. Applicant’s election without traverse of Group 1 claims 1-11 in the reply filed on 05/13/2026 is acknowledged.
4. Claims 12-14 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Claim Rejections - 35 USC § 102
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 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 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 -
(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.
5 Claims 1-4, 7-8 and 10-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by OSAWA SATORU ( hereafter OSAWA ), JP 2006325937 A, pub. 06/12/2006.
As to claim 1, OSAWA teaches medical image in which at least one partial region as an estimation target among a plurality of partial regions in an anatomical region included in a medical image is estimated based on at least one partial region other than the estimation target(Claim 1, [0011], Classification means: Subdivides the medical image into small subregions and groups them by similar anatomical features. Artificial image generation means: Creates normal artificial versions of the classified subregions. Candidate region detection means: Scans the medical image to spot potential abnormal shadows. Region setting means: Defines a "region of interest" where the candidate shadow is located and a "neighboring region" close by. Determination means: Compares the difference between original and artificial images in the region of interest against the neighboring region to confirm if an abnormality exists)
As to claim 2, OSAWA teaches the processor divides the anatomical region into the plurality of partial regions (Claim 1, [0011], Classification means: Subdivides the medical image into small subregions and groups them by similar anatomical features).
As to claim 3, OSAWA teaches the processor performs control of displaying the partial region as the estimation target in the estimated medical image and a region corresponding to the partial region as the estimation target in the medical image in a comparable manner ([0044], First, N sample images are selected from many chest images in which the ribs are clearly visible. These chest images are then displayed, and a pointing device such as a mouse is used to select points on the ribs in each image, aligning the anterior and posterior ribs to specify n landmarks (for example, 400 points). These landmarks are then used as training data to create a model in advance.).
As to claim 4, OSAWA teaches the processor performs control of displaying information indicating a difference between the partial region as the estimation target in the estimated medical image and a region corresponding to the partial region as the estimation target in the medical image (Figs.18(a) and 18(b), [0092]-[0094], Figure 18(a) (Category 4-1) shows an example of an artificial image of normal structure generated corresponding to an image in Category 4-1 where cancer is present. When the artificial image is subtracted from the original image in which cancer is present in category 4-1, the cancerous areas appear as black. On the other hand, Figure 18(b) (Category 4-1) shows an example of an artificial image generated corresponding to a Category 4-1 image in which cancer is absent.
Since no cancer is present in Category 4-1, subtracting the artificial image from the original image results in a uniform density across the entire image).
As to claim 7, OSAWA teaches the estimated medical image is an image in which the estimated medical image generated for at least one of the plurality of partial regions is combined with the anatomical region in the medical image (Claim 1, [0011], Subdivides the medical image into small subregions using classification means and groups them by similar anatomical features, defines a "region of interest" where the candidate shadow is located and a "neighboring region" close by, and compares the difference between original and artificial images in the region of interest against the neighboring region to confirm if an abnormality exists).
As to claim 8, OSAWA teaches the processor performs a process of detecting a candidate for an abnormality in the anatomical region, and generates the estimated medical image using only a trained model corresponding to the partial region in which the detected candidate for the abnormality exists among a plurality of trained models that are respectively trained in advance for the plurality of partial regions, the trained model being used to generate the estimated medical image ([0044]-[0045], [0088]First, N sample images are selected from many chest images in which the ribs are clearly visible. These chest images are then displayed, and a pointing device such as a mouse is used to select points on the ribs in each image, aligning the anterior and posterior ribs to specify n landmarks (for example, 400 points). These landmarks are then used as training data to create a model in advance. As shown in Figure 16, a small rectangular area of 80 pixels × 80 pixels belonging to category 4-1 is extracted from a normal chest image in which no abnormal shadows are captured, and M such images (Figure (a)) are selected as sample images and used as training data. Figure 16 primarily extracts areas where one rib overlaps, but principal component analysis is performed including the surrounding regions.).
Claim 10 is rejected the same as claim 1 except claim 10 is directed to a method claim. All the limitations of claim 10 are addressed in claim 1. Thus, argument analogous to that presented above for claim 1 is applicable to claim 10.
As to claim 11 OSAWA teaches A non-transitory computer-readable storage medium storing an image processing program for causing a processor of an image processing apparatus to execute (Claim 8, [0107] Furthermore, an image recognition device can be created by installing a program that performs the above-mentioned processing on a computer onto a personal computer or other computer via a CD-ROM or network.): generating an estimated medical image in which at least one partial region as an estimation target among a plurality of partial regions in an anatomical region included in a medical image is estimated based on at least one partial region other than the estimation target (all these limitation are similar to claim 1 and discussed in claim 1 above) .
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 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.
8. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over OSAWA, JP 2006325937 A, in view of HATTORI et al.,(hereafter HATTORI), US 20230124908 A1, filed on 08/30/2022.
As to claim 9, OSAWA teaches the image processing apparatus, the anatomical region the plurality of partial regions (Claim 1, [0011])
It is noted that OSAWA does not specifically teach “the anatomical region is a pancreas, and the plurality of partial regions include a head part, a body part, and a tail part.”
On the other hand HATTORI teaches the anatomical region is a pancreas, and the plurality of partial regions include a head part, a body part, and a tail part ([0067], [0104], . For example, through a matching process with anatomical landmarks in the surroundings of the pancreas region or with an anatomical model, it is possible to divide the pancreas
region into the pancreatic head part (a part enclosed by the aorta and the duodenum), a pancreatic body part (one half positioned on the aorta side when the part interposed between the aorta and the spleen is divided into two in terms of the distance), and the pancreatic tail part (the other half positioned on the spleen side when the part interposed between the aorta and the spleen is divided into two in terms of the distance).)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporating Hattori's pancreatic sub-region segmentation method into Osawa's lung and cancer identification framework.
The suggestion/motivation for doing so would have been to allow user of OSAWA to expand multi-organ diagnostic capabilities, enabling comprehensive abdominal and oncological analysis and better staging of complex metastatic cancers.
8. Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over OSAWA, JP 2006325937 A, in view of JP 4544745 B2, pub 09/15/2010.
As to claim 8 OSAWA teaches indicating a difference between the partial region as the estimation target in the estimated medical image and the region corresponding to the partial region as the estimation target in the medical image is equal to or greater than a threshold value ([0011],determination means that compares the difference between the artificial image of the region of interest and the original image of the region of interest with the difference between the artificial image of the neighboring region and the original image of the neighboring region, and determines whether or not the candidate region included in the region of interest has abnormal shading based on the presence or absence of a difference between the two differences.),
it is noted that OSAWA does not specifically teach the underline section of the limitation “wherein the processor performs the control in a case in which a value indicating a difference between the partial region as the estimation target in the estimated medical image and the region corresponding to the partial region as the estimation target in the medica image is equal to or greater than a threshold value”
On the other hand JP 4544745 B2 teaches in case in which a value indicating a difference between the partial region as the estimation target in the estimated medical image and the region corresponding to the partial region as the estimation target in the medical image is equal to or greater than a threshold value([006], [0010], The area of the object inside each of the frames is then calculated based on the area of the position that has parameters that fall within an acceptable range. The areas of two adjacent frames are then compared to determine if the difference between the two areas exceeds a predetermined amount. If so, the area of one of the adjacent frames is recalculated using a different criterion).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporating a method of comparing the areas
of two adjacent frames taught by JP 4544745 B2 into Osawa's lung and cancer identification framework
The suggestion/motivation for doing so would have been to allow user of OSAWA to automate tumor growth and boundary change detection across sequential medical scans. By tracking area shifts between adjacent image frames against a set threshold, Osawa's system gains quantitative precision to flag malignant progression or treatment.
As to claim 6 OSAWA teaches the processor generates the estimated medical image for each of the plurality of partial regions ([0011] determination means that compares the difference between the artificial image of the region of interest and the original image of the region of interest with the difference between the artificial image of the neighboring region and the original image of the neighboring region, and determines whether or not the candidate region included in the region of interest has abnormal shading based on the presence or absence of a difference between the two differences).
It is noted that OSAWA does not specifically teach “performs the control in a case in which a value indicating the difference for at least one estimated medical image is equal to or greater than the threshold value”.
On the other hand JP 4544745 B2 teaches performs the control in a case in which a value indicating the difference for at least one estimated medical image is equal to or greater than the threshold value (([006], [0010] this limitation discussed in claim 5 above).
Prior art not used in rejections but pertinent to the claims or disclosure
“ABNORMAL SHADOW IMAGE DETECTION METHOD AND DEVICE THEREOF”
JP 2002158923 A, pub. 05/30/2002, to OSAWA SATORU, disclosed:
An automatically detect an approximately circular potential abnormal shadow such as an incipient lung cancer which is easy to overlook from a radiation image. SOLUTION: An elapsed subtraction image Psu is generated from a first image P1 and a second image P2. An approximately circular region is detected from the elapsed subtraction image Psu as a potential abnormal shadow. As a potential abnormal shadow such as an incipient lung cancer which is easy to overlook is emphasized in the elapsed subtraction image Psu, the approximately circular potential abnormal shadow can be detected with a high precision (see Abstract and [0013])
Contact Information
Any inquiry concerning this communication or earlier communication from the examiner should be directed to Mekonen Bekele whose telephone number is (469) 295-9077.The examiner can normally be reached on Monday-Friday from 9:00AM to 6:50 PM Eastern Time.
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/MEKONEN T BEKELE/Primary Examiner, Art Unit 2699
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