DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Group II (claims 9-12 and 15) in the reply filed on is acknowledged. Claims 1-2, 13, 14 and 16 are generic to all groups.
Claim Rejections - 35 USC § 101
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.
Claim 9 describe(s) a machine learning model computer program. While the claim language discloses that the model causes a computer to execute instructions, said language fails to disclose that said apparatus includes one or more hardware components and is not merely software. Thus, the claims read on computer listings per se. Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical "things." They are neither computer components nor statutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program's functionality to be realized. In contrast, a claimed non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program's functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
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.
Claim(s) 1-2, 9-11 and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morgas (US 20210304402 A1; provided by Applicant)
Regarding claim 1, Morgas discloses a learning data set generation method causing a computer to execute: (Morgas teaches a system for generating synthetic medical training images for a deep neural network to provide segmentation and labelling. Images of an initial imaging modality are converted to generate an augmented training database, see Abstract and ¶ 0029, 0079 and 0137.)
acquiring second medical imaging data generated by image conversion processing on first medical imaging data; and (As above, images of an initial imaging modality are converted to generate an augmented training database, see Abstract and ¶ 0029 and 0091.)
generating a pair of the second medical imaging data and a first ground truth label as a learning data set, the first ground truth label being a ground truth label for the first medical imaging data. (¶ 0029, 0079 and 0137 teach that the converted pseudo images contain the same ground truths as the original training images, in order to preserve the annotated ground truth labels from the original modality for input as pairs into the learning network.)
Morgas does not expressly disclose that all of its above-cited teachings on training image conversion are expressly disclosed as occurring in the same embodiment. That is, despite the reference being clear that these functions are disclosed, there is no express disclosure that the details are all found in the same embodiment. Instead, the reference presents some of the individual detailed disclosures as ‘according to some embodiments.’ It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the various teachings to provide a single system capable of the variety of tasks which are disclosed. In view of these teachings, this cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 2, the above combination discloses the learning data set generation method according to claim 1, wherein the image conversion processing is executed by an image conversion model, the image conversion model being a model on which machine learning has been performed. (¶ 0134-0135 teach using neural network learning models for the pseudo image generation.)
Regarding claim 9, the above combination discloses a machine learning model causing a computer to execute: (See rejection of claim 1)
acquiring medical imaging data; and (See ¶ 0092 and 0127 for acquiring medical images for the inference phase.)
outputting an inference result with respect to the medical imaging data, wherein machine learning using a learning data set formed of a pair of second medical imaging data and a first ground truth label has been performed on the machine learning model, the second medical imaging data generated by image conversion processing on first medical imaging data, the first ground truth label being a ground truth label for the first medical imaging data. (As above, see ¶ 0092 and 0127 for acquiring medical images for the inference phase after training. As above, ¶ 0029, 0079 and 0137 teach that the converted pseudo images contain the same ground truths as the original training images, in order to preserve the annotated ground truth labels from the original modality for input as pairs into the learning network.)
Regarding claim 10, the above combination discloses the image processing device, comprising an inference section that outputs a first inference result obtained by inputting third medical imaging data into the machine learning model according to claim 9. (As above, see ¶ 0092 and 0127 for acquiring medical images for the inference phase after training and outputting an inference result.)
Regarding claim 11, the above combination discloses the image processing device according to claim 10, further comprising an image generation section that generates the third medical imaging data. (See ¶ 0127-0128 for acquiring the medical image data for inference.)
Regarding claim 13, the above combination discloses a learning data set generation device, comprising: an image data acquisition section that acquires second medical imaging data generated by image conversion processing on first medical imaging data; (See rejection of claims 1 and 9) and a generation section that generates a pair of the second medical imaging data and a first ground truth label as a learning data set, the first ground truth label being a ground truth label for the first medical imaging data. (See rejection of claims 1 and 9)
Regarding claim 14, the above combination discloses a machine learning device, comprising: (See rejection of claims 1 and 9) a learning data set acquisition section that acquires a learning data set formed of a pair of second medical imaging data and a first ground truth label, the second medical imaging data generated by image conversion processing on first medical imaging data, the first ground truth label being a ground truth label for the first medical imaging data; (See rejection of claims 1 and 9) and a learning section that performs machine learning on a machine learning model by using the learning data set. (See rejection of claims 1 and 9)
Regarding claim 15, the above combination discloses an image diagnosis system, comprising: (See rejection of claims 1 and 9) a learning data set generation device that generates a learning data set formed of a pair of second medical imaging data and a first ground truth label, the second medical imaging data generated by image conversion processing on first medical imaging data, the first ground truth label being a ground truth label for the first medical imaging data; (See rejection of claims 1 and 9) a machine learning device that performs machine learning on a machine learning model by using the learning data set; (See rejection of claims 1 and 9) and an image diagnosis device that outputs an inference result obtained by inputting third medical imaging data into the machine learning model on which the machine learning has been performed. (See rejection of claims 1, 9 and 10)
Regarding claim 16, the above combination discloses anon-transitory computer-readable recording medium storing therein a program to be executed by a computer, the program causing a computer to implement: (See rejection of claims 1 and 9 and ¶ 0037) an acquisition function of acquiring second medical imaging data generated by image conversion processing on first medical imaging data; (See rejection of claims 1 and 9) and a generation function of generating, as a learning data set, a pair of the second medical imaging data and a first ground truth label, the first ground truth label being a ground truth label for the first medical imaging data. (See rejection of claims 1 and 9)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morgas (US 20210304402 A1; provided by Applicant) in view of Fujito (JP-2019525786-A; translation provided).
Regarding claim 12, the above combination discloses the image processing device according to claim 11, wherein the image generation section generates ultrasound image data, (See ¶ 0127-0128 for acquiring the medical image data for inference which is ultrasound imaging.)
In the field of synthetic image generation Fujito teaches ultrasound image data generated based on a reflected ultrasound wave with respect to an ultrasound wave transmitted to a subject. (Fujito pg. 5, ¶ 2 teaches using an ultrasound wave probe and digitization for image generation. Fujito also teaches image conversion for synthetic image generation to provide training images.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Morgas’s synthetic image generation with Fujito’s synthetic image generation. Morgas teaches using ultrasound imaging for inference but Fujito expressly teaches generating an image based on an ultrasound wave probe. Fujito also teaches image conversion for synthetic image generation to provide training images. This cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
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