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 .
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.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a medical data processing method comprising: outputting second spectral data by inputting first spectral data related to an examined subject imaged by a spectral medical imaging apparatus to a trained model configured to generate, on a basis of the first spectral data, the second spectral data having less noise than the first spectral data and a higher resolution than the first spectral data, wherein the first spectral data corresponds to medical data obtained by performing a spectral scan on the examined subject, and the trained model is configured to perform a noise reducing process and a super-resolution process on the first spectral data. This judicial exception is not integrated into a practical application because the steps as claimed to provide an output of second spectral data using the first spectral data are mental process. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because it represents an abstract idea without practical application.
Claim 2 recites “wherein the first spectral data is first pre-reconstruction data before being reconstructed that is acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, the second spectral data is second pre-reconstruction data before being reconstructed, and a medical image is generated on a basis of the second pre-reconstruction data before being reconstructed”. This claim merely limits the selection of input data, that does not add significantly more to the abstract idea.
Claim 3 recites “ wherein the first pre-reconstruction data is one selected from among: first projection data acquired by the spectral medical imaging apparatus at first X-ray tube voltage and second projection data acquired at second X-ray tube voltage higher than the first X-ray tube voltage; first reference projection data corresponding to each of a plurality of reference substances; and first count data corresponding to each of a plurality of energy ranges, the second pre-reconstruction data is one selected from among: third projection data corresponding to the first projection data and fourth projection data corresponding to the second projection data; second reference projection data corresponding to the first reference projection data; and second count data corresponding to the first count data, when the first projection data and the second projection data are input to the trained model, the third projection data and the fourth projection data are output, when the first reference projection data is input to the trained model, the second reference projection data is output, and when the first count data is input to the trained model, the second count data is output.” This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 4 recites “wherein the first spectral data is a first reconstructed image reconstructed on a basis of acquisition data acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, and the second spectral data is a second reconstructed image having less noise than the first reconstructed image and a higher resolution than the first reconstructed image.” This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 5 recites “the first reconstructed image is one selected from among: a plurality of first reference substance images corresponding to a plurality of reference substances; at least one first virtual monochrome X-ray image having a different X-ray energy level; a first virtual non-contrast-enhanced image; a first iodine map image; a first effective atomic number image; a first electron density image; a plurality of first energy images corresponding to a plurality of energy ranges; a first X-ray tube voltage image corresponding to first X-ray tube voltage used in the imaging process performed by the spectral medical imaging apparatus and a second X-ray tube voltage image corresponding to second X-ray tube voltage higher than the first X-ray tube voltage, the second reconstructed image is one selected from among: a plurality of second reference substance images corresponding to the plurality of first reference substance images; a second virtual monochrome X-ray image corresponding to the first virtual monochrome X-ray image; a second virtual non-contrast-enhanced image corresponding to the first virtual non-contrast-enhanced image; a second iodine map image corresponding to the first iodine map image; a second effective atomic number image corresponding to the first effective atomic number image; a second electron density image corresponding to the first electron density image; a plurality of second energy images corresponding to the plurality of first energy images; a third X-ray tube voltage image corresponding to the first X-ray tube voltage image and a fourth X-ray tube voltage image corresponding to the second X-ray tube voltage image, when the plurality of first reference substance image are input to the trained model, the plurality of second reference substance images are output, when the first virtual monochrome X-ray image is input to the trained model, the second virtual monochrome X-ray image is output, when the first virtual non-contrast-enhanced image is input to the trained model, the second virtual non-contrast-enhanced image is output, when the first iodine map image is input to the trained model, the second iodine map image is output, when the first effective atomic number image is input to the trained model, the second effective atomic number image is output, when the first electron density image is input to the trained model, the second electron density image is output, when the plurality of first energy images are input to the trained model, the plurality of second energy images is output, and when the first X-ray tube voltage image and the second X-ray tube voltage image are input to the trained model, the third X-ray tube voltage image and the fourth X-ray tube voltage image are output”. This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 6 recites “the first reconstructed image is represented by a first X-ray tube voltage image corresponding to first X-ray tube voltage used in the imaging process performed by the spectral medical imaging apparatus and a second X-ray tube voltage image corresponding to second X-ray tube voltage higher than the first X-ray tube voltage, and the second reconstructed image is represented by a third X-ray tube voltage image corresponding to the first X-ray tube voltage image and a fourth X-ray tube voltage image corresponding to the second X-ray tube voltage image.” This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 7 recites “the trained model is a model trained by using training data generated by a medical imaging apparatus that uses single energy X-rays, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus”. This claim limits the type of input data, and is considered to not adding significantly more to the abstract idea.
Claim 8 recites “ wherein the trained model is a model trained by using training data generated by a medical imaging apparatus that uses dual energy X-rays, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus”. This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 9 recites “the trained model is a model trained by using training data generated by a photon counting X-ray computed tomography apparatus, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus.” This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim 10 recites ” wherein the spectral medical imaging apparatus is a dual energy computed tomography apparatus that uses dual energy X-rays, and the trained model is trained on a basis of: a first virtual monochrome X-ray image generated on a basis of count projection data related to the examined subject imaged by a photon counting X-ray computed tomography apparatus; and a second virtual monochrome X-ray image obtained by applying, to the count projection data, a simulation process including a resolution lowering process and a noise adding process performed on the count projection data.” This claim merely limits how the input data was created and is thus considered to not adding significantly more to the abstract idea.
Claim 11 recites” wherein the first virtual monochrome X-ray image includes a plurality of first virtual monochrome X-ray images corresponding to a plurality of X-ray energy levels, the second virtual monochrome X-ray image includes a plurality of second virtual monochrome X-ray images corresponding to the plurality of X-ray energy levels, the trained model includes a plurality of trained models corresponding to the plurality of X-ray energy levels, and the plurality of trained models are trained by using the plurality of first virtual monochrome X-ray images and the plurality of second virtual monochrome X-ray images in correspondence with the plurality of X-ray energy levels, respectively.” This claim merely limits how the input data was created and is thus considered to not adding significantly more to the abstract idea.
Claim 12 recites “ A model generating method for generating a trained model configured, on a basis of first spectral data related to an examined subject imaged by a spectral medical imaging apparatus, to generate the second spectral data having less noise than the first spectral data and a higher resolution than the first spectral data, the model generating method comprising: generating second training data corresponding to noise and a resolution of the first spectral data, by adding noise to and lowering a resolution of first training data corresponding to the noise and the resolution of the second spectral data; and generating the trained model by training a convolution neural network while using the first training data and the second training data.” This judicial exception is not integrated into a practical application because the steps as claimed to train a model by providing an output of second spectral data using the first spectral data are an abstract idea which adds no non-generic to computing environment. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because it represents an abstract idea without practical application.
Claim 13 recites “ wherein the first spectral data is a first reconstructed image reconstructed on a basis of acquisition data acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, the second spectral data is a second reconstructed image having less noise than the first reconstructed image and a higher resolution than the first reconstructed image, and the model generating method comprises: generating first pre-reconstruction data before being reconstructed that corresponds to noise and a resolution of the first reconstructed image, by adding noise to and lowering a resolution of the second pre-reconstruction data before being reconstructed that corresponds to the noise and the resolution of the second reconstructed image; reconstructing a first training image on a basis of the second pre-reconstruction data; reconstructing a second training image on a basis of the first pre-reconstruction data; and generating the trained model by training a convolution neural network while using the first training image and the second training image.” This claim merely limits the selection of input data and is considered to not adding significantly more to the abstract idea.
Claim Rejections - 35 USC § 102
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.
Claims 1-18 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Matsuura et al. (US 2022/0327662 A1).
1. A medical data processing method (Fig. 2) comprising: outputting second spectral data (S203 outputs second data) by inputting first spectral data (S201, S202) related to an examined subject imaged by a spectral medical imaging apparatus to a trained model configured to generate, on a basis of the first spectral data, the second spectral data having less noise than the first spectral data and a higher resolution than the first spectral data (concept of “Noise-reduction Super-resolution Processing”, discussion starting paragraph 0079), wherein the first spectral data corresponds to medical data obtained by performing a spectral scan on the examined subject, and the trained model is configured to perform a noise reducing process and a super-resolution process on the first spectral data (Fig. 2 illustrate this method. Abstract says inputting first medical data a learned model configured to generated second medical data having lower noise than the first.).
2. The medical data processing method according to claim 1, wherein the first spectral data is first pre-reconstruction data before being reconstructed that is acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, the second spectral data is second pre-reconstruction data before being reconstructed, and a medical image is generated on a basis of the second pre-reconstruction data before being reconstructed (paragraph 0076).
3. The medical data processing method according to claim 2, wherein the first pre-reconstruction data is one selected from among: first projection data acquired by the spectral medical imaging apparatus at first X-ray tube voltage and second projection data acquired at second X-ray tube voltage higher than the first X-ray tube voltage; first reference projection data corresponding to each of a plurality of reference substances; and first count data corresponding to each of a plurality of energy ranges, the second pre-reconstruction data is one selected from among: third projection data corresponding to the first projection data and fourth projection data corresponding to the second projection data; second reference projection data corresponding to the first reference projection data; and second count data corresponding to the first count data, when the first projection data and the second projection data are input to the trained model, the third projection data and the fourth projection data are output, when the first reference projection data is input to the trained model, the second reference projection data is output, and when the first count data is input to the trained model, the second count data is output (Paragraph 0075 and 0076 discuss the input data options that corelate to this claim).
4. The medical data processing method according to claim 1, wherein the first spectral data is a first reconstructed image reconstructed on a basis of acquisition data acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, and the second spectral data is a second reconstructed image having less noise than the first reconstructed image and a higher resolution than the first reconstructed image (note paragraphs 0075-0076).
5. The medical data processing method according to claim 4, wherein the first reconstructed image is one selected from among: a plurality of first reference substance images corresponding to a plurality of reference substances; at least one first virtual monochrome X-ray image having a different X-ray energy level; a first virtual non-contrast-enhanced image; a first iodine map image; a first effective atomic number image; a first electron density image; a plurality of first energy images corresponding to a plurality of energy ranges; a first X-ray tube voltage image corresponding to first X-ray tube voltage used in the imaging process performed by the spectral medical imaging apparatus and a second X-ray tube voltage image corresponding to second X-ray tube voltage higher than the first X-ray tube voltage, the second reconstructed image is one selected from among: a plurality of second reference substance images corresponding to the plurality of first reference substance images; a second virtual monochrome X-ray image corresponding to the first virtual monochrome X-ray image; a second virtual non-contrast-enhanced image corresponding to the first virtual non-contrast-enhanced image; a second iodine map image corresponding to the first iodine map image; a second effective atomic number image corresponding to the first effective atomic number image; a second electron density image corresponding to the first electron density image; a plurality of second energy images corresponding to the plurality of first energy images; a third X-ray tube voltage image corresponding to the first X-ray tube voltage image and a fourth X-ray tube voltage image corresponding to the second X-ray tube voltage image, when the plurality of first reference substance image are input to the trained model, the plurality of second reference substance images are output, when the first virtual monochrome X-ray image is input to the trained model, the second virtual monochrome X-ray image is output, when the first virtual non-contrast-enhanced image is input to the trained model, the second virtual non-contrast-enhanced image is output, when the first iodine map image is input to the trained model, the second iodine map image is output, when the first effective atomic number image is input to the trained model, the second effective atomic number image is output, when the first electron density image is input to the trained model, the second electron density image is output, when the plurality of first energy images are input to the trained model, the plurality of second energy images is output, and when the first X-ray tube voltage image and the second X-ray tube voltage image are input to the trained model, the third X-ray tube voltage image and the fourth X-ray tube voltage image are output (Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies).
6. The medical data processing method according to claim 4, wherein the first reconstructed image is represented by a first X-ray tube voltage image corresponding to first X-ray tube voltage used in the imaging process performed by the spectral medical imaging apparatus and a second X-ray tube voltage image corresponding to second X-ray tube voltage higher than the first X-ray tube voltage, and the second reconstructed image is represented by a third X-ray tube voltage image corresponding to the first X-ray tube voltage image and a fourth X-ray tube voltage image corresponding to the second X-ray tube voltage image (creating image data from scans at different energy levels inherently requires different input tube voltage. Thus is an inherency of multiple scans imaging data).
7. The medical data processing method according to claim 1, wherein the trained model is a model trained by using training data generated by a medical imaging apparatus that uses single energy X-rays, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus (Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies. Note discussion of model generation starting from paragraph 0125). .
8. The medical data processing method according to claim 1, wherein the trained model is a model trained by using training data generated by a medical imaging apparatus that uses dual energy X-rays, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus . (Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies. Note paragraphs 0125, 0126-0132).
9. The medical data processing method according to claim 1, wherein the trained model is a model trained by using training data generated by a photon counting X-ray computed tomography apparatus, and the second spectral data is used for visualizing an image related to X-ray spectra from an imaging process performed on the examined subject by the spectral medical imaging apparatus (note paragraph 0053).
10. The medical data processing method according to claim 1, wherein the spectral medical imaging apparatus is a dual energy computed tomography apparatus that uses dual energy X-rays, and the trained model is trained on a basis of: a first virtual monochrome X-ray image generated on a basis of count projection data related to the examined subject imaged by a photon counting X-ray computed tomography apparatus; and a second virtual monochrome X-ray image obtained by applying, to the count projection data, a simulation process including a resolution lowering process and a noise adding process performed on the count projection data (Fig. 1).
11. The medical data processing method according to claim 10, wherein the first virtual monochrome X-ray image includes a plurality of first virtual monochrome X-ray images corresponding to a plurality of X-ray energy levels, the second virtual monochrome X-ray image includes a plurality of second virtual monochrome X-ray images corresponding to the plurality of X-ray energy levels, the trained model includes a plurality of trained models corresponding to the plurality of X-ray energy levels, and the plurality of trained models are trained by using the plurality of first virtual monochrome X-ray images and the plurality of second virtual monochrome X-ray images in correspondence with the plurality of X-ray energy levels, respectively (Fig. 1).
12. A model generating method for generating a trained model configured, on a basis of first spectral data related to an examined subject imaged by a spectral medical imaging apparatus, to generate the second spectral data having less noise than the first spectral data and a higher resolution than the first spectral data, the model generating method comprising: generating second training data corresponding to noise and a resolution of the first spectral data, by adding noise to and lowering a resolution of first training data corresponding to the noise and the resolution of the second spectral data; and generating the trained model by training a convolution neural network while using the first training data and the second training data (Note paragraphs 0125, 0126-0132) .
13. The model generating method according to claim 12, wherein the first spectral data is a first reconstructed image reconstructed on a basis of acquisition data acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, the second spectral data is a second reconstructed image having less noise than the first reconstructed image and a higher resolution than the first reconstructed image, and the model generating method comprises: generating first pre-reconstruction data before being reconstructed that corresponds to noise and a resolution of the first reconstructed image, by adding noise to and lowering a resolution of the second pre-reconstruction data before being reconstructed that corresponds to the noise and the resolution of the second reconstructed image; reconstructing a first training image on a basis of the second pre-reconstruction data; reconstructing a second training image on a basis of the first pre-reconstruction data; and generating the trained model by training a convolution neural network while using the first training image and the second training image (paragraph 0076).
14. A medical data processing apparatus comprising: processing circuitry configured to output second spectral data by inputting first spectral data related to an examined subject imaged by a spectral medical imaging apparatus to a trained model that generates, on a basis of the first spectral data, the second spectral data having less noise than the first spectral data and a higher resolution than the first (spectral data, wherein the first spectral data corresponds to medical data obtained by performing a spectral scan on the examined subject, and the trained model is configured to perform a noise reducing process and a super-resolution process on the first spectral data (Fig. 2).
15. The medical data processing apparatus according to claim 14, wherein the first spectral data is first pre-reconstruction data before being reconstructed that is acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, the second spectral data is second pre-reconstruction data before being reconstructed, and the processing circuitry generates a medical image on a basis of the second pre-reconstruction data before being reconstructed . Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies).
16. The medical data processing apparatus according to claim 14, wherein the first spectral data is a first reconstructed image reconstructed on a basis of acquisition data acquired from an imaging process performed on the examined subject by the spectral medical imaging apparatus, and the second spectral data is a second reconstructed image having less noise than the first reconstructed image and a higher resolution than the first reconstructed image (paragraph 00760.
17. The medical data processing apparatus according to claim 14, wherein the spectral medical imaging apparatus is a dual energy computed tomography apparatus that uses dual energy X-rays, and the trained model is trained on a basis of: a first virtual monochrome X-ray image generated on a basis of count projection data related to the examined subject imaged by a photon counting X-ray computed tomography apparatus; and a second virtual monochrome X-ray image obtained by applying, to the count projection data, a simulation process including a resolution lowering process and a noise adding process performed on the count projection data Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies)..
18. The medical data processing apparatus according to claim 17, wherein the first virtual monochrome X-ray image includes a plurality of first virtual monochrome X-ray images corresponding to a plurality of X-ray energy levels, the second virtual monochrome X-ray image includes a plurality of second virtual monochrome X-ray images corresponding to the plurality of X-ray energy levels, the trained model includes a plurality of trained models corresponding to the plurality of X-ray energy levels, and the plurality of trained models are trained by using the plurality of first virtual monochrome X-ray images and the plurality of second virtual monochrome X-ray images in correspondence with the plurality of X-ray energy levels, respectively Note discussion in paragraphs 0075-0076. Paragraph 0217 discusses creating first imaging data using different scans at different energies). .
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/DON K WONG/Primary Examiner, Art Unit 2884