DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Using claim 3 as exemplary, the claims require “the at least one sample second images” but only provide antecedent basis for this phrase in the singular tense.
Claims 10-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 10 and 19 both use the phrase “the trained model” with antecedent basis for two trained models. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,266,116. Although the claims at issue are not identical, they are not patentably distinct from each other:
U.S. Patent No. 12,266,116 discloses a system, comprising:
at least one storage device storing executable instructions for image processing; (Claim 1)
and at least one processor in communication with the at least one storage device, wherein when executing the executable instructions, the at least one processor is configured to cause the system to perform operations including: (Claim 1)
obtaining initial data of a subject acquired by an imaging device according to a second tracer; (Claim 1)
obtaining a trained image processing model; and (Claim 1)
generating a target image of the subject by inputting the initial data into the trained image processing model, the target image having a higher image quality than an initial image related to the initial data; (Claim 1)
wherein the trained image processing model is provided by training a preliminary model using training samples related to the second tracer and training samples related to a first tracer different from the second tracer. (Claim 1)
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu (“Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET”) in view of Zhou (US 20190104940 A1).
Regarding claim 1, Liu discloses the system, comprising: and at least one processor configured to cause the system to perform operations including: (Liu teaches a technique for accomplishing deep learning noise reduction for tracers without sufficient training datasets by using a deep transfer learning approach, which can utilize existing networks trained by the widely available FDG datasets, a more widely available tracer, see Abstract. Pg. 4, ¶ 2 discloses a the computer workstation and GPU.)
obtaining initial data of a subject acquired by an imaging device according to a second tracer; (The training data is acquired as follows, pg. 2, second paragraph from bottom through pg. 3, ¶ 3, “this study included four groups of datasets: single-bed FDG, single-bed FMISO, whole-body FDG and whole-body DOTATATE . . . . For the deep transfer learning strategy, the U-Net was initialized with the well-trained FDG U-Net from the conventional training, which was pre-trained using the datasets in the single-bed or whole-body FDG group as the training data. Then a small number of datasets in the non-FDG group (single-bed FMISO or whole-body DOTATATE) were used to fine tune the pre-trained U-Net . . For each dataset in all the four groups, a full-count sinogram was generated with the acquired full-count list-mode data. Then ten independent replicates of low-count sinograms (each containing 10% counts) were generated.” The initial inference/validation data is fed into the model at pg. 4, ¶ 3, “To test all the scans in the non-FDG tracer group without the correlation between training and testing data, a ‘leave-3-out’ cross-validation approach was used for U-Net A, resulting in four replicate networks trained for single-bed FMISO and five replicate networks trained for whole-body DOTATATE.”)
obtaining a trained image processing model; and (See above and the U-net at pg. 3, Fig. 1.)
generating a target image of the subject by inputting the initial data into the trained image processing model, the target image having a higher image quality than an initial image related to the initial data; (See above and the U-net at pg. 3, Fig. 1. The U-net is trained to generate higher quality versions of the input data.)
wherein the trained image processing model is provided by training a preliminary model using training samples related to the second tracer and training samples related to a first tracer different from the second tracer. (Pg. 3, last paragraph, “For the deep transfer learning strategy, the U-Net was initialized with the well-trained FDG U-Net from the conventional training, which was pre-trained using the datasets in the single-bed or whole-body FDG group as the training data.”)
In the field of emissions tomography image processing Zhou teaches at least one storage device storing executable instructions for image processing, and the at least one processor in communication with the at least one storage device, wherein when executing the executable instructions, the at least one processor is configured to cause the system to perform operations (Zhou is an emissions tomography denoising processing technique that includes a processor and a storage device for storing instructions, ¶ 0110-0111.)
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 Liu’s emissions tomography processing with Zhou’s emissions tomography processing. The combination constitutes the repeatable and predictable result of simply applying Zhou’s teaching for using a processor and storage device for storing instructions. Liu already teaches a computer and GPU. 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.
Regarding claim 2, the above combination discloses the system of claim 1, wherein the initial data includes the initial image generated by reconstructing imaging data acquired by the imaging device after the second tracer is injected into the subject, and an output of the trained image processing model includes the target image. 9See rejection of claim 1 above. The initial inference data is fed into the model at pg. 4, ¶ 3, “To test all the scans in the non-FDG tracer group without the correlation between training and testing data, a ‘leave-3-out’ cross-validation approach was used for U-Net A, resulting in four replicate networks trained for single-bed FMISO and five replicate networks trained for whole-body DOTATATE.”)
Regarding claim 3, the above combination discloses the system of claim 2, wherein the training samples related to the first tracer include at least one sample first image generated based on the first tracer, and at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image; and the training samples related to the second tracer include at least one sample second image generated based on the second tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image. (See rejection of claim 1 above. The training data is acquired as follows, Liu pg. 2, second paragraph from bottom through pg. 3, ¶ 3, “For the deep transfer learning strategy, the U-Net was initialized with the well-trained FDG U-Net from the conventional training, which was pre-trained using the datasets in the single-bed or whole-body FDG group as the training data. Then a small number of datasets in the non-FDG group (single-bed FMISO or whole-body DOTATATE) were used to fine tune the pre-trained U-Net . . For each dataset in all the four groups, a full-count sinogram was generated with the acquired full-count list-mode data. Then ten independent replicates of low-count sinograms (each containing 10% counts) were generated.”)
Regarding claim 4, the above combination discloses the system of claim 1, wherein the initial data includes imaging data acquired by the imaging device after the second tracer is injected into the subject. (See rejection of claim 2.)
Regarding claim 5, the above combination discloses the system of claim 4, wherein the imaging data includes histogram data, list-mode data, or sinogram data. (Liu pg. 3, ¶ 3.)
Regarding claim 6, the above combination discloses the system of claim 4, wherein an output of the trained image processing model includes the target image. (See rejection of claim 1.)
Regarding claim 7, the above combination discloses the system of claim 6, wherein the training samples related to the first tracer include sample first imaging data generated based on the first tracer, and a reference first image that corresponds to the sample first imaging data and has a higher image quality than a sample first image generated by reconstructing the sample first imaging data; and the training samples related to the second tracer include sample second imaging data generated based on the second tracer, and a reference second image that corresponds to the sample second imaging data and has a higher image quality than a sample second image generated by reconstructing the sample second imaging data. (See rejection of claim 2.)
Regarding claim 8, the above combination discloses the system of claim 4, wherein an output of the trained image processing model includes updated imaging data, the target image is generated by reconstructing the updated imaging data and has a higher image quality than the initial image generated by reconstructing the initial data. (See rejection of claims 1 and 3.)
Regarding claim 9, the above combination discloses the system of claim 8, wherein the training samples related to the first tracer include sample first imaging data generated based on the first tracer, and a reference first imaging data corresponding to the sample first imaging data, an image generated by reconstructing the reference first imaging data having a higher image quality than an image generated by reconstructing the sample first imaging data; and the training samples related to the second tracer include sample second imaging data generated based on the second tracer, and a reference second imaging data corresponding to the sample second imaging data, an image generated by reconstructing the reference second imaging data having a higher image quality than an image generated by reconstructing the sample second imaging data. (See rejection of claims 1 and 2.)
Claims 10-18 is the method claim corresponding to claims 1-9, additionally including: the trained model is determined by training a preliminary model using training samples related to at least one tracer different from the second tracer (the rejection of claim 1 teaches these limitations). Remaining limitations are rejected similarly. See detailed analysis above.
Regarding claim 19, the above combination discloses the system, comprising: at least one storage device storing executable instructions for model training; and at least one processor in communication with the at least one storage device, wherein when executing the executable instructions, the at least one processor is configured to cause the system to perform operations including: obtaining training samples related to a second tracer; obtaining a trained model, the training model being determined by training a preliminary model using training samples related to at least one tracer different from the second tracer; and generating a trained image processing model by training the trained model using the training samples related to the second tracer. (See rejection of claim 1)
Regarding claim 20, the above combination discloses the system of claim 19, wherein the training samples related to the at least one tracer include first sample data that is generated based on the at least one tracer and used as training input data, and first reference data that corresponds to the first sample data and is used as label data, an image related to the first sample data having a higher image quality than an image related to the first reference data; and the training samples related to the second tracer include second sample data that is generated based on the second tracer and used as the training input data, and second reference data that corresponds to the second sample data and is used as the label data, an image related to the second sample data having a higher image quality than an image related to the second reference data. (See rejection of claim 3)
Conclusion
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/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671
