DETAILED ACTION
Notice of Pre-AIA or 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 .
Election/Restrictions
2. Applicant’s election without traverse of Species 1 (corresponding to claims 1-10, 12-16, 21, and 33) in the reply filed on 3/31/2026 is acknowledged. Applicant has/had canceled claims 11, 17-20, 22-24, 27, and 29-32. Applicant has added one new claim (# 33) which corresponds to species 1. Currently, claims 1-10, 12-16, 21, 25-26, 28, and 33 are pending. Applicant has changed the dependency of claims 25-26 and 28 to claim 16 which is in species 1, therefore these claims will be considered with species 1. Examiner refers to the action below.
Claim Rejections - 35 USC § 103
3. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10, 21, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Feng et al. (Deep Learning-Based Image Reconstruction For TOF PET With Direct Data Partitioning Format, PHYSICS IN MEDICINE AND BIOLOGY, 66(16): 165007-1-165007-10, 2021, will be further referred to as Feng) and further in view of Xie et al. (Rapid High-Quality Pet Patlak Parametric Image Generation Based on Direct Reconstruction and Temporal Nonlocal Neural Network, NEUROIMAGE, 2021, 11 pages, will be further referred to as Xie).
Regarding claim 1: Feng et al. discloses a method for positron emission computed tomography (PET) image reconstruction, implemented on a computing device having at least one processor and at least one storage device, the method comprising:
determining correction data based on original PET data (Feng et al.; abstract and section 2 to section 2.4);
determining reconstruction data to be reconstructed based on the correction data (Feng et al.; abstract and section 2 to section 2.4); and
Feng et al. does not teach the feature of “generating, based on the reconstruction data, one or more of a PET reconstruction image and a PET parametric image.” Xie et al. teaches the feature of “generating, based on the reconstruction data, one or more of a PET reconstruction image and a PET parametric image” (Xie et al.; abstract section 1and section 2.1). It would have been obvious to one ordinary skilled in the art to combine the teaching of Xie et al. to the disclosure of Feng et al. since they are analogous in the field of PET image reconstruction. One ordinary skilled in the art would have been motivated to incorporate the teaching of Xie et al. into the disclosure of Feng et al. in order to produce more accurate PET images.
Regarding claim 2: The method of claim 1, wherein the reconstruction data includes target PET data and the correction data, the target PET data is generated based on the original PET data, and the target PET data has a TOF histo-image format (Feng et al.; section 2); and
the generating, based on the reconstruction data, one or more of a PET reconstruction image and a PET parametric image (Feng et al.; abstract and section 2) includes:
generating the PET reconstruction image based on the target PET data and the correction data (Feng et al.; section 2).
Regarding claim 3: The method of claim 2, wherein the original PET data includes PET data obtained based on a plurality of projection angles (Feng et al.; abstract and section 2).
Regarding claim 4: The method of claim 2, wherein the generating the PET reconstruction image based on the target PET data and the correction data includes:
generating the PET reconstruction image by inputting the target PET data and the correction data into a first deep learning model (Feng et al.; section 2.3).
Regarding claim 5: The method of claim 4, wherein the correction data is determined based on at least two types of data among an attenuation map, scatter correction data, and random correction data, and weight values corresponding to the at least two types of data (Feng et al.; section 2.2),
the weight values corresponding to the at least two types of data are determined based on a processing result, which is generated by a weight prediction model based on environmental parameters, and
the weight prediction model is a trained machine learning model (Feng et al.; sections 2.2. and 2.3).
Regarding claim 6: The method of claim 4, wherein the first deep learning model at least includes a first embedding layer and a second embedding layer, and the generating the PET reconstruction image by inputting the target PET data and the correction data into a first deep learning model includes: splitting the target PET data into first data sets; obtaining first feature information by processing the first data sets using the first embedding layer; splitting the correction data into second data sets; obtaining second feature information by processing the second data sets using the second embedding layer; and generating the PET reconstruction image by processing the first feature information and the second feature information using other components of the first deep learning model (Feng et al. sections 2.2. and 2.3 and Xie et al. sections 2.2.2 and 2.3).
Regarding claim 7: The method of claim 3, wherein the generating the PET reconstruction image based on the target PET data and the correction data includes:
generating corrected target PET data based on the target PET data and the correction data, wherein the corrected target PET data has a TOF histo-image format; and generating the PET reconstruction image based on the corrected target PET data (Feng et al.; sections 2.2 and 2.3).
Regarding claim 8: The method of claim 7, wherein the generating the PET reconstruction image based on the corrected target PET data includes:
generating the PET reconstruction image by inputting the corrected target PET data into a second deep learning model (Feng et al. section 2.3).
Regarding claim 9: The method of claim 7, wherein the correction data includes first correction data and second correction data, the corrected target PET data is generated based on the first correction data, and the generating the PET reconstruction image based on the corrected target PET data includes:
generating an initial PET reconstruction image based on the corrected target PET data; and generating the PET reconstruction image by correcting the initial PET reconstruction image based on the second correction data (Feng et al.; section 2.2).
Regarding claim 10: The method of claim 9, wherein the correction data includes an attenuation map, scatter correction data, and random correction data, and the method further includes:
for each data of the attenuation map, the scatter correction data, and the random correction data, generating, based on the data, a reference PET reconstruction image; and determining, based on the reference PET reconstruction image, an evaluation score corresponding to the data; and determining the first correction data based on the evaluation score corresponding to the each data (Feng et al. section 2.2).
Regarding claim 21: See claim 1.
Regarding claim 33: See claim 1.
Allowable Subject Matter
4. Claims 12-16, 25-26, and 28 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Contact Information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on 571-272-4650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
June 8, 2026
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