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 .
Priority
Receipt is acknowledged that application is a PCT/US2022/047929 . Priority to US PRO 63/272,603 with a priority date of 10/27/2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78. Copies of certified papers required by 37 CFR 1.55 have been retrieved.
Information Disclosure Statement
The IDS dated 04/26/2024, 06/16/2025, 08/04/2025, and 03/25/2026 have been considered and placed in the application file.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the
plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claim 1 recite “at least one of ” then listing “at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 19 recite “at least one of ” then listing “at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
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 obviousness-type 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.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 UFR 3.73(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-38 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims of co-pending Application No. 18/707,555. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are narrower in every aspect than the claims in the above-listed reference application and are therefore obvious variants thereof.
This is a provisional nonstatutory obviousness-type double patenting rejection because the patentably indistinct claims have not in fact been patented.
For example, the following is a chart comparing claim 1 and claim 19 of the instant application to the claim 1 and claim 18 of the co-pending application number 18/707,555:
Instant application: 18/705,228
U.S. Application No: 18/707,555
Claim 1. A computing system comprising:
one or more processors; and
a non-transitory computer-readable medium having instructions stored thereon that when executed by the one or more processors cause the computing system to:
obtain, by the one or more processors, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes;
generate, by the one or more processors, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images, the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint, and being based on a first regularization term,
a second regularization term, a coupling term, and a fidelity term; and
present, by the one or more processors, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.
Claim 19: A method comprising:
obtaining, by a computing system, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes;
generating, by the computing system, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images, the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint, and being based on a first regularization term, a second regularization term, a coupling term and a fidelity term; and
presenting, by the computing system, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.
Claim 1: A computing system comprising:
one or more processors; and
a non-transitory computer-readable medium having instructions stored thereon that when executed by the one or more processors cause the computing system to:
obtain, by the one or more processors, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes; generate, by the one or more processors, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images, the planar scintigraphy image reconstruction model comprising a non-negativity constraint and being based on a regularization term and a fidelity term; and present, by the one or more processors, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.
Claim 18: A method comprising:
obtaining, by a computing system, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes;
generating, by the computing system, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images, the planar scintigraphy image reconstruction model comprising a non-negativity constraint and being based on a regularization term and a fidelity term; and presenting, by the computing system, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen.
Although co-pending application 18/707,555 discloses “A computing system comprising:
one or more processors; and a non-transitory computer-readable medium having instructions stored thereon that when executed by the one or more processors cause the computing system to:
obtain, by the one or more processors, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes; generate, by the one or more processors, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images, the planar scintigraphy image reconstruction model comprising” and “wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen” it does not explicitly disclose “first non-negativity constraint and a second non-negativity constraint, and being based on a first regularization term, a second regularization term, a coupling term, and a fidelity term”. However, in an analogous field of endeavor Bresler discloses “a first non-negativity constraint and a second non-negativity constraint (Bresler ¶0035, ¶0048, ¶00113 and ¶0131 discloses multiple (P1 or P2) positive constraints on the model function), and being based on a first regularization term, a second regularization term, (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms) a coupling term (Bresler ¶0048 discloses a norm penalty that sums the magnitudes of the entries of matrix B, which the specification details that the coupling term can be a KL norm), and a fidelity term (Bresler ¶0032 discloses a data fidelity term)”. Accordingly, before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the limitations of claim 1 and 19 of the co-pending application 18/707,555 with the teachings of Bresler to implement multiple regularization terms and multiple nonnegativity constraints to create a more robust reconstruction model. One of ordinary skill in the art would be motivated to combine the limitations of claim 1 and 19 of the co-pending application 18/707,555 with the Bresler reference in order to produce “numerous techniques have been proposed to reduce the amount of data required for accurate reconstruction, with the aim of enabling much higher clinical throughput, or accurately capturing time varying phenomena such as motion, changes in concentration, flow, etc., or avoiding artifacts due to such phenomena..” as disclosed by Bresler in ¶0006. Therefore, it would have been obvious to combine the limitations of claim 1 of the co-pending application 18/707,555 and Bresler to obtain the invention of the instant claim 1 and 19.
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 (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 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 1-12, 19-26, and 28-31 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Schmidtlein {Schmidtlein, Charles Ross, et al. "A deblurring/denoising corrected scintigraphic planar image reconstruction model for targeted alpha therapy." Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging. Vol. 11600. SPIE, 2021).
It is recognized that Schmiedtlein is a grace period reference having common but not completely identical inventorship/authorship including common inventor/authors Schmidtlein, Charles Ross; Krol, Andrzej; Gifford, Howard; and Xu, Yuesheng. As such, Schmidtlein is potentially subject to a 102(b)(1)(A) exception but such an exception has not yet been perfected via a 132 Declaration. It is further noted that Schmidtlein is the NPL version of the instant application that clearly anticipates the rejected claims as follows:
Regarding Claim 1 Schmeidtlein discloses a computing system comprising: one or more processors; and
a non-transitory computer-readable medium having instructions stored thereon (Schmidtlein Section III discloses implementing the algorithms via Matlab, a laptop with Intel processor, and various memories for storing computer programs) that when executed by the one or more processors cause the computing system (Schmidtlein Section III discloses a laptop with Intel processor, and various memories for storing computer programs) to:
obtain, by the one or more processors, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes (Schmidtlein abstract, Introduction, and section IIA disclose including obtaining/inputting PET (positron emission tomography) images which are within the BRI of planar scintigraphy images of a subject that contain artifacts per the instant specification);
generate, by the one or more processors, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images (Schmidtlein abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation), , the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under bri can interpreted as a first and second constraint), and being based on a first regularization term, a second regularization term, (Schmidrlein 2.3 discloses second order isotropic total variation norms, y1;2 are the regularization weights) a coupling term (Schmidtlein 2.1 Equation 3 discloses a KL-divergence norm, which the specification details that the coupling term can be a KL norm), and a fidelity term (Schmidtlein 2.1 Equation 2 discloses a KL fidelity term ); and
present, by the one or more processors, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen (Schmidtlein Section 1 discloses which PET imagery is used for diagnosis, therapy and response assessment of various tracer’s biodistribution in the imagery).
Regarding Claim 2, Schmidlein discloses the computing system of claim 1, wherein the plurality of planar scintigraphy images comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image (Schmidlein, Abstract, Introduction, and Section 2.1 discloses anterior/posterior (A/P) planar image pairs).
Regarding Claim 3, Schmidlein discloses the computing system of claim 1, wherein the one or more physical processes comprise gamma ray attenuation, gamma ray collimator penetration, or gamma ray scatter (Schmidtlein Abstract, Introduction, and Section 2.2 discloses gamma ray attenuation, collimator penetration and gamma ray scatter).
Regarding Claim 4, Schmidlein discloses the computing system of claim 1, wherein obtaining the plurality of planar scintigraphy images comprises using a plurality of gamma ray detectors to generate the plurality of planar scintigraphy images (Schmidtlein, Abstract and Section 2.2 disclose obtaining a plurality of planar scintigraphy images using gamma ray detectors).
Regarding Claim 5, Schmidlein discloses the computing system of claim 1, wherein the first regularization term (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under broadest reasonable interpretation can interpreted as a first and second constraint) corresponds to a total variation regularization for controlling noise (Schmidtlein Section 2.1, Introduction, and abstract disclose see total variation regularization to control noise).
Regarding Claim 6, Schmidlein discloses the computing system of claim 1, wherein the second regularization term (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under broadest reasonable interpretation can interpreted as a first and second constraint) corresponds to a total variation regularization for controlling noise (Schmidtlein Section 2.1, Introduction, and abstract disclose see total variation regularization to control noise).
Regarding Claim 7, Schmidlein discloses the computing system of claim 1, wherein the planar scintigraphy image reconstruction model comprises a minimization operation (Schmidtlein Section 2.1 discloses using a minimization operation in equation 3) based on the first regularization term, the second regularization term (Schmidrlein 2.3 discloses second order isotropic total variation norms, y1;2 are the regularization weights), the fidelity term (Schmidtlein 2.1 Equation 2 discloses a KL fidelity term ), the coupling term (Schmidtlein 2.1 Equation 3 discloses a KL-divergence norm, which the specification details that the coupling term can be a KL norm), the first non-negativity constraint, and the second non-negativity constraint (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under bri can interpreted as a first and second constraint).
Regarding Claim 8, Schmidlein discloses the computing system of claim 7, wherein the minimization operation is based on a divergence norm, a coupling parameter (
β
), a first regularization parameter (
λ
1
), and a second regularization parameter
(
λ
2
). (Schmidtlein Abstract, Introduction, and Section 2.1 disclose equation 1 SPECT image reconstruction model, lambda is the regularization parameter and i+ is the indicator function imposing a non-negativity constraint on f and wherein the KL-divergence norm is the fidelity term. As to plural images including anterior/posterior (A/P) planar image pairs).
Regarding Claim 9, Schmidlein discloses the computing system of claim 1, wherein the planar scintigraphy image reconstruction model is based on a two-view single photon emission computed tomography (SPECT) physical model (Schmidtlein Section 2 discloses physical model for planar image formation is described by a two-view SPECT system model.)
Regarding Claim 10, Schmidlein discloses the computing system of claim 9, wherein the two-view SPECT physical model (Schmidtlein Section 2 discloses physical model for planar image formation is described by a two-view SPECT system model.) comprises an anterior view and a posterior view (Schmidlein, Abstract, Introduction, and Section 2.1 discloses anterior/posterior (A/P) planar image pairs).
Regarding Claim 11, Schmidlein discloses the computing system of claim 9, wherein generating the CPSI (Schmidtlein abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation) comprises: estimating an anterior/posterior (A/P) projection of activity bio-distribution using the two-view SPECT physical model as a constraint (Schmidtlein Section 1 and 2.1 discloses estimating the projection of activity using the model).
Regarding Claim 12, Schmidlein discloses the computing system of claim 9, wherein the two-view SPECT physical model is determined according to:
PNG
media_image1.png
56
239
media_image1.png
Greyscale
wherein x == (x1, x2, x3),
Y == (Y1, Y2, y3),
the g(x) describes the plurality of planar scintigraphy images,
the f (y) represents a 3D biodistribution, and
the K(x; y) describes a kernel of a region J == [a, b].
(Schmidtlein Section 2 Equation 1 discloses the exact equation)
Regarding Claim 19, Schmidlein discloses a method (Schmidtlein Section 2 discloses the method) comprising:
obtaining, by a computing system (Schmidtlein Section III discloses a laptop with Intel processor, and various memories for storing computer programs), in the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes (Schmidtlein abstract, Introduction, and section IIA disclose including obtaining/inputting PET (positron emission tomography) images which are within the BRI of planar scintigraphy images of a subject that contain artifacts per the instant specification);
generating, by the computing system (Schmidtlein Section III discloses a laptop with Intel processor, and various memories for storing computer programs), a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images (Schmidtlein abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation), the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under bri can interpreted as a first and second constraint), and being based on a first regularization term, a second regularization term, (Schmidrlein 2.3 discloses second order isotropic total variation norms, y1;2 are the regularization weights) a coupling term (Schmidtlein 2.1 Equation 3 discloses a KL-divergence norm, which the specification details that the coupling term can be a KL norm), and a fidelity term (Schmidtlein 2.1 Equation 2 discloses a KL fidelity term ); and
presenting, by the computing system(Schmidtlein Section III discloses a laptop with Intel processor, and various memories for storing computer programs), the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen (Schmidtlein Section 1 discloses which PET imagery is used for diagnosis, therapy and response assessment of various tracer’s biodistribution in the imagery).
Regarding Claim 20, Schmidlein discloses the method of claim 19, wherein the plurality of planar scintigraphy images comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image (Schmidlein, Abstract, Introduction, and Section 2.1 discloses anterior/posterior (A/P) planar image pairs).
Regarding Claim 21, Schmidlein discloses the method of claim 19, wherein the one or more physical processes comprise gamma ray attenuation, gamma ray collimator penetration, or gamma ray scatter (Schmidtlein Abstract, Introduction, and Section 2.2 discloses gamma ray attenuation, collimator penetration and gamma ray scatter).
Regarding Claim 22, Schmidlein discloses the method of claim 19, wherein obtaining the plurality of planar scintigraphy images comprises using a plurality of gamma ray detectors to generate the plurality of planar scintigraphy images(Schmidtlein, Abstract and Section 2.2 disclose obtaining a plurality of planar scintigraphy images using gamma ray detectors).
Regarding Claim 23, Schmidlein discloses the method of claim 19, wherein the first regularization term (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under broadest reasonable interpretation can interpreted as a first and second constraint) corresponds to a total variation regularization for controlling noise (Schmidtlein Section 2.1, Introduction, and abstract disclose see total variation regularization to control noise).
Regarding Claim 24, Schmidlein discloses the method of claim 19, wherein the second regularization term (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under broadest reasonable interpretation can interpreted as a first and second constraint) corresponds to a total variation regularization for controlling noise (Schmidtlein Section 2.1, Introduction, and abstract disclose see total variation regularization to control noise).
Regarding Claim 25, Schmidlein discloses the method of claim 19, wherein the planar scintigraphy image reconstruction model comprises a minimization operation (Schmidtlein Section 2.1 discloses using a minimization operation in equation 3) based on the first regularization term, the second regularization term (Schmidrlein 2.3 discloses second order isotropic total variation norms, y1;2 are the regularization weights), the fidelity term (Schmidtlein 2.1 Equation 2 discloses a KL fidelity term ), the coupling term (Schmidtlein 2.1 Equation 3 discloses a KL-divergence norm, which the specification details that the coupling term can be a KL norm), the first non-negativity constraint, and the second non-negativity constraint (Schmidtlein table 1 includes a non-negativity (and smooth) constraint term which under bri can interpreted as a first and second constraint).
Regarding Claim 26, Schmidlein discloses the method of claim 25, wherein the minimization operation is based on a divergence norm, a coupling parameter (
β
), a first regularization parameter (
λ
1
), and a second regularization parameter
(
λ
2
). (Schmidtlein Abstract, Introduction, and Section 2.1 disclose equation 1 SPECT image reconstruction model, lambda is the regularization parameter and i+ is the indicator function imposing a non-negativity constraint on f and wherein the KL-divergence norm is the fidelity term. As to plural images including anterior/posterior (A/P) planar image pairs).
Regarding Claim 28, Schmidlein discloses the method of claim 19, wherein the planar scintigraphy image reconstruction model is based on a two-view single photon emission computed tomography (SPECT) physical model (Schmidtlein Section 2 discloses physical model for planar image formation is described by a two-view SPECT system model.)
Regarding Claim 29, Schmidlein discloses the method of claim 28, wherein the two-view SPECT physical model (Schmidtlein Section 2 discloses physical model for planar image formation is described by a two-view SPECT system model.) comprises an anterior view and a posterior view (Schmidlein, Abstract, Introduction, and Section 2.1 discloses anterior/posterior (A/P) planar image pairs).
Regarding Claim 30, Schmidlein discloses the method of claim 28, wherein generating the CPSI (Schmidtlein abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation) comprises: estimating an anterior/posterior (A/P) projection of activity bio-distribution using the two-view SPECT physical model as a constraint (Schmidtlein Section 1 and 2.1 discloses estimating the projection of activity using the model).
Regarding Claim 31, Schmidlein discloses The method of claim 28, comprising:
determining the two-view SPECT physical model according to:
PNG
media_image1.png
56
239
media_image1.png
Greyscale
wherein x == (xi, x2, x3),
Y == (Yi Y2, y3),
the g(x) describes the plurality of planar scintigraphy images,
the f (y) represents a 3D biodistribution, and
the K(x; y) describes a kernel of a region J == [a, b].
(Schmidtlein Section 2 Equation 1 discloses the exact equation).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-8, 19, and 21-26 are rejected under 35 U.S.C. 103 as unpatentable over Lin et al. (Lin, Yizun, et al. "A Krasnoselskii-Mann algorithm with an improved EM preconditioner for PET image reconstruction." IEEE transactions on medical imaging 38.9 (2019): 2114-2126, hereafter referred to as Lin) in view of Bresler et al (US Patent Publication US 2015/0287223 A1, hereafter referred to as Bresler).
Regarding Claim 1, Lin teaches a computing system comprising: one or more processors; and
a non-transitory computer-readable medium having instructions stored thereon (Lin, Section III, A, discloses implementing the algorithms via Matlab, a laptop with Intel processor, and various memories for storing computer programs) that when executed by the one or more processors cause the computing system (Lin, Section III, A, discloses a laptop with Intel processor, and various memories for storing computer programs) to:
obtain, by the one or more processors, a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes (Lin abstract, Introduction, and section IIA disclose including obtaining/inputting PET (positron emission tomography) images which are within the BRI of planar scintigraphy images of a subject that contain artifacts per the instant specification);
generate, by the one or more processors, a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation)
present, by the one or more processors, the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen (Lin Section 1 discloses which PET imagery is used for diagnosis, therapy and response assessment of various tracer’s biodistribution in the imagery).
Lin does not explicitly disclose the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint and being based on a first regularization term, a second regularization term, a coupling term, and a fidelity term.
Bresler is in the same field of image analysis of medical images for reconstruction. Further, Bresler teaches the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint (Bresler ¶0035, ¶0048, ¶00113 and ¶0131 discloses multiple (P1 or P2) positive constraints on the model function), and being based on a first regularization term, a second regularization term, (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms) a coupling term (Bresler ¶0048 discloses a norm penalty that sums the magnitudes of the entries of matrix B, which the specification details that the coupling term can be a KL norm), and a fidelity term (Bresler ¶0032 discloses a data fidelity term).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin by incorporating the multiple regularization terms and multiple nonnegativity constraints to create a more robust reconstruction model as taught by Bresler; to make an invention that can automatically determine the reconstruction model of the images; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need for numerous techniques have been proposed to reduce the amount of data required for accurate reconstruction, with the aim of enabling much higher clinical throughput, or accurately capturing time varying phenomena such as motion, changes in concentration, flow, etc., or avoiding artifacts due to such phenomena as disclosed by Bresler in ¶0006.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 3, Lin in view of Bresler teaches the computing system of claim 1, wherein the one or more physical processes comprise gamma ray attenuation, gamma ray collimator penetration, or gamma ray scatter (Lin Section 2 A and Section 3A disclose PET images employ gamma ray detectors to detect images and artifacts include gamma ray collimator penetration and scatter). See rationale for Claim 1 (its parent claim).
Regarding Claim 4, Lin in view of Bresler teaches the computing system of claim 1, wherein obtaining the plurality of planar scintigraphy images comprises using a plurality of gamma ray detectors to generate the plurality of planar scintigraphy images (Lin Section 2A and abstract disclose PET images employ gamma ray detectors to detect images, the plurality of planar scintigraphy images note that the restoration model is iterative on plural images until convergence). See rationale for Claim 1 (its parent claim).
Regarding Claim 5, Lin in view of Bresler teaches the computing system of claim 1, wherein the first regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms) corresponds to a total variation regularization for controlling noise (Lin Abstract, Introduction, Section 2A, and Section 3b, discloses including total variation regularized model and total variation (TV) penalty and higher order total variation (HOTV)). See rationale for Claim 1 (its parent claim).
Regarding Claim 6, Lin in view of Bresler teaches the computing system of claim 1, wherein the second regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms)corresponds to a total variation regularization for controlling noise (Lin Abstract, Introduction, Section 2A, and Section 3b, discloses including total variation regularized model and total variation (TV) penalty and higher order total variation (HOTV)). See rationale for Claim 1 (its parent claim).
Regarding Claim 7, Lin in view of Bresler teaches the computing system of claim 1, wherein the planar scintigraphy image reconstruction model comprises a minimization operation (Lin Abstract and Section 2A discloses HOTV Regularized PET Image Reconstruction Model and Section III, table 1 which includes a minimization operation based on non-negativity (and smooth) constraint term, regularization term and a KL fidelity term) based on the first regularization term, the second regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms), the fidelity term (Bresler ¶0032 discloses a data fidelity term), the coupling term (Bresler ¶0048 discloses a norm penalty that sums the magnitudes of the entries of matrix B, which the specification details that the coupling term can be a KL norm), the first non-negativity constraint, and the second non-negativity constraint (Bresler ¶0035, ¶0048, ¶00113 and ¶0131 discloses multiple (P1 or P2) positive constraints on the model function).See rationale for Claim 1 (its parent claim).
Regarding Claim 8, Lin in view of Bresler teaches the computing system of claim 7, wherein the minimization operation is based on a divergence norm, a coupling parameter (/3 ), a first regularization parameter (;\.1), and a second regularization parameter (;\.2). (Lin Section 2A discloses a minimization of fidelity term and applying regularization terms to avoid over-fitting. As to divergence norm see “The two functions ϕ1, ϕ2 are defined by the l1-norm for the anisotropic TV or the l2-norm for the isotropic TV, and thus they are convex. Here B1 ∈ Rm1×d , B2 ∈ Rm2×d are the first-order and second order difference matrices, respectively, and λ1, λ2 ∈ R+ are the corresponding regularization parameters”). See rationale for Claim 1 (its parent claim).
Regarding Claim 19, Lin teaches a method (Lin Abstract discloses the method) comprising:
obtaining, by a computing system (Lin, Section III, A, discloses a laptop with Intel processor, and various memories for storing computer programs), a plurality of planar scintigraphy images of a subject, wherein the plurality of planar scintigraphy images contain image artifacts caused by one or more physical processes (Lin abstract, Introduction, and section IIA disclose including obtaining/inputting PET (positron emission tomography) images which are within the BRI of planar scintigraphy images of a subject that contain artifacts per the instant specification);
generating, by the computing system (Lin, Section III, A, discloses a laptop with Intel processor, and various memories for storing computer programs), a corrected planar scintigraphy image (CPSI) corrected for the image artifacts by applying a planar scintigraphy image reconstruction model to the plurality of planar scintigraphy images (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation),
presenting, by the computing system (Lin, Section III, A, discloses a laptop with Intel processor, and various memories for storing computer programs), the CPSI for evaluation of a condition of the subject, wherein presenting the CPSI comprises at least one of transmitting the CPSI to a computing device or displaying the CPSI on a display screen(Lin Section 1 discloses which PET imagery is used for diagnosis, therapy and response assessment of various tracer’s biodistribution in the imagery).
Lin does not explicitly disclose the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint, and being based on a first regularization term, a second regularization term, a coupling term and a fidelity term.
Bresler is in the same field of image analysis of medical images for reconstruction. Further, Bresler teaches the planar scintigraphy image reconstruction model comprising a first non-negativity constraint and a second non-negativity constraint (Bresler ¶0035, ¶0048, ¶00113 and ¶0131 discloses multiple (P1 or P2) positive constraints on the model function), and being based on a first regularization term, a second regularization term, (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms) a coupling term (Bresler ¶0048 discloses a norm penalty that sums the magnitudes of the entries of matrix B, which the specification details that the coupling term can be a KL norm), and a fidelity term (Bresler ¶0032 discloses a data fidelity term).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin by incorporating the multiple regularization terms and multiple nonnegativity constraints to create a more robust reconstruction model as taught by Bresler; to make an invention that can automatically determine the reconstruction model of the images; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need for numerous techniques have been proposed to reduce the amount of data required for accurate reconstruction, with the aim of enabling much higher clinical throughput, or accurately capturing time varying phenomena such as motion, changes in concentration, flow, etc., or avoiding artifacts due to such phenomena as disclosed by Bresler in ¶0006.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 21, Lin in view of Bresler teaches the method of claim 19, wherein the one or more physical processes comprise gamma ray attenuation, gamma ray collimator penetration, or gamma ray scatter (Lin Section 2 A and Section 3A disclose PET images employ gamma ray detectors to detect images and artifacts include gamma ray collimator penetration and scatter). See rationale for Claim 19 (its parent claim).
Regarding Claim 22, Lin in view of Bresler teaches the method of claim 19, wherein obtaining the plurality of planar scintigraphy images comprises using a plurality of gamma ray detectors to generate the plurality of planar scintigraphy images (Lin Section 2A and abstract disclose PET images employ gamma ray detectors to detect images, the plurality of planar scintigraphy images note that the restoration model is iterative on plural images until convergence). See rationale for Claim 19 (its parent claim).
Regarding Claim 23, Lin in view of Bresler teaches the method of claim 19, wherein the first regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms) corresponds to a total variation regularization for controlling noise (Lin Abstract, Introduction, Section 2A, and Section 3b, discloses including total variation regularized model and total variation (TV) penalty and higher order total variation (HOTV)). See rationale for Claim 19 (its parent claim).
Regarding Claim 24, Lin in view of Bresler teaches the method of claim 19, wherein the second regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms)corresponds to a total variation regularization for controlling noise (Lin Abstract, Introduction, Section 2A, and Section 3b, discloses including total variation regularized model and total variation (TV) penalty and higher order total variation (HOTV)). See rationale for Claim 19 (its parent claim).
Regarding Claim 25, Lin in view of Bresler teaches the method of claim 19, wherein the planar scintigraphy image reconstruction model comprises a minimization operation (Lin Abstract and Section 2A discloses HOTV Regularized PET Image Reconstruction Model and Section III, table 1 which includes a minimization operation based on non-negativity (and smooth) constraint term, regularization term and a KL fidelity term) based on the first regularization term, the second regularization term (Bresler ¶0033, ¶0090 discloses a regularization parameter that is transformed based on the learning regularizer, which under BRI can be interpreted as multiple regularization terms), the fidelity term (Bresler ¶0032 discloses a data fidelity term), the coupling term (Bresler ¶0048 discloses a norm penalty that sums the magnitudes of the entries of matrix B, which the specification details that the coupling term can be a KL norm), the first non-negativity constraint, and the second non-negativity constraint (Bresler ¶0035, ¶0048, ¶00113 and ¶0131 discloses multiple (P1 or P2) positive constraints on the model function).See rationale for Claim 19 (its parent claim).
Regarding Claim 26, Lin in view of Bresler teaches the method of claim 25, wherein the minimization operation is based on a divergence norm, a coupling parameter (/3 ), a first regularization parameter (;\.1), and a second regularization parameter (;\.2).(Lin Section 2A discloses a minimization of fidelity term and applying regularization terms to avoid over-fitting. As to divergence norm see “The two functions ϕ1, ϕ2 are defined by the l1-norm for the anisotropic TV or the l2-norm for the isotropic TV, and thus they are convex. Here B1 ∈ Rm1×d , B2 ∈ Rm2×d are the first-order and second order difference matrices, respectively, and λ1, λ2 ∈ R+ are the corresponding regularization parameters”). See rationale for Claim 19 (its parent claim).
Claims 2, 9-11, 20, 27-30, 38 are rejected under 35 U.S.C. 103 as unpatentable over Lin in view of Bresler in father view of Chen et al (US Patent Publication US 2020/0094074 A1, hereafter referred to as Chen).
Regarding Claim 2, Lin in view of Bresler teaches the computing system of claim 1, wherein the plurality of planar scintigraphy images (Lin Abstract, Section 2 discloses the plurality of planar scintigraphy images note that the restoration model is iterative on plural images until convergence).
Lin in view of Bresler does not explicitly disclose comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image (Chen ¶0007 disclose MR imagining typically include an anterior/ posterior (A/P) view and a lateral view).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 9, Lin in view of Bresler teaches the computing system of claim 1, wherein the planar scintigraphy image reconstruction model (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation).
Lin in view of Bresler does not explicitly disclose is based on a two-view single photon emission computed tomography (SPECT) physical model.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches is based on a two-view single photon emission computed tomography (SPECT) physical model (Chen ¶0007, ¶0029 discloses a spect system based on including an anterior/ posterior (A/P) view and a lateral view).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 10, Lin in view of Bresler in view of Chen teaches the computing system of claim 9, wherein the two-view SPECT physical model comprises an anterior view and a posterior view (Chen ¶0007 disclose MR imagining typically include an anterior/ posterior (A/P) view and a lateral view). See rationale for Claim 9, its parent claim.
Regarding Claim 11, Lin in view of Bresler in view of Chen teaches the computing system of claim 9, wherein generating the CPSI (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation) comprises: estimating an anterior/posterior (A/P) projection (Chen ¶0009, ¶0038 discloses 2D projection image data of different views) of activity bio-distribution (Lin, Section I in which PET imagery is used for response assessment of various tracer’s biodistribution in the imagery) using the two-view SPECT physical model as a constraint (Chen ¶0007, ¶0029 discloses a spect system based on including an anterior/ posterior (A/P) view and a lateral view). See rationale for Claim 9, its parent claim.
Regarding Claim 20, Lin in view of Bresler teaches the method of claim 19, wherein the plurality of planar scintigraphy images (Lin Abstract, Section 2 discloses the plurality of planar scintigraphy images note that the restoration model is iterative on plural images until convergence).
Lin in view of Bresler does not explicitly disclose comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches comprise an anterior planar scintigraphy image or a posterior planar scintigraphy image (Chen ¶0007 disclose MR imagining typically include an anterior/ posterior (A/P) view and a lateral view).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 27, Lin in view of Bresler teaches the method of claim 19, comprising:
determining, according to the generated CPSI (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation).
Lin in view of Bresler does not explicitly disclose a dosage of radiation administered to the subject that minimizes a risk of toxicity to non-cancerous tissue, while optimizing treatment for cancerous tissue.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches a dosage of radiation administered to the subject that minimizes a risk of toxicity to non-cancerous tissue, while optimizing treatment for cancerous tissue (Chen ¶0051, ¶0053, ¶0060, ¶0072 discloses providing accurate radiation dose estimates that can inform scanning parameter prescription and, thus, overcome limitations of automatic exposure control schemes in diagnostic CT).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 28, Lin in view of Bresler teaches the method of claim 19, wherein the planar scintigraphy image reconstruction model (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation).
Lin in view of Bresler does not explicitly disclose is based on a two-view single photon emission computed tomography (SPECT) physical model.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches is based on a two-view single photon emission computed tomography (SPECT) physical model (Chen ¶0007, ¶0029 discloses a spect system based on including an anterior/ posterior (A/P) view and a lateral view).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 29, Lin in view of Bresler in view of Chen teaches the method of claim 28, wherein the two-view SPECT physical model comprises an anterior view and a posterior view (Chen ¶0007 disclose MR imagining typically include an anterior/ posterior (A/P) view and a lateral view). See rationale for Claim 28, its parent claim.
Regarding Claim 30, Lin in view of Bresler in view of Chen teaches the method of claim 28, wherein generating the CPSI (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation) comprises:
estimating an anterior/posterior (A/P) projection (Chen ¶0009, ¶0038 discloses 2D projection image data of different views) of activity bio-distribution (Lin, Section I in which PET imagery is used for response assessment of various tracer’s biodistribution in the imagery) using the two-view SPECT physical model as a constraint (Chen ¶0007, ¶0029 discloses a spect system based on including an anterior/ posterior (A/P) view and a lateral view). See rationale for Claim 28, its parent claim.
Regarding Claim 38, Lin in view of Bresler teaches the method of claim 19, further comprising using the CPSI (Lin abstract, discloses a reconstruction-based method to recover a single corrected planar scintigraphic patient image corrected for attenuation).
Lin in view of Bresler does not explicitly disclose to evaluate the condition of the subject.
Chen is in the same field of image analysis of medical images for reconstruction. Further, Chen teaches to evaluate the condition of the subject (Chen ¶0073 discloses accurate patient geometry and crosssectional attenuation distribution estimations from the radiograph localizers acquired prior to the actual CT scan, the method provides a new way to conceptualize radiation dose and image quality prescription for diagnostic MDCT).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lin in view of Bresler by incorporating the multiple image views for the SPECT model for practical application in patient treatment as taught by Chen; to make an invention that can automatically determine the reconstruction model based on multiple clinical views; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to have improved systems and methods to assist with managing signal-to-noise ratio and, in CT imaging, its relation to dose prescription/control.as disclosed by Chen in ¶0008.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claims 13-18, and 32-37 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.
The following is an examiner’s statement of reasons for allowance:
Although Lin and Schmiedtlein disclose regularization terms none of the prior art discloses or fairly suggests wherein two-view SPECT physical model is reformulated, including coupling term imposes an equivalence constraint, the planar scintigraphy image reconstruction model is discretized, and to apply the discretized planar scintigraphy image reconstruction model using a fixed point algorithm with higher order total variation regularization (HOTV). Claim 14 is dependent on Claim 13 which is why it is objected to, even though is it taught by Schmidtlein. Claim 33 is dependent on Claim 32 which is why it is objected to, even though is it taught by Schmidtlein.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent US-10448909-B2 to Wieczorek et al. discloses a method to provide an x-ray image and a corresponding nuclear image of a region of interest
Conclusion
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/RACHEL L ROBERTS/Examiner, Art Unit 2674
/Ross Varndell/Primary Examiner, Art Unit 2674