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 Objections
Claim 15 is objected to because of the following informalities: there is no punction at the end of the sentence, i.e., “.”. 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, 13-16, 24, 31, 46 and 54 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,638,533. Although the claims at issue are not identical, they are not patentably distinct from each other.
Current Application U.S. Patent No. 12,638,533
1. (Original) A non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining invariants associated with at least one physical structure, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising:
receiving at least one particular component which is at least one of a component of a diffusion tensor or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure; and
generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component.
13. (Original) The computer-accessible medium of claim 1, wherein the physical structure is at least one of (i) a biological tissue, (ii) a composite material, (iii) a continuous medium, (iv) a random medium, (v) porous medium, or (vi) porous rocks.
14. (Original) The computer-accessible medium of claim 1, wherein the particular component is based on diffusion magnetic resonance (dMR) image of the at least one physical structure.
15. (Original) The computer-accessible medium of claim 1, wherein the invariants are associated with at least one parameter of at least one tissue
16. A non-transitory computer-accessible medium having stored thereon computer- executable instructions for determining invariants associated with at least one physical structure, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising: receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information.
24. (Original) A non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining at least one component of at least one tensor associated with at least one physical structure, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising: receiving first information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; receiving second information related to at least one constraint on the at least one component of the at least one tensor; and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information.
31. (Original) A method for determining invariants associated with at least one physical structure, comprising: PATENT receiving at least one particular component which is at least one of a component of a diffusion tensor or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure; and generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component.
46. (Original) A method for determining invariants associated with at least one physical structure, comprising: receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information.
54. (Original) A method for determining at least one component of at least one tensor associated with at least one physical structure, comprising: receiving first information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; receiving second information related to at least one constraint on the at least one component of the at least one tensor; and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information.
1. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining a plurality of tissue parameters of at least one tissue, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising:
receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one tissue, wherein the dMR image is based on a plurality of scan parameters; selecting a subset of imaging voxels that contains at least one voxel in at least one image; generating protocol-specific and tissue-specific components of a signal in the selected subset of imaging voxels, wherein, when at least one scan parameter is modified, diffusion directions are under sampled; and
generating the at least one rotational invariant of the signal based on the at least one tissue-specific component of the signal in the at least one voxel of the selected subset of imaging voxels.
(See Claim 1: tissue)
(See Claim 1: diffusion magnetic resonance (dMR) image)
(See Claim 1: tissue)
(See Claim 1)
(See Claim 1)
(See Claim 1)
(See Claim 1)
(See Claim 1)
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.
Claim(s) 1, 13-16, 24, 31, 46, 54 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by P.J. BASSER, C. PIERPAOLI: "Estimating the Principal Diffusivities (Eigenvalues) of the Effective Diffusion Tensor'', PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE, vol. 5, 12 April 1997 (1997-04-12), XP040658477.
Regarding claim 1, BASSER discloses “receiving at least one particular component which is at least one of a component of a diffusion tensor (Introduction: diffusion tensor D) or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure; and generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component (Methods: estimate D and then calculate eigenvalues of D).”
Regarding claim 13, BASSER discloses “wherein the physical structure is at least one of (i) a biological tissue, (ii) a composite material, (iii) a continuous medium, (iv) a random medium, (v) porous medium, or (vi) porous rocks.” (Introduction: tissues; Results and Discussions: human brain, cortical tissue)
Regarding claim 14, BASSER discloses “wherein the particular component is based on diffusion magnetic resonance (dMR) image of the at least one physical structure.” (Introduction: Diffusion tensor MRI (DT-MRI))
Regarding claim 15, BASSER discloses “wherein the invariants are associated with at least one parameter of at least one tissue[.]”
Regarding claim 16, BASSER discloses “receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information.” (Methods: estimate D and then calculate eigenvalues of D)
Regarding claim 24, BASSER discloses “receiving first information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure (Introduction: Diffusion tensor MRI (DT-MRI)); receiving second information related to at least one constraint on the at least one component of the at least one tensor; and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information.” (Methods: estimate D and then calculate eigenvalues of D)
Regarding claim 31, BASSER discloses “receiving at least one particular component which is at least one of a component of a diffusion tensor or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure; and generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component.” (Methods: estimate D and then calculate eigenvalues of D)
Regarding claim 46, BASSER discloses “receiving information related to at least one diffusion magnetic resonance (dMR) image (Introduction: Diffusion tensor MRI (DT-MRI)) of the at least one physical structure; and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information.” (Methods: estimate D and then calculate eigenvalues of D)
Regarding claim 54, BASSER discloses “receiving first information related to at least one diffusion magnetic resonance (dMR) image (Introduction: Diffusion tensor MRI (DT-MRI)) of the at least one physical structure; receiving second information related to at least one constraint on the at least one component of the at least one tensor; and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information.” (Methods: estimate D and then calculate eigenvalues of D)
Claim(s) 1, 10-11, 13-16, 24, 31, 46 and 54 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Topgaard, et al. (US 2016/0356873).
Regarding claim 1, Topgaard discloses “receiving at least one particular component which is at least one of a component of a diffusion tensor or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); and generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component (paragraphs 0020-0021, 0085, 0041-0044).”
Regarding claim 10, Topgaard discloses “wherein the computer arrangement is configured to generate compartmental tensor covariances associated with tissue parameters.” (paragraph 0136)
Regarding claim 11, Topgaard discloses “wherein the compartmental tensor covariances include size-size covariance, shape-shape covariance, and size-shape covariance.” (paragraph 0136)
Regarding claim 13, Topgaard discloses “wherein the physical structure is at least one of (i) a biological tissue, (ii) a composite material, (iii) a continuous medium, (iv) a random medium, (v) porous medium, or (vi) porous rocks.” (paragraph 0002: porous materials, brain tissue)
Regarding claim 14, Topgaard discloses “wherein the particular component is based on diffusion magnetic resonance (dMR) image of the at least one physical structure.” (paragraph 0003: diffusion MRI)
Regarding claim 15, Topgaard discloses “”wherein the invariants are associated with at least one parameter of at least one tissue[.] (paragraph 0002: brain tissue)
Regarding claim 16, Topgaard discloses “receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information (paragraphs 0020-0021, 0085, 0041-0044).”
Regarding claim 24, Topgaard discloses “receiving first information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure; receiving second information related to at least one constraint on the at least one component of the at least one tensor (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information (paragraphs 0020-0021, 0085, 0041-0044).”
Regarding claim 31, Topgaard discloses “receiving at least one particular component which is at least one of a component of a diffusion tensor or a component of a covariance tensor, wherein the at least particular component is associated with the at least one physical structure (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); and generating the invariants of at least one of the diffusion tensor or the covariance tensor based on the particular component (paragraphs 0020-0021, 0085, 0041-0044).”
Regarding claim 46, Topgaard discloses “receiving information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); and generating the invariants of at least one of a diffusion tensor or covariance tensors using (i) at least one of a particular number or particular directions of diffusion acquisitions, and (ii) the information (paragraphs 0020-0021, 0085, 0041-0044).”
Regarding claim 54, Topgaard discloses “receiving first information related to at least one diffusion magnetic resonance (dMR) image of the at least one physical structure (paragraphs 0020-0021, 0034, 0041-0046, 0089, 0091, 0110); receiving second information related to at least one constraint on the at least one component of the at least one tensor; and generating the at least one component which is at least one of a component of a diffusion tensor and a component of a covariance tensor based on the first information and the second information (paragraphs 0020-0021, 0085, 0041-0044).”
Allowable Subject Matter
Claims 2-9 and 12 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sughrue, et al. (US 2023/0108272), Tian, et al. (US 2022/0179030), Pierpaoli, et al. (US 5,969,524) and Basser, et al. (US 5,539,310) teach a method and/or system to receive a diffusion sensor and generate an invariant.
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/RODNEY E FULLER/Primary Examiner, Art Unit 2852
July 29, 2026
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