Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12, 15 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because “A computer software product, comprising a computer-readable medium” given the broadest reasonable includes a transient computer-readable medium, e.g. a carrier wave, which is non-statutory subject matter.
Dependent claims 17-20 do not fix this above deficiency.
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) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kober et al. (EP 3767634), hereinafter referred to as Kober.
With reference to claim 1, Kober teaches A method of synthesizing a magnetic resonance (MR) image, the method comprising: obtaining a quantitative MRI (qMRI) map of values of an MRI parameter (¶0017); modulating values of said MRI parameter within a region of said qMRI map, to mimic a tissue pathology therein, thereby providing a modulated qMRI map (¶0021-0022, ¶0025); and generating an MR image based on said modulated qMRI map, thereby synthesizing the MR image (¶0022).
With reference to claim 2, Kober further teaches generating said qMRI map (¶0017). With reference to claim 3, Kober further teaches said generating said qMRI map is based on an MR signal acquired from a subject (¶0017). With reference to claim 4, Kober further teaches said subject is a healthy subject (¶0017). With reference to claim 5, Kober further teaches accessing a computer readable medium storing a database having a plurality of entries each associating a database pathology to a database value or range of values of at least one MRI parameter, and searching said database for an entry having a database pathology matching said tissue pathology, wherein said modulating said values of said parameter is based on a database value or range of values of said found entry (¶0025). With reference to claim 6, Kober further teaches randomly selecting said region (¶0024). With reference to claim 7, Kober further teaches said modulating is along a randomly selected pattern within said region (¶0024, ¶0025). With reference to claim 8, Kober further teaches said region is predetermined (¶0022). With reference to claim 9, Kober further teaches accessing a computer readable medium storing a database having a plurality of entries each associating a database pathology to a database morphology, and searching said database for an entry having a database pathology matching said tissue pathology, wherein said modulating said values within said region is along a pattern selected based on a database morphology of said found entry (¶0025). With reference to claim 10, Kober further teaches receiving input pertaining to a severity level of said tissue pathology, wherein said modulating said values is based on said received severity level (¶0025). With reference to claim 11, Kober further teaches generating a simultaneous graphical output of said synthesized the MR image, and an MR image corresponding to said qMRI map prior to said modulation (¶0024, ¶0025). With reference to claim 12, Kober further teaches A computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a data processor, cause the data processor to receive a qMRI map of values of at least one MRI parameter and execute the method according to claim 1 (¶0025). With reference to claim 13, Kober further teaches a method of training an artificial neural network, comprising: executing a method of synthesizing a magnetic resonance (MR) image a plurality of times to respectively synthesize a plurality of MR images, each associated with at least one tissue pathology; feeding the artificial neural network with said synthesized MR images and said respective tissue pathologies, to obtain weight parameters for the artificial neural network; and storing the weight parameters in a computer readable medium; wherein said method of synthesizing an MR image is the method of claim 1 (¶0025)
With reference to claim 14, Kober further teaches the method according to claim 13, further comprising re-executing said method of synthesizing an MR image an additional plurality of times to respectively synthesize an additional plurality of MR images, each associated with at least one tissue pathology; validating said weight parameters by feeding the artificial neural network with each of said additional plurality of synthesized MR images, and comparing an output of the artificial neural network with a respective tissue pathology; and generating a report indicative of said validation (¶0025). With reference to claim 15, Kober further teaches a computer software product, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a data processor, cause the data processor to receive a qMRI map of values of at least one MRI parameter and execute the method according to claim 13 (¶0025). With reference to claim 16, Kober further teaches a computer software product for training a user, the computer software product comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a data processor, cause the data processor to: display on a display device a graphical user interface (GUI) having a training activation control; automatically execute the method according to claim 1, responsively to an activation of said control by the user; and generate a graphical output of said synthesized the MR image on said GUI (¶0025).
With reference to claim 17, Kober further teaches the computer software product according the claim 16, wherein said program instructions, when read by a data processor, cause the data processor to synthesize an ordered set of MR images mimicking said tissue pathology, and to generate a graphical output separately for each of said MR images on said GUI (¶0025). With reference to claim 18, Kober further teaches the computer software product according to claim 17, wherein said set of MR images comprises synthesized MR images at which a visibility of said synthesized pathology gradually increases or decreases (¶0022). With reference to claim 19, Kober further teaches the computer software product according to claim 18, wherein said set of MR images comprises synthesized MR images at which a severity level of said synthesized pathology gradually increases or decreases (¶0022).
With reference to claim 20, Kober further teaches the computer software product according to claim 18, wherein said set of MR images comprises synthesized MR images at which a size of said region gradually increases or decreases (¶0022).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Banerjee et al. (US 11,880,962 B2) teaches a system and method for synthesizing MR images.
Chatterjee et al. (US 11,808,832 B2) teaches a system and method for deep learning-based generation of true contrast images utilizing synthetic MRI data.
Jara (US 6,823,205 B1) teaches synthetic images for a MRI scanner using linear combination of source images to generate contrast and spatial navigation.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852