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 .
Terminal Disclaimer
2. The Terminal Disclaimer filed on 03/11/2026 is acknowledged.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the claims are directed toward non-statutory subject matter, as shown below:
STEP 1: Do the claims fall within one of the statutory categories? Yes.
Claims 1-19 are directed towards a method, i.e., process.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
a. Mathematical concepts — mathematical relationships, mathematical formulas or equations, mathematical calculations;
b. Certain methods of organizing human activity - fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
c. Mental processes - concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1 is directed to the abstract idea and/or mental steps as follows:
First Prong:
(a) accessing with a computer system, magnetic resonance imaging (MRI) data acquired from a subject with an MRI system;
(b) accessing with the computer system, magnetic resonance fingerprinting (MRF) data acquired from the subject, wherein the MRF data comprise quantitative parameter maps;
(c) generating labeled MRI data and labeled MRF data with the computer system by identifying tumor regions in the MRI data and the MRF data and labeling the identified tumor regions;
(d) performing radiomic analysis on the labeled MRI data and the labeled MRF data using the computer system, generating output as radiomic feature data; (hereinafter mentioned as “Mental-Steps/Calculations’).
(These limitations can be performed by mental steps using mathematical formulas that can also be performed using a general processor)
Second Prong:
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
The claimed Mental-Steps/Calculations above is neither implemented into any practical application (device or thing), nor effect any transformation/reduction of a particular article to a different state or thing.
STEP 2B: The Additional elements “generating a report based on the radiomic feature data using the computer system” in the independent claim 1 could be consider as not significantly more than the abstract idea because “generating a report based on the radiomic feature data using the computer system” appears to be post-solution activity and data gathering required to implement the abstract idea of preprocessing and data gathering. These additional elements do not amount to significantly more than the abstract idea. Furthermore “generating a report based on the radiomic feature data using the computer system” is insignificant extra solution activity and is routine, conventional and well known in the art. US PG Pub 2020/0341102 A1 discloses generating a report based on the radiomic feature data using the computer system in paragraph [0040]; US PG-Pub 2020/0341092 A1 discloses generating a report based on the radiomic feature data using the computer system in paragraph [0053].
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 2 depends on claim 1, therefore, it has the abstract idea and also has the routine and conventional structure above said claims.
Furthermore, Claim 2 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these/this limitation(s) are/is simply routine and conventional structures previously known to the pertinent industry that serve to generate the data to be processed by implementing the idea on a computer, and/or recitation of generic computer structure and also serve to perform generic computer functions that are well- understood routine, and conventional activities previously known to the pertinent industry.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 3 depends on claim 1,
therefore, it has the abstract idea and also has the routine and conventional structure above said claims.
In addition, claim 3 is further recites the element(s) “wherein the radiomic feature data comprise at least one of shape data, first-order statistical feature data, or second-order statistical feature data’, which are/is simply more calculations/mental-steps, value numbers, insignificant extra solution activity(s), routine and/or conventional structure(s) previously known to the pertinent industry.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 9 depends on claim 8, therefore, it has the abstract idea and also has the routine and conventional structure above said claims.
In addition, claim 4 is further recites the element(s) “wherein the shape data comprise at least one of volume or surface area of regions-of-interest in the MRI data and the MRF data’, which are/is simply more calculations/mental-steps, value numbers, insignificant extra solution activity(s), routine and/or conventional structure(s) previously known to the pertinent industry.
Furthermore, Claim 4 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these/this limitation(s) are/is simply routine and conventional structures previously known to the pertinent industry that serve to generate the data to be processed by implementing the idea on a computer, and/or recitation of generic computer structure and also serve to perform generic computer functions that are well- understood routine, and conventional activities previously known to the pertinent industry.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 5 depends on claim 3,
therefore, it has the abstract idea and also has the routine and conventional structure above said claims.
In addition, claim 5 is further recites the element(s) “wherein the first-order statistical feature data comprise at least one of mean or variance of image values within the identified tumor regions in the labeled MRI data and the labeled MRF data”, which are/is simply more calculations/mental-steps, value numbers, insignificant extra solution activity(s), routine and/or conventional structure(s) previously known to the pertinent industry.
Furthermore, Claim 5 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these/this limitation(s) are/is simply routine and conventional structures previously known to the pertinent industry that serve to generate the data to be processed by implementing the idea on a computer, and/or recitation of generic computer structure and also serve to perform generic computer functions that are well- understood routine, and conventional activities previously Known to the pertinent industry.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 6 depends on claim 3,
therefore, it has the abstract idea and also has the routine and conventional structure above said claims.
In addition, claim 6 is further recites the element(s) “wherein the second-order statistical feature data comprise at least one gray-level co-occurrence matrix-based features, gray-level run length matrix-based features, gray-level size zone matrix-based features, neighborhood gray tone difference matrix-based features, or gray level dependence matrix-based features computed for image values within identified tumor regions in the labeled MRI data and the labeled MRF data”, which are/is simply more calculations/mental-steps, value numbers, insignificant extra solution activity(s), routine and/or conventional structure(s) previously known to the pertinent industry.
Furthermore, Claim 6 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these/this limitation(s) are/is simply routine and conventional structures previously known to the pertinent industry that serve to generate the data to be processed by implementing the idea on a computer, and/or recitation of generic computer structure and also serve to perform generic computer functions that are well- understood routine, and conventional activities previously Known to the pertinent industry.
Dependent claim(s) 7-19 when analyzed as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claim(s) 7-19 the steps, under the broadest reasonable interpretation, are further refinements of (organizing human activities, mental process, mathematical concepts/formulas) because these steps further describe the intermediary steps of the underlying process.
In all the dependent claim(s), the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. This is because the claim(s) do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Thus, the claims as a whole, do not amount to significantly more than the
abstract idea itself. For these reasons, the dependent claim(s) also are not patent eligible.
Dependent claim(s) 2-19 do not add any limitations that would remedy the deficiencies outlined above and are rejected accordingly.
Claim Rejections - 35 USC § 102
5. 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.
6. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-5, 12, 15-19 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by BOERNERT et al. U.S. Patent Application No. US 2021/0109180 (hereinafter BOERNERT).
Regarding claim 1, BOERNERT discloses a method for radiomic analysis of magnetic resonance fingerprinting (MRF) data (Machine-executable instructions cause the processor to select a set of magnetic resonance fingerprinting dictionaries for each of the anatomical regions; paragraphs 7, 64, Figure 1. See also rest of reference), the method comprising:
(a) accessing with a computer system, magnetic resonance imaging (MRI) data acquired from a subject with an MRI system (A magnetic resonance imaging system 100 with a magnet 104 with sections to allow access to perform magnetic resonance imaging; paragraph 60. See also the rest of reference);
(b) accessing with the computer system, magnetic resonance fingerprinting (MRF) data acquired from the subject, wherein the MRF data comprise quantitative parameter maps (Containing an MRF signal 148 for a voxel constructed from the intermediate images 146. The computer memory 134 may also contain a B1+ mapping 150 and also possibly a B0 mapping which may be used to correct the intermediate images 146; paragraphs 66-68 and see also rest of reference);
(c) generating labeled MRI data and labeled MRF data with the computer system by identifying tumor regions in the MRI data and the MRF data and labeling the identified tumor regions (Using a magnetic resonance fingerprinting dictionary for detecting abnormal voxels and correcting them during magnetic resonance fingerprinting. This may be used to identify voxels which are potentially abnormal, an analysis for the T1 values. The curve 500 is the normal or expected T1 value for the white matter and 502 shows a distribution of measured values of T1; paragraph 90, Figures 3-5. See also rest of reference);
(d) performing radiomic analysis on the labeled MRI data and the labeled MRF data using the computer system, generating output as radiomic feature data (Measurements of radio frequency signals emitted by atomic spins using a magnetic resonance apparatus during a magnetic resonance imaging scan. A Magnetic Resonance (MR) image is defined herein as being the reconstructed dimensional visualization of anatomic data contained within the magnetic resonance imaging data; paragraph 51 and see the rest of reference); and
(e) generating a report based on the radiomic feature data using the computer system (A detailed report is conceivable, containing information about the size/volume and the tissue composition of different organs or organ sub-regions; paragraph 87).
Regarding claim 2, BOERNERT (US 20210109180) discloses the method of claim 1, wherein the tumor regions comprise at least one of necrotic core, enhancing tumor, peritumoral white matter, or whole tumor regions (a T1 distribution 400 that could be expected within the white matter of the brain; paragraph 90, Figures 3-6).
Regarding claim 3, BOERNERT discloses the method of claim 1, wherein the radiomic feature data comprise at least one of shape data, first-order statistical feature data, or second-order statistical feature data (a detailed report is conceivable, containing information about the size/volume and the tissue composition of different organs or organ sub-regions; paragraph 87, Figures 3-6 and see the rest of reference).
Regarding claim 4, BOERNERT discloses the method of claim 3, wherein the shape data comprise at least one of volume or surface area of the identified tumor regions in the labeled MRI data and the labeled MRF data (Detecting abnormal voxels and correcting them during magnetic resonance fingerprinting; paragraphs 86-90, Figures 3-6 and see the rest of reference).
Regarding claim 5, BOERNERT discloses the method of claim 3, wherein the first-order statistical feature data comprise at least one of mean or variance of image values within the identified tumor regions in the labeled MRI data and the labeled MRF data (paragraphs 7, 67, 73, 90 and Figures 3-6 illustrating 300 MRF composition mapping or image 400 normal T2 distribution , 402 measured T2 distribution , 404 due to abnormal voxels, 500 measured T1 distribution, 502 measured T2 distribution and 600 corrected MRF composition mapping or image 602 potential pathological structure).
Regarding claim 12, BOERNERT discloses the method of claim 1, wherein generating the report comprises computing statistical features of the radiomic feature data and displaying the statistical features to a user using the computer system (Reconstructed two- or three-dimensional visualization of anatomic data contained within the magnetic resonance imaging data and shown a composition mapping 300 or image from magnetic resonance fingerprinting. CSF, white, and gray matter are displayed in a T1 distribution 400 that could be expected within the white matter of the brain; see at least paragraphs 51, 90, Figures 3-6).
Regarding claim 15, BOERNERT discloses the method of claim 1, wherein identifying tumor regions in the MRI data and the MRF data and labeling the identified tumor regions comprises segmenting the MRI data and the MRF data (paragraphs 73-75 and see the rest of reference).
Regarding claim 16, BOERNERT discloses the method of claim 1, wherein the MRI data comprise multi-contrast MRI data comprising images acquired with different contrast weightings (MRI has a great soft tissue contrast and is one of the most versatile imaging modalities. Quantitative MR techniques are desirable to reduce the huge variety of contrasts and/or to make findings more comparable to draw diagnostic conclusions; paragraphs 73, 81, 90 and see the rest of reference).
Regarding claim 17, BOERNERT discloses the method of claim 16, wherein the different contrast weightings include at least two of T1-weighting, contrast-enhanced T1-weighting, T2-weighting, fluid attenuation inversion recovery ("FLAIR") contrast, proton density-weighting, diffusion- weighting, or susceptibility-weighting (paragraphs 81, 90 and see the rest of reference).
Regarding claim 18, BOERNERT discloses the method of claim 1, wherein the MRF data comprise parametric maps computed using a magnetic resonance fingerprinting technique (Using this multi-parametric information, anatomical regions are identified by matching an anatomic model to the image data. The multi-parametric, quantitative, and perfectly co-registered nature of the MRF maps allows for very accurate matching of the anatomic model; paragraphs 80-85).
Regarding claim 19, BOERNERT discloses the method of claim 18, wherein the parametric maps comprise at least one of magnetization (Mo) maps, longitudinal relaxation time (T1) maps, transverse relaxation time (T2) maps, diffusion parameter maps, or perfusion parameter maps (a T1 distribution 400 that could be expected within the white matter of the brain. The line 402 shows the actual measured T2 distribution for. It can be seen; paragraph 90, Figures 3-6 and see the rest of reference).
Claim Rejections - 35 USC § 103
8. 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.
9. Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over BOERNERT in view of McGivney et al. U.S. Patent Application No. US 2019/0353738 (hereinafter McGivney).
Regarding claim 6, BOERNERT discloses the method of claim 3.
BOERNERT does not explicitly disclose wherein the second-order statistical feature data comprise at least one gray-level co-occurrence matrix-based features, gray-level run length matrix-based features, gray-level size zone matrix-based features, neighborhood gray tone difference matrix-based features, or gray level dependence matrix-based features computed for image values within identified tumor regions in the labeled MRI data and the labeled MRF data.
However, McGivney working in the same field teaches wherein the second-order statistical feature data comprise at least one gray-level co-occurrence matrix-based features, gray-level run length matrix-based features, gray-level size zone matrix-based features, neighborhood gray tone difference matrix-based features, or gray level dependence matrix-based features computed for image values within identified tumor regions in the labeled MRI data and the labeled MRF data (Texture features are determined for each ROI of the quantitative maps. In an embodiment, second order texture features may be calculated for each ROI using Haralick texture features. In an embodiment, twenty-six texture features were calculated. The Haralick texture features may be calculated or derived using either Gray Level Co-occurrence Matrices (GLCM) or Gray Level Run Length Matrices (GLRLM); paragraph 42, Figures 3-5).
In view of the above, it would have been obvious to one having ordinary skill in the art at the time of the invention was made to combine the system of BOERNERT as taught by McGivney to include: wherein the second-order statistical feature data comprise at least one gray-level co-occurrence matrix-based features. By doing so, the combined system of McGivney would have generated qualitative images. Thus, providing statistical information undetectable to the naked eye would improve (paragraph 0007 of McGivney).
Regarding claim 7, BOERNERT discloses the method of claim 6.
BOERNERT does not explicitly disclose wherein step (d) comprises computing a gray- level co-occurrence matrix (GLCM) from at least one of the labeled MRI data or the labeled MRF data in a given labeled region, computing the second-order statistical feature data from the GLCM, and storing the second-order statistical feature data as the radiomic feature data.
However, McGivney working in the same field teaches wherein step (d) comprises computing a gray- level co-occurrence matrix (GLCM) from at least one of the labeled MRI data or the labeled MRF data in a given labeled region, computing the second-order statistical feature data from the GLCM, and storing the second-order statistical feature data as the radiomic feature data (paragraph 43, Figures 3-5).
Regarding claim 8, BOERNERT discloses the method of claim 6.
BOERNERT does not explicitly disclose wherein step (d) comprises computing a gray- level run length matrix (GLRLM) from at least one of the labeled MRI data or the labeled MRF data in a given labeled region, computing the second-order statistical feature data from the GLRLM, and storing the second-order statistical feature data as the radiomic feature data.
However, McGivney working in the same field teaches wherein step (d) comprises computing a gray- level run length matrix (GLRLM) from at least one of the labeled MRI data or the labeled MRF data in a given labeled region, computing the second-order statistical feature data from the GLRLM, and storing the second-order statistical feature data as the radiomic feature data (paragraph 44, Figures 3-5).
10. Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over BOERNERT in view of Eck et al. U.S. Patent Application No. US 2020/0341102 (hereinafter Eck).
Regarding claim 13, BOERNERT discloses the method of claim 1.
BOERNERT does not explicitly disclose wherein the report comprises a quantitative score of a prediction of peritumoral infiltration for the subject.
However, Eck working in the same field of endeavor teaches wherein the report comprises a quantitative score of a prediction of peritumoral infiltration for the subject (paragraphs 9-10, 19, 34, 40, 42 and see the rest of reference).
In view of the above, it would have been obvious to one having ordinary skill in the art at the time of the invention was made to combine the system of BOERNERT as taught by Eck to include: wherein the report comprises a quantitative score of a prediction of peritumoral infiltration for the subject. By doing so, the combined system of Eck would have generated a desired field of view for magnetic resonance fingerprinting. Thus, suppressing out-of-view artifacts would improve.
Regarding claim 14, BOERNERT discloses the method of claim 1.
BOERNERT does not explicitly disclose wherein the report comprises a quantitative score of a prediction of peritumoral recurrence for the subject.
However, Eck working in the same field of endeavor teaches wherein the report comprises a quantitative score of a prediction of peritumoral recurrence for the subject (paragraphs 9-10, 19, 34, 40, 42 and see the rest of reference).
In view of the above, it would have been obvious to one having ordinary skill in the art at the time of the invention was made to combine the system of BOERNERT as taught by Eck to include: wherein the report comprises a quantitative score of a prediction of peritumoral recurrence for the subject. By doing so, the combined system of Eck would have generated a desired field of view for magnetic resonance fingerprinting. Thus, suppressing out-of-view artifacts would improve.
Allowable Subject Matter
11. Claims 9-11 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
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN H NGUYEN whose telephone number is (571)270-1229. The examiner can normally be reached M-F 7 am-4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDERRAHIM MEROUAN can be reached at (571) 270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN H NGUYEN/Primary Examiner, Art Unit 2683
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