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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/09/2024 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 04/22/2024 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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)(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.
Claims 1-4, 6, 9 and 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Du et al (Du hereinafter 20210106250 A1)
As per claim 1
Du teaches acquiring multiple quantitative maps of the biological object wherein each of the multiple quantitative maps is a quantitative map of a different physical tissue property of the biological object (Figure 6 boxes 1 and 2 and Paragraph [0070] “A plurality of co-registered T1W and T2W images can be compiled to generate a MRI contrast image depicting the differences in T1 relaxation of the spins of protons within the tissue and the differences in T2 relaxation of the spins of protons within the tissue at each anatomical location simultaneously.”) and combining the multiple quantitative maps into a combined quantitative map (CQM) (Du shows combining T1W and T2W into a single ratio map for example: Figure 6 box 4 and Paragraph [0011] “The first T1W/T2W ratio map can be generated by dividing each T1W image signal intensity by its co-registered T2W image signal intensity.” A ratio map is in essence a single combined quantitative representation derived from both the T1W and T2W maps i.e. a CQM. ) and calculating a deviation map by comparing the CQM to a normative atlas configured for providing expected values for the CQM (Figure 6 box 8 and Paragraph [0037] “MRI contrast image can be compared to a MRI contrast image template so that a quantitative contrast analysis can be performed to identify differences between T1W/T2W ratio intensities of the Mill contrast image and T1W/T2W ratio intensities of the MM contrast image template.” The difference between the ratio map is being interpreted as equivalent to a deviation map and the contrast image template the normative atlas.
As per claim 2
Du covers all limitations rejected in claim 1’s 102 rejection
Du teaches the combining step comprising the performance of an algebraic operation on the acquired quantitative maps for combining the quantitative maps with one another ( “T1W/T2W ratio map can be generated by dividing each T1W image signal intensity by its co-registered T2W image signal intensity.” )
As per claim 3
Du covers all limitations rejected in claim 1’s 102 rejection
Du teaches combining the quantitative maps voxel wise or region wise (Paragraph [0014] “In some embodiments, the comparing step can involve voxel-based analysis. In other embodiments, the comparing step can involve a region-of-interest based analysis” This comparison is being done on the MRI contrast images versus the T1W/T2W. This means that the ratio map must have voxel wise and or region wise attributes to make even this comparison Du’s ratio map generation must be performed at the voxel level to produce a spatial map.)
As per claim 4
Du covers all limitations rejected in claim 1’s 102 rejection
Du teaches obtaining a normative atlas by acquiring and combining each normative biological object of a group of normative biological objects to thereby obtain a normative CQM for each normative biological object (Paragraph [0049] “A MRI contrast image template can be generated for specimens belonging to a cohort. A cohort can be a group of specimens sharing a same or similar”) and generating the normative atlas by mathematically combining the normative CQMs (Paragraph [0016] “ contrast image template can include a first T1-weighted (T1W)/T2-weighted (T2W) ratio map. The first T1W/T2W ratio map can include a plurality of T1W/T2W ratios for a plurality of anatomical locations of the first specimen's tissue”)
As per claim 6
Du covers all limitations rejected in claim 1’s 102 rejection
Du teaches quantitative maps acquired by an MRI apparatus (Figure 6 “…using a magnetic resonance scanner”)
As per claim 9
Du covers all limitations rejected in claim 1’s 102 rejection
Du discloses a system that has memory, an interface and processing unit
Paragraph Some embodiments of the method can be implemented using a magnetic resonance imaging (MRI) scanner and at least one computer device connected to the scanner. The computer device can include hardware that includes a processor connected to a non-transitory computer readable medium. The medium may have a program and/or application stored thereon that can be run to have the computer device perform an embodiment of the method. A computer system that utilizes an MRI scanner and/or a computer that may have T1W images, T2W images, and MRI images stored in its non-transitory computer readable medium or stored in non-transitory computer readable medium that is connectable to the computer device (e.g. a remote server that has the images and/or other data stored in its non-transitory memory where the server is connectable to the computer device via at least one network having a plurality of nodes (e.g. access points, routers, servers, gateways, etc.).
As per claim 10
Du covers all limitations rejected in claim 9. See claim 9’s 102 rejection
A display is inherent in the MRI/computer system described in claim 9 as anatomical structures shown through MRI inherently need a display to assess.
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.
Claims 5, 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Du et al (Du hereinafter 20210106250 A1) in view of Lipton et al (Lipton hereinafter US 9245334 B2)
As per claim 5
Du teaches all limitations covered in claim 1’s 102 rejection
Lipton teaches computing the deviation map as a z-score map (“The Z-score was computed at each voxel within a subject's FA volume” A value computed “at each voxel” forms a spatial map of deviation values i.e. a “z-score map”), difference between a measured value and an expected value (Lipton teaches comparing subject voxels to control group reference values. Column 3 line 43: “using the computer to compare voxels from the image from the subject being assessed with corresponding voxels from a computer database of images from a control group of subjects,” and Column 7 line 18: “Reference values (mean and Standard Deviation (SD)) for the Z-score computation were computed from the control group.” The subject voxel value is the “measured value” and the control group mean is the ”expected value” (normative atlas value). Lipton further teaches normalization by control group dispersion. Column 7 line 18: “Reference values (mean and Standard Deviation (SD)) for the Z-score computation were computed from the control group. Lipton may not expressly say “RMSE of residues” but a person of ordinary skill in the art would recognize that RMSE of residuals is a standard root mean square measure of model error and dispersion that is mathematically equivalent to standard deviations for normalizing deviations from expected values produced by a statistical model. Because Lipton’s normal atlas is generated by control group statistics, characterizing the dispersion term as RMSE or residuals would have been a variant leading the same normalized deviation metric. Through this Lipton shows an explicit normative reference framework based on a control group and computing expected values (mean) and dispersion(SD) and then computing voxelwise standardized deviation metric (Z-score).
The combined teaching becomes a modified framework where Du supplies the CQM formation (T1W/T2W ratio map) and template comparison framework (comparing ratio map to cohort template to identify differences) and Lipton suppling expected values from a normative group and explicit per voxel deviation computation via z-scoring as the quantitative form of the deviation map.
Du’s concept of “identify differences between ratio intensities of the subject and the template” is modified so that these differences that constitute the deviation map are not just raw differences but are normalized and standardized against expected values derived from a normative population producing a quantitative deviation map (voxelwise Z/EZ score map) per Lipton’s technique.
A person of ordinary skill in the art at the time this invention was effectively filed would have been motivated to incorporate Lipton’s control group expected value and voxelwise Z score computation into Du’s ratio map and template comparison because Du already performs a quantitative comparison between a subject’s ratio map (CQM) and a cohort template (Normative atlas) to identify differences (deviation). Lipton provides a specific proven statical mechanism for turning such differences into a standardized, proven statistical mechanism for turning such deviations into a standardized deviation metric relative to a normal population. This modification applies Lipton’s disclosed normative statistics pipeline to Du’s CQM output in order to achieve a predictable technical benefit: deviation values that are comparable across voxels and subjects, reduced sensitivity to scaling as well as variance and more objective detection/quantification of abnormal tissue than unnormalized template differences.
As per claim 7
Du teaches all limitations previously rejected in claim 6’s 102 rejection. See claim 6’s 102 rejection.
Lipton teaches multiple quantitative maps are a T1 qMRI map and a T2 qMRI map (Column 3 line 63 “the method can be used to compare any quantitative imaging parameter such as…T1,T2 T2* proton density blood flow…” By explicitly naming T1 and T2 as quantitative imaging parameters to be compared, Lipton teaches using T1 quantitative maps and T2 quantitative maps as the multiple quantitative maps)
A person of ordinary skill in the art at the time this invention was effectively filed would have selected T1 and T2 quantitative maps in the Lipton/Du modified workflow because Lipton identifies T1 and T2 as quantitative parameters suitable for voxel wise comparison to normative reference values. Using the two provide complementary tissue property sensitivity which predictably improves the robustness of abnormality detection and quantification compared to relying on a single parameter map or semi-quantitative map.
As per claim 8
Lipton and Du teach all limitations previously rejected in claim 7’s 103 rejection. See claim 7’s 103 rejection.
Lipton teaches the relative quantitative maps T1 and T2 (Column 3 line 63 “the method can be used to compare any quantitative imaging parameter such as…T1,T2 T2* proton density blood flow), This supplies the underlying maps are qMRI type T1 and T2 parameters.
Due teaches a division-based ratio map that combines a quantitative based maps ( “a T1W/T2W ratio map can be generated by dividing each T1W image…by its co registered T2W image”). Using T1/T2 is a reciprocal of T2/T1 and is an obvious algebraic variant once division-based ratio mapping of the two maps are taught.
Accordingly, a person of ordinary skill in the art would have been motivated to make a division-based ratio of the T1 and T2 qMRI maps MRI maps because Du explicitly teaches that dividing a quantitative map by its co-registered map produces a single ratio map rather than treating the maps separately. Applying that same taught ratio map technique to Lipton’s identifiable and explicitly labeled qMRI maps/parameters provides a standardized combined metric that normalizes one relaxation property by the other. This improves comparability and contrast of tissue differences in the combined map. Choosing T2/T1 rather than T1/T2 is a predictable reciprocal ratio that preserves the same underlying coupling of information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667
/TOM Y LU/Primary Examiner, Art Unit 2667