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.
Response to Arguments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Griswold et al (Griswold hereinafter US 20150301141 A1) in view of Piredda et al (Piredda hereinafter “Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology”)
As per claim 1
Griswold teaches A method for detecting and quantifying an abnormal state of a tissue of a biological object, the method comprising: acquiring multiple quantitative maps of the biological object, wherein each of the multiple quantitative maps is a quantitative of a different physical property of the biological object: (Paragraph [0031] “Example apparatus and methods distinguish normal tissue from abnormal (e.g., diseased) tissue using the MR parameter (e.g., T1, T2, M0) maps derived from MRF” Paragraph [0035] “Tissue relaxation parameters T1 and T2, or the corresponding reciprocal rates R1 and R2, may be identified for voxels in a volume. The volume may be, for example, a portion of a human anatomy. The relaxation parameters may be acquired using MRF”) combining the multiple quantitative maps into a combined quantitative map (CQM) (Figures 11-13. Paragraph [0095] “Producing the overlays and then merging (e.g., super-imposing) an overlay on an MR map produces figures like those illustrated in FIGS. 11-13. In FIGS. 11-13, pixels with the T1/T2 values assigned to the clusters were superimposed on top of the T1 map to identify structures in the brain that corresponded to the tissue types associated with the clusters.” Paragraph [0103] “An overlay may be, for example, a displayable image that can be merged with another displayable image (e.g., an MR map)” paragraph [0112] “Overlay logic 1520 may combine different overlays with MR map 1505 to produce, for example, overlaid maps 1530, 1540, and 1550. “
Griswold does not teach calculating a deviation map by comparing the CQM to a normative atlas configured for providing expected values for the CQM .
Piredda also teaches method for detecting and quantifying an abnormal state of a tissue of a biological object, the method comprising: acquiring multiple quantitative maps of the biological object, wherein each of the multiple quantitative maps is a quantitative of a different physical property of the biological object: (Figure 1) combining the multiple quantitative maps into a combined quantitative map (Figure 1 “ First, for each data set, the T2 map is rigidly registered to the T1 map “).
In regards to the limitation of “calculating a deviation map by comparing the CQM to a normative atlas configured for providing expected values for the CQM”.
Piredda calculates a deviation map by comparing the separate T1 and T2 maps to a normative atlas configured for providing expected values for the quantitative maps (Figure 8, Results: “T1 and T2 z-score deviation maps for a healthy subject (included in HC), a healthy volunteer with an EPVS (not included in HC), and two MS patients are shown in Figure 8.”)
Piredda and Griswold teach complementary quantitative MRI analysis techniques directed toward improving tissue abnormality detection using quantitative information. Although Piredda performs the comparison (Deviation map) using individual analyzed T1 and T2 maps, a person or ordinary skill in the art would have been motivated to apply Piredda normative atlas comparison techniques to the combined quantitative map generated by Griswold because Griswold expressly teaches that combining quantitative MR parameters improves tissue classification and structural identification beyond the use of isolated relaxation parameters alone. Griswold explains in paragraph [0003] that “the images are only as good as the image interpreter and all image based (e.g., qualitative) diagnoses end up being subjective.” And being able to identify “other properties including, but not limited to, tissue types, materials, and super-position of attributes (e.g., T1, T2) “ may be beneficial over qualitative analysis. A person of ordinary skill in the art would have recognized that comparing the richer multiparametric combined quantitative map against normal healthy atlas values would improve pathological tissue detection by the enablement of evaluation of correlated abnormalities across multiple quantitative magnetic resonance properties simultaneously. The limitation of “calculating a deviation map by comparing the CQM to a normative atlas configured for providing expected values for the CQM” is bridged by this obvious combination.
Furthermore, Piredda says in their discussion section that “Contrary to the deviations shown in focal MS lesions, the cerebrospinal and interstitial fluids inside the EPVS revealed a higher T2 deviation (mean
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= 27.34) than T1 (mean
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= 8.77). This exemplifies that the use of z-scores as deviation measures allows relating the amplitude of the changes in T1 and T2 to each other; combining these “abnormality” metrics may therefore enable their use as biomarkers to classify different kinds of brain tissue alterations. For instance, previous attempts in the differentiation of active and non-active lesion by the means of relaxation times difference were only partially successful.10 The quality of the quantitative maps and the coherence of the data set built in this study may be of help in fulfilling this goal.” Alluding to the need to have deviation maps corresponding to combined quantitative maps.
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to modify Griswold’s method of combining quantitative maps with Piredda’s known deviation analysis technique. Griswold’s combined quantitative map provides integrated multiparametric tissue characterization while Piredda’s normative at las provides expected healthy quantitative values for detecting abnormal deviations. Combining these references would have predictably resulted in calculating a deviation map by comparing the combined quantitative map to normative atlas values in order to boost sensitivity specificity and robustness of pathological tissue detection and characterization.
As per claim 2
Griswold and Piredda teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Griswold teaches wherein the combining step comprises performing an algebraic operation on the acquired quantitative maps for combining the quantitative maps with on another (Paragraph [0095] Producing the overlays and then merging (e.g., super-imposing) an overlay on an MR map produces figures like those illustrated in FIGS. 11-13. In FIGS. 11-13, pixels with the T1/T2 values assigned to the clusters were superimposed on top of the T1 map to identify structures in the brain that corresponded to the tissue types associated with the clusters. ” Examiner notes “superimposing” as an algebraic operation. To stack and superimpose MRI maps, a processer must perform pixel by pixel math using algorithms to compute matrices or compute physical properties. Superposition of pixels is mathematically equivalent to adding matrices or plotting intersecting vectors. )
As per claim 3
Griswold and Piredda teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Griswold teaches combining the quantitative maps voxel-wise or region wise (Paragraph [0035] “issue relaxation parameters T1 and T2, or the corresponding reciprocal rates R1 and R2, may be identified for voxels in a volume. The volume may be, for example, a portion of a human anatomy…Maps with overlays that depend on data located in different clusters may then be produced.” Paragraph [0036] “While tissues and brains are described, more generally, clusters of regions (e.g., pixels, voxels) having related signal evolutions may be produced and overlaid onto an MR map” This shows Griswold is working in voxel and or region wise when combination maps are made)
As per claim 4
Griswold and Piredda teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Piredda teaches obtaining the normative atlas by performing the acquiring and combining steps for each normative biological object of a group of normative biological objects to thereby obtain a normative CQM for each biological object (Figure 1, T1 and T2 brain normative atlases were estimated by modeling the relaxation time variability within the healthy cohort (HC) using a linear regression model in each voxel. The impact of the subjects' sex and age on the physical properties was accounted for by using them as predictive variable). Generating the normative atlas by mathematically combining the normative CQM’S (Equations 1-4) Examiner notes that with Griswold’s overlay in mind, a person of ordinary skill in the art is aware that these calculations and comparisons would be done with a overlayed quantitative map.
As per claim 5
Piredda teaches computing the deviation map as a z-score map (Figure 8) wherein a difference between a measured value obtained for a first specific location within the CQM and an expected value obtained for a corresponding specific location within the normative atlas is divided by a root mean square error of residues of a mathematical model used for creating the normative atlas (Section 2.4 Population-derived norms “For each model, the root mean squared error (RMSE) was evaluated as an estimation of the SD of the residuals error across the linear model.” Section 2.5 Method of single-subject comparison Equation 3 and 4. Examiner notes that a person of ordinary skill in the art is aware that within the Griswold/Piredda workflow these calculations are done with the overlayed quantitative map parameters)
As per claim 6
Griswold and Piredda teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Griswold teaches wherein the acquired quantitative maps are MRI quantitative maps acquired by an MRI apparatus (Figure 8 Paragraph [0022] “Example methods and apparatus facilitate identifying tissue types based on clusters of data identified using magnetic resonance (MR) parameters retrieved from magnetic resonance fingerprinting (MRF). More generally, example methods and apparatus facilitate distinguishing groups of materials in a volume based on MR parameter-based clusters identified from MR parameters retrieved using MRF. MRF simultaneously provides quantitative maps of multiple MR parameters. The quantitative mapping uses different combinations of MR parameters of interest, (e.g., T1, T2, off-resonance).”
As per claim 7
Griswold and Piredda teach all claim limitations previously rejected in claim 6’s 103 rejection. See claim 6’s 103 rejection.
Griswold teaches wherein the multiple quantitative maps are T1 qMRI and a T2 qMRI map (Paragraph [0031] “Example apparatus and methods distinguish normal tissue from abnormal (e.g., diseased) tissue using the MR parameter (e.g., T1, T2, M0) maps derived from MRF” Paragraph [0035] “Tissue relaxation parameters T1 and T2, or the corresponding reciprocal rates R1 and R2, may be identified for voxels in a volume” Paragraph [0095] “Producing the overlays and then merging (e.g., super-imposing) an overlay on an MR map produces figures like those illustrated in FIGS. 11-13. In FIGS. 11-13, pixels with the T1/T2 values assigned to the clusters were superimposed on top of the T1 map “
As per claim 9
Griswold and Piredda teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Claim 9 is the parallel system claim of method claim 1 and will be rejected under the same premise.
Furthermore, Griswold teaches a processing unit, interface and memory (Figure 8)
As per claim 10
Griswold and Piredda teach all claim limitations previously rejected in claim 9’s 103 rejection. See claim 9’s 103 rejection.
Griswold teaches a display (figure 8)
Conclusion
Claim 8 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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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