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 Status
Applicant’s amendment filed on July 16, 2026 is acknowledged. Currently claims 1-17 are pending. Claims 1 and 13 have been amended.
Response to Arguments
Applicant's arguments filed July 16, 2026, have been fully considered but they are not persuasive.
On pages 9-10 of Applicant’s remarks, Applicant alleges that Wang discloses a logistic regression algorithm that classifies a candidate target according to extracted features from the candidate target. Applicant further states that Wang does not disclose the logistic regression algorithm generating a regression analysis of a physical quantity on biological tissue based on processed information of spatially correlated voxels and/or pixels. Accordingly, Wang does not teach or disclose a regression unit configured to “generate a regression analysis of the physical quantity on the biological tissue based on the processed information of the spatially correlated voxels, pixels, or voxels, and pixels” as recited by independent claim 1.
The examiner respectfully disagrees. The examiner asserts that Kano in view of Lachner and Wang teaches a regression unit configured to generate a regression analysis of the physical quantity on the biological tissue based on the processed information of the spatially correlated voxels, pixels, or voxels, and pixels (Lachner - [0022] “Accordingly, voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image (and of the normalised difference image, see below) which represent the same part of the anatomical body and have a similar and in particular identical position in the common reference system are referred to as “corresponding voxels”. Preferably, all the voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image which represent the same part of the anatomical body have a similar and in particular identical position in the common reference system.”) (Wang - [0100] “The classification unit 380 may generate a classification result by classifying a candidate target according to the extracted features from the candidate target. In some embodiments, a classification process may include performing data prediction by mapping features of a candidate target to a specific category based on a classification model or function. A classifier may correspond to a classification technique. In some embodiments, the classification technique may include a supervised technique. The supervised technique may include identifying a pending sample according to a specific rule and element features of the pending sample, and classifying the element features of the pending sample into a category of training samples based on similar features to the pending sample. The supervised technique may include a linear discriminant algorithm, an artificial neural network algorithm, a Bayes classification algorithm, a support vector machine (SVM) algorithm, a decision tree algorithm, a logistic regression algorithm, or the like, or a combination thereof.” wherein a regression unit is the classification unit) (Wang - [0082] “In some embodiments, the machine learning technique may include a regression algorithm, a case learning algorithm, a formal learning algorithm, a decision tree learning algorithm, a Bayesian learning algorithm, a kernel learning algorithm, a clustering algorithm, an association rules learning algorithm, a neural network learning algorithm, a deep learning algorithm, a dimension reduction algorithm, etc. The regression algorithm may include a logistic regression algorithm, a stepwise regression algorithm, a multivariate adaptive regression splines algorithm, a locally estimated scatterplot smoothing algorithm, etc.”) (Kano - [0043] “([0043] “Further, the three-dimensional information converting section 222 classifies each voxel of the three-dimensional brain image according to the type of tissue, such as gray matter, white matter, and the like (Step S13).”) ([0047] “Further, the standard brain converting section 223 calculates the volume and the degree of shrinkage of each voxel in the spatial coordinate system of the standard brain (Step S23), and extracts a feature amount resulting from the calculated volume and degree of shrinkage of each part (Step S24).”) (Kano - [0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a regression analysis of Wang of the physical quantity on the biological tissue based on the spatially correlated voxels of Kano in view of Lachner to elucidate a relationship between the spatially correlated voxels and other biological variables thereby identifying biologically relevant quantitative patterns.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are:
“a computation module”, “a digitization unit”, “a concatenation unit”, and “a regression unit” in claims 1-2, 7-8, 11, and 13 described in paragraphs [0020], [0085], [0086], and [0087] respectively, and thus, claims 3-6, 9-10, 12, and 14 are similarly interpreted.
Regarding the claim limitation above, 112(f) is invoked because “unit” is a non-functional generic placeholder expressed merely by the function it performs. Although claims 1-2, 7-8, 11, and 13 are drafted as systems claims, the terms “module” and “unit” are Applicant’s claim term preceding a functional limitation of “computation/digitization/concatenation/regression”. Because Applicant fails to recite sufficiently definite structure for the terms “module” and “unit”, the claimed limitations are akin to a generic term.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 7-12, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable of Kano et al., US 20160155226 A1, (hereinafter “Kano”) in view of Lachner et al., US 20160300359 A1, (hereinafter “Lachner”) in further view of Wang et al., US 20170337687 A1, (hereinafter “Wang”).
Regarding claim 1, Kano teaches a computing system for providing a mapping of a physical quantity on a biological tissue, the computing system comprising:
an input data interface configured to obtain data from the physical quantity on different spatial points of the biological tissue ([0032] “A standard brain converting section 223 converts the three-dimensional surface layer information obtained by the three-dimensional information converting section 222 into three-dimensional surface layer information established in a spatial coordinate system aligned to the template of a standard brain.”);
a computation module comprising ([0042] “FIG. 3 is a flowchart showing the flow of the detection processing of the brain of the subject performed in the terminal device 200.” wherein a computation module is the terminal device):
a digitization unit configured to produce a digitized representation of the biological tissue in voxels, pixels, or voxels and pixels, wherein, based on the obtained data, a subset of the voxels, pixels, or voxels and pixels are assigned a value of the physical quantity ([0043] “Further, the three-dimensional information converting section 222 classifies each voxel of the three-dimensional brain image according to the type of tissue, such as gray matter, white matter, and the like (Step S13).”) ([0047] “Further, the standard brain converting section 223 calculates the volume and the degree of shrinkage of each voxel in the spatial coordinate system of the standard brain (Step S23), and extracts a feature amount resulting from the calculated volume and degree of shrinkage of each part (Step S24).”) ([0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value);
a ([0043] “Next, the three-dimensional information converting section 222 converts the set of the captured scan images of the brain into three-dimensional information (Step S12).”); and
generate ([0043] “Further, the three-dimensional information converting section 222 classifies each voxel of the three-dimensional brain image according to the type of tissue, such as gray matter, white matter, and the like (Step S13).”) ([0047] “Further, the standard brain converting section 223 calculates the volume and the degree of shrinkage of each voxel in the spatial coordinate system of the standard brain (Step S23), and extracts a feature amount resulting from the calculated volume and degree of shrinkage of each part (Step S24).”) ([0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value); and
an output data interface configured to, provide a mapping of the physical quantity on the biological tissue ([0050] “After completing the processing for extracting the aforesaid feature amounts, the individual-characteristic predicting section 225 performs the individual-characteristic prediction processing, and the display 215 displays the prediction result.”) ([0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value),
wherein a value of the physical quantity is assigned to each voxel, pixel, or voxel and pixel ([0047] “Further, the standard brain converting section 223 calculates the volume and the degree of shrinkage of each voxel in the spatial coordinate system of the standard brain (Step S23), and extracts a feature amount resulting from the calculated volume and degree of shrinkage of each part (Step S24).”) ([0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value).
Kano does not specifically disclose a concatenation unit configured to spatially correlate the voxels, pixels, or voxels and pixels; and
the processed information of the spatially correlated voxels, pixels or voxels and pixels.
However, Lachner teaches a concatenation unit configured to spatially correlate the voxels, pixels, or voxels and pixels ([0022] “Accordingly, voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image (and of the normalised difference image, see below) which represent the same part of the anatomical body and have a similar and in particular identical position in the common reference system are referred to as “corresponding voxels”. Preferably, all the voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image which represent the same part of the anatomical body have a similar and in particular identical position in the common reference system.”); and
the processed information of the spatially correlated voxels, pixels or voxels and pixels ([0022] “Accordingly, voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image (and of the normalised difference image, see below) which represent the same part of the anatomical body and have a similar and in particular identical position in the common reference system are referred to as “corresponding voxels”. Preferably, all the voxels of the spatially correlated enhanced image and the spatially correlated non-enhanced image which represent the same part of the anatomical body have a similar and in particular identical position in the common reference system.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to spatially correlate the pixels and/or voxel of Lachner in the tissue quantification method of Kano to increase the accuracy of tissue quantification by measuring the differences in brain tissue volume across brain at every pixel and/or voxel.
Kano in view of Lachner does not specifically disclose a regression unit and a regression analysis.
However, Wang teaches a regression unit and a regression analysis ([0100] “The classification unit 380 may generate a classification result by classifying a candidate target according to the extracted features from the candidate target. In some embodiments, a classification process may include performing data prediction by mapping features of a candidate target to a specific category based on a classification model or function.” wherein a regression unit is the classification unit and a regression analysis is a classification result).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a regression analysis of Wang of the physical quantity on the biological tissue based on the spatially correlated voxels of Kano in view of Lachner to elucidate a relationship between the spatially correlated voxels and other biological variables thereby identifying biologically relevant quantitative patterns.
Regarding claim 2, Kano in view of Lachner and Wang teaches the computing system of claim 1, wherein the computation module further comprises a performance assessment unit that is configured to evaluate accuracy of the provided mapping based on a ground truth (Kano - [0050] “After completing the processing for extracting the aforesaid feature amounts, the individual-characteristic predicting section 225 performs the individual-characteristic prediction processing, and the display 215 displays the prediction result.”) (Kano - [0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value) (Kano - [0051] “First, the individual-characteristic predicting section 225 acquires the feature amounts obtained in Steps S17, S25 and S32 of the flowchart shown in FIG. 3 (Step S41). Further, the individual-characteristic predicting section 225 compares the acquired feature amounts with feature amounts of the brain image of the prediction model(s) searched out by the database searching section 212 (Step S42).”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 7, Kano in view of Lachner and Wang teaches the computing system of claim 1, wherein the computation module further comprises a feature generating unit configured to compute a volume of the biological tissue, spatial coordinates of a center of mass of the biological tissue, or the volume and the spatial coordinates (Kano - [0045] “The standard brain converting section 223 converts the three-dimensional surface layer information converted by the three-dimensional information converting section 222 into three-dimensional surface layer information established in the spatial coordinate system of the standard brain (Step S15). Based on the three-dimensional surface layer information of the standard brain, the characteristic value detecting section 224 detects the characteristic values of each part of the brain of the subject, and calculates the differences of the detected characteristic values from the averages (the standards) (Step S16). Examples of the characteristic values of each part include, for example, the thickness, the volume, the surface area, the curvature and the like of each part of the surface layer. The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 8, Kano in view of Lachner and Wang teaches the computing system of claim 2, wherein the regression unit comprises an artificial intelligence entity configured to implement at least one machine learning regression algorithm (Wang - [0082] “In some embodiments, the machine learning technique may include a regression algorithm, a case learning algorithm, a formal learning algorithm, a decision tree learning algorithm, a Bayesian learning algorithm, a kernel learning algorithm, a clustering algorithm, an association rules learning algorithm, a neural network learning algorithm, a deep learning algorithm, a dimension reduction algorithm, etc.”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 9, Kano in view of Lachner and Wang teaches the computing system of claim 8, wherein the at least one machine learning regression algorithm comprises a random forest tree algorithm, a gradient boosting algorithm, or the random forest tree algorithm and the gradient boosting algorithm (Wang - [0082] “The decision tree learning algorithm may include a classification and regression tree algorithm, an iterative dichotomiser 3 (ID3) algorithm, a C4.5 algorithm, a chi-squared automatic interaction detection (CHAID) algorithm, a decision stump algorithm, a random forest algorithm, a mars algorithm, a gradient boosting machine (GBM) algorithm, etc.”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 10, Kano in view of Lachner and Wang teaches the computing system of claim 8, wherein the machine learning regression algorithm uses as input a feature matrix comprising spatial components of each voxel, pixel, or voxel and pixel, and the value of the physical quantity assigned to the voxel, pixel, or voxel and pixel (Wang - [0082] “The decision tree learning algorithm may include a classification and regression tree algorithm, an iterative dichotomiser 3 (ID3) algorithm, a C4.5 algorithm, a chi-squared automatic interaction detection (CHAID) algorithm, a decision stump algorithm, a random forest algorithm, a mars algorithm, a gradient boosting machine (GBM) algorithm, etc.”) (Kano - [0047] “Further, the standard brain converting section 223 calculates the volume and the degree of shrinkage of each voxel in the spatial coordinate system of the standard brain (Step S23), and extracts a feature amount resulting from the calculated volume and degree of shrinkage of each part (Step S24).”) (Kano - [0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).” wherein the physical quantity is the characteristic value).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 11, Kano in view of Lachner and Wang teaches the computing system of claim 8, further comprising an optimization unit configured to use the evaluation of the performance assessment unit to optimize the machine learning regression algorithm (Wang - [0100] “A classifier may correspond to a classification technique. In some embodiments, the classification technique may include a supervised technique. The supervised technique may include identifying a pending sample according to a specific rule and element features of the pending sample, and classifying the element features of the pending sample into a category of training samples based on similar features to the pending sample.” wherein an optimization unit is the classification technique) (Wang - [0082] “The decision tree learning algorithm may include a classification and regression tree algorithm, an iterative dichotomiser 3 (ID3) algorithm, a C4.5 algorithm, a chi-squared automatic interaction detection (CHAID) algorithm, a decision stump algorithm, a random forest algorithm, a mars algorithm, a gradient boosting machine (GBM) algorithm, etc.”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 12, Kano in view of Lachner and Wang teaches the computing system of claim 1, wherein the biological tissue is brain tissue (Kano - [0032] “A standard brain converting section 223 converts the three-dimensional surface layer information obtained by the three-dimensional information converting section 222 into three-dimensional surface layer information established in a spatial coordinate system aligned to the template of a standard brain.”).
The motivation for combining Kano, Lachner, and Wang is the same motivation as used for claim 1.
Regarding claim 15, the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 15 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 16, the claim recites similar limitations to claim 10 but in the form of a method. Therefore, claim 16 recites similar limitations to claim 10 and is rejected for similar rationale and reasoning (see the analysis for claim 10 above).
Regarding claim 17, the claim recites similar limitations to claim 1 but in the form of a non-transient computer-readable storage medium (Lachner - [0058] “The invention also relates to a program which, when running on a computer, causes the computer to perform one or more or all of the method steps described herein and/or to a program storage medium on which the program is stored (in particular in a non-transitory form)”). Therefore, claim 17 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Claims 3-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable of Kano et al., US 20160155226 A1, (hereinafter “Kano”) in view of Lachner et al., US 20160300359 A1, (hereinafter “Lachner”) in further view of Wang et al., US 20170337687 A1, (hereinafter “Wang”) in further view of Malik et al., US 20190128977 A1, (hereinafter “Malik”).
Regarding claim 3, Kano in view of Lachner and Wang teaches the computing system of claim 2, wherein the physical quantity is ([0045] “The characteristic value detecting section 224 extracts the characteristic values obtained above as an individual feature amount (Step S17).”).
Kano in view of Lachner and Wang does not specifically disclose a radiofrequency magnetic field generated during magnetic resonance imaging (MRI).
However, Malik teaches a radiofrequency magnetic field generated during magnetic resonance imaging (MRI) ([0080] “The MR based method uses a very short series of very low flip angle (vLFA) RF pulses that can be implemented as the pre-calibration for imaging sequences. The method relies on strong local enhancement of B1 fields close to conductors due to induced currents.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the radiofrequency magnetic resonance with parallel transmission imaging method of Malik with the biological tissue quantification method of Kano in view of Lachner and Wang because using a radiofrequency magnetic field and techniques such as parallel transmission enhance the accuracy and efficiency of MRI tissue quantification.
Regarding claim 4, Kano in view of Lachner, Wang, and Malik teaches the computing system of claim 3, wherein the MRI comprises ultrahigh field magnetic resonance imaging with parallel transmission imaging radiofrequency (Malik - [0017] “Embodiments of the invention relate to a parallel transmit MR scanner and associated method of operation thereof, which is used to image a conductive object such as an interventional device like a guidewire within a subject…However, even at these low power levels sufficient electric currents are induced in the conductive interventional device to allow localised magnetic fields to be induced immediately around the surface of the conductive object, the localised magnetic fields being sufficient to cause detectable MR signals to be generated from around the conductive object which can be detected by the MR scanner.”).
The motivation for combining Kano, Lachner, Wang, and Malik is the same motivation as used for claim 3.
Regarding claim 5, Kano in view of Lachner, Wang, and Malik teaches the computing system of claim 4, wherein the obtained data of the radiofrequency magnetic field is generated following a fast low angle shot magnetic resonance imaging (Malik - [0080] “The MR based method uses a very short series of very low flip angle (vLFA) RF pulses that can be implemented as the pre-calibration for imaging sequences. The method relies on strong local enhancement of B1 fields close to conductors due to induced currents.”).
The motivation for combining Kano, Lachner, Wang, and Malik is the same motivation as used for claim 3.
Regarding claim 6, Kano in view of Lachner, Wang, and Malik teaches the computing system of claim 3, wherein the ground truth comprises a mapping of the radiofrequency magnetic field acquired with actual flip angle imaging (Kano - [0051] “First, the individual-characteristic predicting section 225 acquires the feature amounts obtained in Steps S17, S25 and S32 of the flowchart shown in FIG. 3 (Step S41). Further, the individual-characteristic predicting section 225 compares the acquired feature amounts with feature amounts of the brain image of the prediction model(s) searched out by the database searching section 212 (Step S42).”) (Malik - [0080] “The MR based method uses a very short series of very low flip angle (vLFA) RF pulses that can be implemented as the pre-calibration for imaging sequences. The method relies on strong local enhancement of B1 fields close to conductors due to induced currents.”).
The motivation for combining Kano, Lachner, Wang, and Malik is the same motivation as used for claim 3.
Regarding claim 13, the claim recites similar limitations to claims 1 and 3 but in the form of a medical scanning system, and wherein the computing system is configured to obtain data corresponding to the radio-frequency magnetic field from the magnetic resonance imaging scanner (Malik - [0080] “The MR based method uses a very short series of very low flip angle (vLFA) RF pulses that can be implemented as the pre-calibration for imaging sequences. The method relies on strong local enhancement of B1 fields close to conductors due to induced currents.”). Therefore, claim 13 recites similar limitations to claims 1 and 3 and is rejected for similar rationale and reasoning (see the analysis for claims 1 and 3 above).
Regarding claim 14, the claim recites similar limitations to claim 4 but in the form of a medical scanning system. Therefore, claim 14 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA PEARSON whose telephone number is (703)-756-5786. The examiner can normally be reached Monday - Friday 8:00 - 5:30.
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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