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) was submitted on 5/24/2024. The submission is 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 § 101
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas of “mathematical relationships”, “mathematical formulas or equations”, or “mathematical calculations” without significantly more.
Analyses of the subject matter eligibility tests are performed for each of the independent claims and associated dependent claims below.
Regarding independent claim 1, the claim recites:
The limitation of “predicting aleotoric uncertainty information and a sample spectrum from a pre-processed input spectrum using an artificial neural network, wherein the sample spectrum has a form in which signal quality is improved compared to the pre-processed input spectrum;” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the input spectrum processing and prediction forms a mathematical calculation and output through the artificial neural network. The limitation of “generating a predictive mean spectrum by calculating an average of the sample spectrum;” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the calculation of an average is a mathematical calculation. The limitation of “calculating an epistemic uncertainty spectrum of the sample spectrum based on a variance of the sample spectrum” is considered to be a mathematical relationship and mathematical calculations as the calculation of an uncertainty spectrum forms a mathematical calculation. The limitation of “calculating an aleatoric uncertainty spectrum of the sample spectrum based on an average of the aleatoric uncertainty information” is considered to be a mathematical relationship and mathematical calculations as the calculation of an aleatoric uncertainty spectrum forms a mathematical calculation. The limitation of “calculating a two-standard deviation spectrum (2SD spectrum) of the sample spectrum based on the epistemic uncertainty spectrum and the aleatoric uncertainty spectrum” is considered to be a mathematical relationship and mathematical calculations as the calculation of a spectrum from another spectrum is a mathematical calculation. Therefore, the claim is directed to an abstract idea and a judicial exception.
Step 2A Prong 2 Analysis (Claim 1): This judicial exception is not integrated into a practical application because it does not recite any elements that integrate the abstract idea into a practical application such as improving the operation of the diagnostic device, or effecting a particular treatment or prophylaxis for a disease or medical condition. The claims do not recite any features of components that integrates the judicial exception into a practical application because there are no additional recited elements beyond the judicial exception. Therefore, all of these claimed elements are not sufficient to improve the functioning of a diagnostic device or form of technology. Furthermore, while directed to activity for medical diagnostics, the claimed steps do not effect a particular treatment or prophylaxis for a disease or medical condition.
Step 2B Analysis (Claim 1): The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional recited elements beyond the judicial exception. The limitations do not include improvements to the functioning of a computer or to any other technology or technical field, and the elements of the claim further do not effect a particular treatment or prophylaxis for a disease or medical condition. Furthermore, there are no claimed features that provide elements to identify improvements to these general computing technologies based on the claimed features. Any limitations merely reference insignificantly extra-solution activity of data gathering of an input spectrum, and link the judicial exception to generic computing elements within the art of medical diagnostics.
Dependent claim 2 includes limitations that are directed to a “BCNN” neural network which merely limits the mathematical calculations abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 3 includes limitations that are directed to spectrum characteristics that are mathematically manipulated which merely limits the mathematical calculations abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 4 includes limitations that are directed to sample spectrum predicting that are further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 5 includes limitations that are directed to predictive mean spectrum based upon an average value with is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 6 includes limitations that are directed to narrowing of calculating of the 2SD spectrum which is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 7 includes limitations that are directed to additional calculations which is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 8 includes limitations that are directed to additional calculations of vector calculations which is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 9 includes limitations that are directed to additional calculations of normalizations which is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 10 includes limitations that are directed to an equation which forms a mathematical relationship of a mathematical formula/equation. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 11 includes limitations that are directed to additional calculations of signal processing which is further narrowing of the mathematical calculation abstract idea of the independent claim. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Dependent claim 12 includes limitations that are directed to an output unit transmitting which forms extra solution activity of mere data outputting. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Regarding independent claim 13, the claim recites:
The limitation of “a calculation of pre-processing encrypted magnetic resonance spectroscopy (MRS) data to provide an input spectrum” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the input spectrum processing forms a mathematical calculation performed on the magnetic resonance data. The limitation of “a calculation of predicting aleatoric uncertainty information and a sample spectrum from the pre-processed input spectrum using an artificial neural network;” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the input spectrum processing and prediction forms a mathematical calculation and output through the artificial neural network. The limitation of “a calculation of calculating a predictive mean spectrum of the predicted sample spectrum and a 2SD spectrum of the predicted sample spectrum based on the predicted sample spectrum and the predicted aleatoric uncertainty information” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the calculation of an average is a mathematical calculation. The limitation of “a calculation of calculating a metabolite content value by performing linear regression on the predictive mean spectrum” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the calculation of a value using linear regression is a mathematical calculation. The limitation of “a calculation of calculating a metabolite standard deviation value by performing linear regression on the 2SD spectrum” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the calculation of a value using linear regression is a mathematical calculation. The limitation of “a calculation of normalizing each of the metabolite content value and the metabolite standard deviation value” is considered to be an abstract idea of a mathematical relationship and mathematical calculations as the normalization of a value is a mathematical calculation. Therefore, the claim is directed to an abstract idea and a judicial exception.
Step 2A Prong 2 Analysis (Claim 13): This judicial exception is not integrated into a practical application because it does not recite any elements that integrate the abstract idea into a practical application such as improving the operation of the diagnostic device, or effecting a particular treatment or prophylaxis for a disease or medical condition. The claims do not recite any features of components that integrates the judicial exception into a practical application because the additional recited elements of “processors” and “memories” form generic computing elements merely configured to apply or implement the recited judicial exception limitations. Therefore, all of these claimed elements are not sufficient to improve the functioning of a diagnostic device or form of technology. Furthermore, while directed to activity for medical diagnostics, the claimed steps do not effect a particular treatment or prophylaxis for a disease or medical condition.
Step 2B Analysis (Claim 13): The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional recited elements of “processors” and “memories” form generic computing elements merely configured to apply or implement the recited judicial exception limitations. The limitations do not include improvements to the functioning of a computer or to any other technology or technical field, and the elements of the claim further do not effect a particular treatment or prophylaxis for a disease or medical condition. Furthermore, there are no claimed features that provide elements to identify improvements to these general computing technologies based on the claimed features. Any limitations merely reference insignificantly extra-solution activity of data gathering of an input spectrum, and link the judicial exception to generic computing elements within the art of medical diagnostics.
Claim 14 includes limitations that are directed to a non-transitory computer-readable storage medium incorporating the method of claim 1. This forms generic computer elements configured to execute the abstract idea mathematical relationships and calculations of independent claim 1. Therefore, it does not integrate the judicial exception of the independent claim into a practical application or amount to significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7, 9, and 11-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee, H., et al. (“Bayesian deep learning-based 1H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout,” Proceedings of the 2021 ISMRM & SMRT Annual Meeting & Exhibition.” 2021. P. 1-6) hereinafter Lee (see NPL reference of applicant’s IDS of 5/24/2024 for citations).
Regarding claim 1, Lee teaches:
A method for non-invasive quantification of metabolites in a body (introduction; page 2, figure 1), comprising:
predicting aleotoric uncertainty information (figure 1, “sampled variance spectrum”; pages 1-2, Methods and Results) and a sample spectrum (figure 1, “sampled metabolite only spectrum”; pages 1-2, Methods and Results) from a pre-processed input spectrum using an artificial neural network (figure 1, “input to BCNN” and “BCNN”; pages 1-2, Methods and Results), wherein the sample spectrum has a form in which signal quality is improved compared to the pre-processed input spectrum (introduction; figure 1, pages 1-2, Methods and Results, sampled metabolite only spectrum includes a better SNR and percent error minimization);
generating a predictive mean spectrum by calculating an average of the sample spectrum (figure 1, “predictive mean spectrum”; pages 1-2, Methods and Results);
calculating an epistemic uncertainty spectrum of the sample spectrum based on a variance of the sample spectrum (figure 1, “epistemic uncertainty spectrum”; pages 1-2, Methods and Results);
calculating an aleatoric uncertainty spectrum of the sample spectrum based on an average of the aleatoric uncertainty information (figure 1, “aleatoric uncertainty spectrum”; pages 1-2, Methods and Results); and
calculating a two-standard deviation spectrum (2SD spectrum) of the sample spectrum based on the epistemic uncertainty spectrum and the aleatoric uncertainty spectrum (figure 1, “2SD spectrum from total uncertainty spectrum”; pages 1-2, Methods and Results).
Regarding claim 2, Lee teaches all of the limitations of claim 1. Lee further teaches:
wherein the artificial neural network includes a Bayesian convolutional neural network (BCNN) (figure 1, “BCNN”; pages 1-2, Methods and Results).
Regarding claim 3, Lee teaches all of the limitations of claim 1. Lee further teaches:
wherein, when compared to the pre-processed input spectrum, a signal-to-noise ratio (SNR), a linewidth, and a phase/frequency are adjusted and an MM signal is added in the sample spectrum (pages 1-2, Methods, “For each spectrum, the SNR was lowered and the linewidth was broadened simultaneously and gradually to generate 10 modified spectra with different SNR and linewidth combinations”).
Regarding claim 4, Lee teaches all of the limitations of claim 1. Lee further teaches:
wherein the predicting includes predicting T sample spectra and T pieces of aleatoric uncertainty information per the pre-processed input spectrum through T times Monte Carlo dropout sampling (MCDO sampling), the sample spectrum corresponds to a metabolite-only output spectrum, and the aleatoric uncertainty information corresponds to a noise variance spectrum (pages 1-2, Methods, “BCNN”; Figure 1, “T times MCDO”; pages 1-2, Methods and Results; Sample spectrum is metabolite only output spectrum as in figure 1 and aleatoric uncertainty corresponds to noise variance as in figure 1).
Regarding claim 5, Lee teaches all of the limitations of claim 4. Lee further teaches
wherein the predictive mean spectrum is generated based on an average value of the sample spectra for each data point (figure 1, “predictive mean spectrum”; and pages 1-2, Methods and Results, generated based upon average from each metabolite content data).
Regarding claim 6, Lee teaches all of the limitations of claim 1. Lee further teaches:
wherein the calculating of the 2SD spectrum includes: calculating a total uncertainty spectrum by summing the epistemic uncertainty spectrum and the aleatoric uncertainty spectrum; calculating a standard deviation spectrum (SD-spectrum) from a 1/2 power of the total uncertainty spectrum; andcalculating the 2SD spectrum by multiplying the SD spectrum by 2 (pages 1-2, Methods and Results, BCNN “For the estimation of the corresponding uncertainty, first, a two-standard deviation (2xSD) spectrum (2σ in Figure 1) was obtained from the total uncertainty spectrum”; figure 1 “2SD spectrum (2σ)”).
Regarding claim 7, Lee teaches all of the limitations of claim 1. Lee further teaches:
further comprising: performing baseline correction on the predictive mean spectrum and the 2SD spectrum; calculating a metabolite content value by performing linear regression on the corrected predictive mean spectrum; calculating a metabolite standard deviation value by performing linear regression on the corrected 2SD spectrum; and normalizing the metabolite content value and the metabolite standard deviation value (page 1, Methods, “BCNN, each individual metabolite content was estimated from the predictive mean spectrum (Figure 1) by multiple regression using the metabolite basis set as previously described… Finally, the uncertainty was converted into the percentage with respect to the metabolite content (% uncertainty) for each metabolite”; pages 1-2, Methods and Results).
Regarding claim 9, Lee teaches all of the limitations of claim 7. Lee further teaches:
wherein the normalizing is based on (1) a relative content of the metabolite to water or (2) a relative content of the metabolite to a reference metabolite (pages 1-2, Methods and Results, “Finally, the uncertainty was converted into the percentage with respect to the metabolite content (%uncertainty) for each metabolite” which as depicted in figure 1 forms a relative content to a reference metabolite).
Regarding claim 11, Lee teaches all of the limitations of claim 7. Lee further teaches:
further comprising: forming a reconstructed signal by calculating a difference between the normalized metabolite content value and the predictive mean spectrum; forming a reconstructed signal by calculating a difference between the normalized metabolite standard deviation value and the 2SD spectrum; and checking accuracy of the normalization based on a result of comparing the reconstructed signals (page 1, “Methods Evaluation of the proposed method”; figures 2-4 provide for different between metabolite content and predictive mean spectrum (see figure 2) and difference between normalized SD value and 2SD spectra (figure 2) and using outputs to determine spectral quality (figures 3-4) which forms a checking of the accuracy).
Regarding claim 12, Lee teaches all of the limitations of claim 1. Lee further teaches:
further comprising transmitting the predictive mean spectrum and the 2SD spectrum to an output unit (figure 2, provides the output of the predictive mean spectrum and 2SD spectrum as graph outputs; see also figure 1 and pages 1-2, Methods and Results,).
Regarding claim 13, Lee teaches:
A quantification apparatus for quantifying metabolites in a body (introduction; page 2, figure 1), comprising:
one or more processors (pages 1-2, Methods and Results, include computer-implemented processing known by one of ordinary skill in the art to require one or more processors); and
one or more memories configured to store instructions that, when executed by the one or more processors, cause the one or more processors to perform a calculation (ages 1-2, Methods and Results, include computer-implemented processing known by one of ordinary skill in the art to require one or more memories),
wherein the calculation performed by the one or more processors includes:
a calculation of pre-processing encrypted magnetic resonance spectroscopy (MRS) data to provide an input spectrum (page 1, Methods, “modified in vivo spectra”; figure 1);
a calculation of predicting aleatoric uncertainty information (figure 1, “sampled variance spectrum”; pages 1-2, Methods and Results) and a sample spectrum (figure 1, “sampled metabolite only spectrum”; pages 1-2, Methods and Results) from the pre-processed input spectrum using an artificial neural network (figure 1, “input to BCNN” and “BCNN”; pages 1-2, Methods and Results);
a calculation of calculating a predictive mean spectrum of the predicted sample spectrum (figure 1, “predictive mean spectrum”; pages 1-2, Methods and Results) and a 2SD spectrum of the predicted sample spectrum based on the predicted sample spectrum and the predicted aleatoric uncertainty information (figure 1, “2SD spectrum from total uncertainty spectrum”; pages 1-2, Methods and Results);
a calculation of calculating a metabolite content value by performing linear regression on the predictive mean spectrum (figure 1, “metabolite basis set”; pages 1-2, Methods and Results);
a calculation of calculating a metabolite standard deviation value by performing linear regression on the 2SD spectrum (figure 1, “metabolite basis set (abs)”; pages 1-2, Methods and Results); and
a calculation of normalizing each of the metabolite content value and the metabolite standard deviation value (figure 1, “metabolite content” and “% uncertainty”; pages 1-2, Methods and Results).
Regarding claim 14, Lee teaches all of the limitations of claim 1. Lee further teaches:
A non-transitory computer-readable storage medium for storing a computer program for executing the quantification method of claim 1 (see rejection of claim 1 above; page 1, Introduction).
Claim Rejections - 35 USC § 103
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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lee as applied to claim 7 above, and further in view of Fortney et al. (U.S. Pat. No. 11881311) hereinafter Fortney.
Regarding claim 8, primary reference Lee teaches all of the limitations of claim 7. Primary reference Lee further fails to teach:
further comprising converting the normalized metabolite content value and the normalized metabolite standard deviation value into a vector form and transmitting the converted vector to an output unit
However, the analogous art of Fortney of a metabolite prediction and analysis method (abstract) teaches:
further comprising converting the normalized metabolite content value and the normalized metabolite standard deviation value into a vector form and transmitting the converted vector to an output unit (claim 1, “comparing the metabolite value in the obtained dataset associated with the sample from the subject to a corresponding normalized metabolite value in the default state to determine a relative metabolite value, wherein each relative metabolite value represents an abundance or lack of metabolites for the corresponding survival biomarker in the sample from the subject compared to the default state; encoding the determined relative metabolite values into a vector representation; inputting the vector representation into a survival predictor model comprising coefficients for the n survival biomarkers to generate a survival metric value”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sample spectrum analysis method of Lee to incorporate the vector form determination and output as taught by Fortney because the vector representation enables multivariate model determination of indicators within the normalized metabolite values (Fortney, col 25, lines 17-34). This provides for enhanced insight into the determine normalized metabolite values, and higher quality diagnostics.
Allowable Subject Matter
Claim 10 is currently rejected under 35 U.S.C. 101 above. The claim is directed to mathematical concepts not found in the closest prior art of record, but are currently not allowable because of the current rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN A FRITH whose telephone number is (571)272-1292. The examiner can normally be reached M-Th 8:00-5:30 Second Fri 8:00-4:30.
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/SEAN A FRITH/Primary Examiner, Art Unit 3798