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 .
Response to Amendment
Applicant’s amendments to the claims, filed 02/27/2026, are accepted and appreciated by the
Examiner.
Response to Arguments
Applicant's arguments, see Remarks, filed 2/27/2026, with respect to the rejections of claims 1, 9, and 10 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Claims 1, 9, and 10 are directed to non-negative matrix factorization and other mathematical methods of analyzing data which are viewed as mathematical and mental processes. The inventions recited do not merely involve the abstract ideas but are abstract ideas. The fact pattern of the subject matter eligibility example 39 is different from that of the claims. The claims do not apply transformations to images or cite training a neural network. Instead, the claims recite calculating a statistic, generating a dendrogram, and applying non-negative matrix factorization which are all known mathematical concepts. They further recite selecting clusters which is a mental process as it can be done in the human mind using observation, judgement, and opinion. As seen in MPEP 2106.04 “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore, claims do not have to recite specific formulas or equations to recite a mathematical concept.
The argued improvement is an improvement to the decomposition of data which would represent the judicial exception alone providing the improvement. As seen in MPEP 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.”
Applicant’s arguments, see Remarks, filed 2/27/2026, with respect to the rejections of claims 1, 9, and 10 under 35 U.S.C. 103 have been fully considered and are persuasive in light of the amendments. The combination of Godwin and Rossi does not explicitly teach “A processing apparatus for applying non-negative matrix factorization to a plurality of-one or more measured profiles of X-ray powder diffraction, wherein, in the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, and the known information is information common to the measurement profiles including in the clusters.” Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Godwin (US 20210020272 A1), Semizarov (US 20100145893 A1), and Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017).
Claim Rejections - 35 USC § 101
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.
Claims 1-4 and 6-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claims 1, 9, and 10 the preamble does not carry patentable weight and the following bolded limitations are considered abstract:
“processing circuitry configured to acquire one or more measured profiles, acquire known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile,
calculate a statistic between the plurality of measured profiles and generate a dendrogram,
select clusters including groups of similar profiles from the dendrogram, or allow a user to select them,
and apply non-negative matrix factorization to the measured profile based on the known information,
wherein, in the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, and
the known information is infom1ation common to the measurement profiles including in the clusters.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. Calculating a statistic and generating a dendrogram are mathematical concepts as calculating is a mathematical calculation and generating a dendrogram represents creating a well-known tree graph and graphing data is a mathematical concept. Non-negative matrix factorization is a mathematical concept as seen in paragraphs 32-36 in the specification. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“processing circuitry configured to acquire the plurality measured profiles, acquire known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use 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 - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic data.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processing circuitry configured to acquire the plurality of measured profiles, acquire known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile” amounts to using a computer as a tool as processing circuitry is viewed as a generic computer and mere data gathering as data is just acquired. Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by
Godwin (US 20210020272 A1)
Tikole (Peak picking NMR spectral data using non-negative matrix factorization; 2014).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “processing circuitry configured to acquire the plurality of measured profiles, acquire known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile;” just tie the claim to a generic computer system and receiving data.
Dependent claims 2-4 and 6-8 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to further limit the data and constraints placed on the non-negative matrix factorization which amount to mathematical concepts and mental processes. Claim 8 includes a well-known x-ray diffractometer which amounts to mere data gathering as obtaining data regardless of where from is data gathering. In other words, the inventive concept does not seem to be directed to the x-ray diffractometer.
Therefore, dependent claims 2-4 and 6-8 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-4 and 6-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, and 10, recite the limitation “the known information is information common to the measurement profiles including in the clusters.” It is unclear and indefinite what is meant by the limitation. For the purposes examination the limitation will be viewed as “the known information is contained within the selected clusters.”
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35
U.S.C. 112 (pre-AIA ), second paragraph.
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.
Claims 1-4, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Godwin (US 20210020272 A1) in view of Semizarov (US 20100145893 A1) and Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017).
With respect to claims 1, 9, and 10,
Godwin teaches,
processing circuitry configured to acquire the plurality of one or more measured profiles, (Para. [0034] teaches “The analysis system 10 may be implemented by a computer apparatus. In this case, a computer program capable of execution by the computer apparatus is provided. The computer program is configured so that, on execution, it causes the computer apparatus to perform the method.” (Para. [0036] teaches “In block B1, the analysis system 10 performs a Fourier Transform of the total scattering data 3 to derive PDF data 12 that is scattering data that represents a pair distribution function (PDF). The PDF data 12 may be any representation and any normalisation. A PDF is a known representation of the results of a diffraction experiment.” (i.e. PDF 12 is viewed as a measured profile.) Para. [0039] teaches “The PDF data 12 is assembled into an n×m matrix D, where n is the number of data sets corresponding to the number of samples and m is the number of data points in each set.” (i.e. can handle more than one data set.)
acquire known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile, (Para. [0043] “As a preliminary step performed in block B2, the analysis system 10 sets initial values for the for the basis components and the fitting coefficients. This may be done in any suitable manner. The initial values may be determined randomly, may be based on prior knowledge, or may be generated in any other way.” (i.e. acquire known information corresponding to a background) Para. [0042] teaches “The basis components comprise an n×k matrix H, and the fitting coefficients make up a k×m matrix W, where k is the number of basis components and n and m correspond to the dimensions of matrix D i.e. the PDF data 12.” (i.e. the coefficients and basis components include a shape of the measured profile.)
wherein the processing circuitry is further configured to calculate a statistic between the plurality of measured profiles. (Para. [0060] teaches “The output data 14 may optionally also include any or all of: the uncertainties in the derived basis components; the uncertainties in the derived fitting coefficients; parameter covariances of the fit of the derived basis components and fitting coefficients to the PDF data 12; the statistical quality of the fit, and any further information characterising the mixture that is derived in block B3.”)
and apply non-negative matrix factorization to the measured profile based on the known information. (Para. [0045] teaches “In block B3, the analysis system 10 performs the optimisation technique to optimise the fit of the basis components and the fitting coefficients to the PDF data 12. In general terms, the optimisation technique processes the refineable parameters, which in this case are the basis components and the fitting coefficients, and refines them against input data, which in this case is the PDF data 12.” Para. [0048] teaches “The optimisation technique performed in block B3 may advantageously be non-negative matrix factorisation (NMF). NMF is in itself one of many known approaches to processing complex data sets and is conceptually related to principal component analysis (PCA).”))
Godwin does not explicitly teach,
A processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction
generate a dendrogram, select clusters including groups of similar profiles from the dendrogram, or allow a user to select them,
wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters,
and the known information is information common to the measurement profiles including in the clusters.
Semizarov teaches,
generate a dendrogram, select clusters including groups of similar profiles from the dendrogram, or allow a user to select them, (Para. [0042] teaches “cutting the dendrogram into r clusters;”)
wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, (Para. [0131] teaches “Using unsupervised clustering (such as hierarchical clustering) to estimate the possible numbers of clusters before fitting the data with a genomic Non-negative Matrix Factorization (gNMF) model;” Para. [0192] teaches “The numbers of subgroups (which are each referred to as "r value", where each r value is an integer from 1 to 100) are then used as input in the clustering analysis using genomic Non-negative Matrix Factorization ("gNMF").”)
the known information is information common to the measurement profiles including in the clusters. (Para. [0185] “Using unsupervised clustering (such as hierarchical clustering) to estimate the possible numbers of clusters before fitting the data with a genomic Non-negative Matrix Factorization (gNMF) model;” (i.e. the data used on both methods are the same.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Godwin with generate a dendrogram, select clusters including groups of similar profiles from the dendrogram, or allow a user to select them, wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, and the known information is information common to the measurement profiles including in the clusters such as that of Semizarov. One of ordinary skill would have been motivated to modify Godwin, because as seen in Para. [0180] a dendrogram allows the system to cluster data and to determine the number of clusters. Furthermore, a dendrogram allows the data to be visualized and allows clusters to be made without predefining the number of groups.
The combination of Godwin and Semizarov do not explicitly teach,
A processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction.
Xiong teaches,
A processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction. (The introduction teaches “computational modeling and experimentation” and “A typical HTE X-ray diffraction experiment involves the acquisition of anywhere from 500 to 40000 diffractograms, far too many for scientists to parse through individually. A large variety of synthesis methods for HTE samples exist but broadly they can be composed of discrete compositions or as continuously varying compositional spreads or composition wedges. Phases present in a sample are characterized by the peak distribution from X-ray diffraction patterns.” Pg. 138 of the introduction teaches “Unsupervised techniques can involve the identification of basis vectors via non-negative matrix factorization, calculation of average distances using weighting functions to form dendrograms, or combinations thereof.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Godwin and Semizarov with a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction such as that of Xiong. One of ordinary skill would have been motivated to modify Godwin, because it shows that the analysis methods applied to the data in Godwin and Xiong can be used in combination to analyze XRD data such as seen on Page 138 of Xiong.
With respect to claim 2,
Godwin further teaches,
wherein the processing circuitry, according to the presence or absence of the known information, is further configured to selectively perform a normal non-negative matrix factorization or a non-negative matrix factorization with a constraint based on the known information. (Para. [0059] teaches “applying a constraint on any of the basis components, the fitting coefficients, or a relationship therebetween. The constraint data 13 represents a constraint on the refineable parameters, i.e. the basis components or the fitting coefficients or both, or a relationship between any of the refineable parameters. Then the optimisation technique performed in block B3 is performed applying the constraint.” (i.e. applying a constraint based on the presence of known information.))
With respect to claim 3,
Godwin further teaches,
The processing apparatus according to claim 1, wherein the known information is information indicating that the coefficient matrix of the predetermined profile commonly included in the plurality of measured profiles has the same values. (Para. [0032] teaches “plural samples that each comprise a mixtures of the same chemical components in nominally the same proportions to reduce experimental error”).
With respect to claim 4,
Godwin further teaches,
The processing apparatus according to claim 1, wherein the information on the shape of the predetermined profile is information obtained from a database or information based on measured data. (Para. [0036] teaches “A PDF is a known representation of the results of a diffraction experiment.” (i.e. with respect to measured data.))
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Godwin (US 20210020272 A1), Semizarov (US 20100145893 A1), and Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017) as applied to claim 1 above, and further in view of Tikole (Peak picking NMR spectral data using non-negative matrix factorization; 2014).
With respect to claim 6,
Godwin does not explicitly teach,
The processing apparatus according to claim 1, wherein the processing circuitry is further configured to perform a peak search on the profile obtained by the non-negative matrix factorization and generate a d-I list, and perform a qualitative analysis using the d-I list.
Tikole teaches,
wherein the processing circuitry is further configured to perform a peak search on the profile obtained by the non-negative matrix factorization (Peak picking section teaches “The model factorizes peak components in one-dimensional (1D) peak shapes in the source matrix X q. Peak positions are obtained by fitting an ideal Gaussian shape of average linewidth to the observed component by minimizing the scalar product between the Gaussian shape and the observed component. Next, the linewidth of the peak is adapted to obtain an optimal agreement. The final peak positions are obtained by performing a three-point parabolic interpolation. The final peak lists are obtained by applying a user-defined intensity threshold.” (i.e. peak search of NMF data.)
and generate a d-I list, and perform a qualitative analysis using the d-I list. (Results and Discussion section teaches “The model was able to pick 201 backbone and side-chain cross peaks from the HNCO spectrum. The peak list of 201 peaks is given in Additional file 1: Table S1. The NTF2 decomposition of a small region of about eight overlapped peaks of the 3D HNCO experiment is shown in Figure 3.” (i.e. Peak list is considered a d-I list.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Godwin wherein the processing circuitry is further configured to perform a peak search on the profile obtained by the non-negative matrix factorization and generate a d-I list, and perform a qualitative analysis using the d-I list such as that of Tikole. One of ordinary skill would have been motivated to modify Godwin, because other peak picking algorithms can often produce wrong intensities and frequencies for overlapping peak clusters as seen in the abstract of Tikole. Tikole further states in the abstract “To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments.”
With respect to claim 7,
Godwin does not explicitly teach,
The processing apparatus according to claim 6, wherein the processing circuitry is further configured to perform quantitative analysis using the qualitatively analyzed data.
Tikole further teaches,
wherein the processing circuitry is further configured to perform quantitative analysis using the qualitatively analyzed data. “The model was able to pick 201 backbone and side-chain cross peaks from the HNCO spectrum. (i.e. picking the backbone and side-chain cross peaks is viewed as quantitively analyzing the data as the model is a mathematical algorithm.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Godwin wherein the processing circuitry is further configured to perform quantitative analysis using the qualitatively analyzed data such as that of Tikole. One of ordinary skill would have been motivated to modify Godwin, because quantitively analyzing the data would confirm qualitative results and therefore, allow for more accurate findings.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Godwin (US 20210020272 A1), Semizarov (US 20100145893 A1), and Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017) as applied to claim 1 above, and further in view of Sasaki (US 20190064083 A1).
With respect to claim 8,
Godwin does not explicitly teach,
A system comprising, an X-ray diffractometer comprising an X-ray source for generating X-rays, a detector for detecting X-rays and a goniometer for controlling rotation of a sample, and the processing apparatus according to claim 1.
Sasaki teaches,
A system comprising, an X-ray diffractometer comprising an X-ray source for generating X-rays, a detector for detecting X-rays and a goniometer for controlling rotation of a sample, (Para. [0035] teaches “The X-ray diffractometer 2 has a goniometer 7, an X-ray generation device 8, a collimator 9, an X-ray detector 10, a control unit 11, and an input/output device 12, as shown in FIG. 2. The goniometer 7 is an angle-measuring instrument. A sample stage 15 that supports a sample S and rotates is provided to a central portion of the goniometer 7.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Godwin with a system comprising, an X-ray diffractometer comprising an X-ray source for generating X-rays, a detector for detecting X-rays and a goniometer for controlling rotation of a sample such as that of Sasaki.
One of ordinary skill would have been motivated to modify Godwin, because as seen in para. [0048] of Godwin “a data set might contain X-ray diffraction patterns of a series of binary mixtures with different mass fractions.” And therefore, a system such as that of Sasaki would be needed to obtain the x-ray diffraction patterns.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 mailing date of this final action.
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/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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