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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/26/2026 has been entered.
Response to Arguments
Applicant's arguments, see Remarks, filed 07/29/2026, with respect to the 35 U.S.C. 101 rejections of claims 1, 9, and 10 have been fully considered but they are not persuasive. The claims are still directed towards an abstract idea. Calculating a statistic, generating a dendrogram, and applying non-negative matrix factorization are all mathematical concepts. Selecting clusters can be done in the human mind using observation, judgement, and opinion. The data may now come from an X-ray diffractometer, but this just limits the source/type of data used in the analysis (see MPEP 2106.05(b): “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more”). This is also viewed as generally linking the use of the judicial exception to a particular technological environment or field of use as seen in MPEP 2106.05(h). The claim does not do anything with the data except for generating a dendrogram and applying non-negative matrix factorization. There is nothing in the claim that ties the mathematical analysis back to the measurement apparatus nor does the argued improvement appear to be directed to the measurement apparatus. The improvement argued is an improvement to the abstract idea itself as seen in MPEP 2106.05(a). Furthermore, the claim does not include anything about the indomethacin profile or the crystalline phase. The elements of the argued improvement must be reflected in the claim as also seen in MPEP 2106.04. Therefore, the rejections under 35 U.S.C. 101 of claims 1, 9, and 10, stand as rejected below.
Applicant’s arguments, see Remarks, filed 07/29/2026, with respect to claims have been fully considered and are persuasive. The 35 U.S.C. 103 rejections of claims 1, 9, and 10 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) have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Godwin (US 20210020272 A1), Malley (“The application of hierarchical cluster analysis and non-negative matrix factorization.…” 2018), Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017), and Sasaki (US 20190064083 A1).
Claim Objections
Claims 1, 9, and 10 are objected to because of the following informalities: They include the limitation “select clusters including groups of similar profiles from the dendrogram, or receive a user input selecting the clusters.” It is unclear if the user is selecting the clusters based on the dendrogram or by some other means. Specification Para. [0081] teaches that the user selects the clusters based on the dendrogram which leads the examiner to believe that is what applicant intended with the limitation. Therefore, for the purposes of examination the limitation will be viewed as “select clusters including groups of similar profiles from the dendrogram, or receive a user input selecting the clusters from the dendrogram.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 1-4, 6, 7, and 9-11 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 include the limitation “apply non-negative matrix factorization to the measured profile based on the known information.” A plurality of measured profiles is defined previously in the claim making it unclear and indefinite as to what measured profile the non-negative matrix factorization is being applied to. For the purposes of examination, the limitation will be read “apply non-negative matrix factorization to the plurality measured profiles based on the known information.”
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 § 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, 6, 7, 9, and 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 following bolded limitations are considered abstract:
“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 a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer, comprising: 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 receive a user input selecting the clusters,
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 information regarding the predetermined profile that is common to each of the measured profiles included 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. 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 [0032-0036] 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.” Selecting clusters is mental process as it can be done in the human mind using observation, judgement, and opinion.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“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 a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer, comprising: 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,”
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), see also MPEP 2106.05(b)
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 “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 a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer, comprising: 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,” amounts to using a computer as a tool, as processing circuitry is viewed as a generic computer. The x-ray diffractometer just links the claim to x-ray diffraction as seen in MPEP 2106.05. Obtaining data from the X-ray diffractometer is viewed as mere data gathering. 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) and
Sasaki (US 20190064083 A1).
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 “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 a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer, comprising: 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” just ties the claim to a generic computer system and receiving data from a well-known x-ray diffractometer.
Dependent claims 2-4 and 6-7 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.
Therefore, dependent claims 2-4 and 6-7 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
Claim 11 is eligible under 35 U.S.C. 101 because it includes the limitation “identify a crystalline phase contained in the sample based on a profile obtained by the non-negative matrix factorization.” The limitation applies the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment as seen in MPEP 2106.05.
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 and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Godwin (US 20210020272 A1) in view of Malley (“The application of hierarchical cluster analysis and non-negative matrix factorization.…” 2018), Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017), and Sasaki (US 20190064083 A1).
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,
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 a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer,
generate a dendrogram, select clusters including groups of similar profiles from the dendrogram, or receive a user input selecting the clusters,
wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters,
and the known information regarding the predetermined profile that is common to each of the measured profiles included in the clusters.
Malley teaches,
generate a dendrogram, (Introduction teaches “Hierarchical cluster analysis was chosen in preference to non-hierarchical techniques as the robustness and suitability of the cluster assignment are more objectively investigated through the resulting dendrogram, particularly when this is combined with NMF.”)
select clusters including groups of similar profiles from the dendrogram, or receive a user input selecting the clusters, wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, (Methodology section 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. (Methodology section teaches “The number of sites included in each time period and the number of countries within which these sites are located are summarised in Table 1. 154 sites contributed ozone data to at least one 4-year period, of which 49 contributed to every 4-year time period.” (i.e. Ozone data is common to all the sites.))
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 receive a user input selecting the clusters, wherein the non-negative matrix factorization, non-negative matrix factorization is applied to the selected clusters, and the known information regarding the predetermined profile that is common to each of the measured profiles included in the clusters such as that of Malley. One of ordinary skill would have been motivated to modify Godwin, because a dendrogram allows the data to be visualized and allows clusters to be made without predefining the number of groups. Furthermore, the introduction of Malley states that the robustness and suitability of the cluster assignment are more objectively investigated through the resulting dendrogram when it is combined with NMF. Therefore, to increase the suitability of the cluster assignments one would be motivated to combine the prior art.
The combination of Godwin and Malley does not explicitly teach,
a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer,
Xiong teaches,
a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer (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 Malley with a processing apparatus for applying non-negative matrix factorization to a plurality of measured profiles of X-ray powder diffraction obtained by the Xray diffractometer such as that of Xiong. One of ordinary skill would have been motivated to modify the combination of Godwin and Malley, because it shows that the analysis methods applied to the data in Godwin and Malley can be used in combination to analyze XRD data such as seen on Page 138 of Xiong.
The combination of Godwin, Malley, and Xiong does not explicitly teach,
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.
Sasaki teaches,
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. The sample stage 15 rotates about a central axis (axis passing through the plane of the drawing depicting FIG. 2) X0 that passes therethrough. The collimator 9 has a pinhole for narrowing X-rays into a beam shape.”)
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, Malley, and Xiong with 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 the combination of Godwin, Malley, and Xiong, 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.
With respect to claim 2,
Godwin further teaches,
The system according to claim 1, 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 system 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.))
With respect to claim 11,
Godwin further teaches,
The system according to claim 1, wherein the processing circuitry is further configured to identify a crystalline phase contained in the sample based on a profile obtained by the non-negative matrix factorization. (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).” Para. [0058] teaches “For example, in the case that the chemical components include a crystalline component and an amorphous component, the ratio of amorphous to crystalline components may used to derive a measure of the degree of crystallinity in the dispersion.”)
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Godwin (US 20210020272 A1), Malley (“The application of hierarchical cluster analysis and non-negative matrix factorization.…” 2018), Xiong (“Automated Phase Segmentation for Large-Scale X‑ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm;” 2017), and Sasaki (US 20190064083 A1) 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 system 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 system 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to JOSHUA L FORRISTALL whose telephone number is 703-756-4554. The examiner
can normally be reached Monday-Friday 8:30 AM- 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use
the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
