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 IDS filed 12/05/2019 have been considered by the Examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to EP19213900.4 filed 12/05/2019.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-19 are drawn to a method, so a process.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. generating a full data cluster set from the full raw data set obtained by grouping full scan 1ons according to isotope and adduct values.
2. generating a SWATH® data cluster set from the raw SWATH® data set obtained by grouping fragment ions according to retention time and mass values.
3. aligning the SWATH® data cluster set with the full data cluster set to generate characteristic profile for each extracted metabolite.
4. comparing the characteristic profile of each extracted metabolite obtained with a reference library of characteristic profiles of metabolites to provide the metabolic content of the biological sample.
5. performing a simple linear regression model (SLRM) analysis for the full raw data set and SWATH® data cluster set to generate a SLRM value for the metabolite (claim 16)
6. selecting those metabolites which have a SLRM correlation coefficient of at least 0.7 as the signature (claim 16).
Dependent claims 1-15 and 17-19 further recite analysis and informatics steps that are drawn to abstract ideas and are therefore judicial exceptions.
Analysis of Recited Abstract Ideas
The above listed steps are drawn to abstract ideas which can be performed by the human mind or with math. Clustering mass spectrometry data is an abstract idea drawn to organizing numerical information which can be accomplished by the human mind or with math. Aligning and comparing clusters can also be performed by the mind. Linear regression is a mathematical concept and reads on math per se. Selecting metabolites is a mental process. Under Step 2A Prong One, the claims are found to recite steps of analyzing data which can be accomplished with the human mind, including linear regression which is math that can also be performed as a mental process.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a comparison step that provides a metabolic content of the biological sample. The claims result in producing information and do not recite any additional elements that integrate the abstract idea into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. providing one or more samples of extracted metabolites from the biological sample.
2. performing a chromatography coupled mass spectrometry analysis of the extracted metabolites to generate a full raw data set for full scan ions.
3. performing a tandem mass spectrometry analysis of the extracted metabolites
4. wherein the chromatography coupled mass spectrometry analysis is performed by liquid or gas chromatography and the mass spectrometry is performed by MS/MS or QToF.
Analysis of Additional Elements
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of providing extracted metabolites and performing chromatography coupled mass spectrometry analysis and tandem mass spectrometry analysis is well known, routine and conventional. Liquid or gas chromatography, MS/MS or QToF are all well known, including LC-MS which is commonly used for metabolite analysis. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112-2nd paragraph
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 1-19 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 pre-AIA the applicant regards as the invention.
Claims 1, 2, 11, and 16 recite SWATH data set wherein SWATH is a trademark. According to MPEP 2173.05(u):
“If the trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of the 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112 second paragraph.”
While a dataset is not a material or product per se, the issue with indefiniteness is the same as one of ordinary skill would not know that the metes and bounds of a SWATH data set are. MPEP 2173.05(u) also states:
“It is important to recognize that a trademark or trade name is used to identify a source of goods, and is not the name of the goods themselves.”
Therefore it is suggested that the claims be amended to instead recite SWATH as a process or method for collecting the data rather than the name of the data itself.
Claim 12 recites “approximately 5 Daltons, preferably approximately 1 Dalton.” The claim is indefinite because it is not clear whether the term “preferably” further limits the claim or not and under which conditions one would know to select a widow width of 1 Dalton. Furthermore, the claim recites “approximately,” which is interpreted as “about” in MPEP 2173.05(b) III. Approximations. It is unclear what the metes and bounds of “approximately” 5 Daltons or 1 Dalton are and where the cutoff to qualify as a 5 Dalton or 1 Dalton widow width would be. For example, would 5.9 Daltons qualify as 5 Daltons?
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-10, 12-15 and 18-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al. (US 2007/0176088) in view of Kang et al. (Journal of Pharmaceutical Analysis, vol. 10 (2020) pgs. 588-596; IDS filed 7/7/2022).
Li et al. teach providing a sample of metabolites (Abstract and par. 0021)(i.e. (i.) providing one or more samples of extracted metabolites from a biological sample) and analyzing by liquid chromatography mass spectral (LC-MS) analysis to generate (par. 0023) (i.e. (ii) performing chromatography coupled mass spectrometry analysis of the metabolites to generate a full raw data set for full scan ions), as in claim 1.
Li et al. teach grouping adducts and isotopes into clusters (par. 0026)(i.e. (iii) grouping full scan ions according to isotope and adduct values to generate a full data cluster from the raw data), as in claim 1.
Li et al. teach grouping isotopes and adducts to facilitate differential profiling by comparing properties of each compound to the spectral behavior of known compounds to identify compounds in the sample (par. 0027)(i.e. (vii) comparing the characteristic profile of each extracted metabolite with a reference library to provide metabolic content of the biological sample), as in claim 1.
Li et al. do not teach performing tandem mass spectrometry analysis of extracted metabolites with mass selection windows to generate raw SWATH data set for fragment ions, as in claim 1, step (iv).
Li et al. teach (par. 0085) grouping peaks by retention time and m/z to form 3-D peaks (i.e. clusters); Li et al. defines 3-D peaks as clusters (par. 0023). However, Li et al. do not teach generating SWATH data cluster set from SWATH data set by grouping ions according to retention time and mass values, as in claim 1, step (v).
Li et al. teach (par. 0031) that the samples are differentially profiled by comparing the extracted features (i.e. the filtered output) with the filtered output for a second biological sample, or by comparing the filtered output with molecular features extracted from known compounds. However, Li et al. do not teach aligning the SWATH data cluster set with the full data cluster set to generate characteristic profile for each extracted metabolite, as in claim 1, step (vi).
Kang et al. teach performing mass spectrometry on metabolites with the method of sequential window acquisition of all theoretical fragment-ion spectra (SWATH)(Abstract); Kang et al. teach variable isolation windows (Abstract and page 290, col. 1, par. 3) to produce clusters peaks for fragment ions (page 593, col. 1, par. 1-2 and Figure 4)(i.e. performing tandem mass spectrometry analysis of extracted metabolites with mass selection windows to generate raw SWATH data set for fragment ions), as in claim 1, step (iv).
Kang et al. teach grouping metabolite ions according to m/z and retention time ranges of 8.1-8.2 min and 11-12 min. (page 592, col. 1, lines 12-22) and grouping metabolites M1, M2, M3, M5, M6, M9, M10, M19, M20 and M23 according to their molecular weight and retention time (page 593, col. 2, par. 2); Kang et al. teach Table 2 and Table 3 of the MS2 data for ion fragments of compounds and the associated retention time tR (i.e. generating SWATH data cluster by grouping fragment ions according retention time and mass values), as in claim 1, step (v).
Regarding claim 1,. step (vi) of aligning SWATH data clusters with the full data cluster to generate a characteristic profile for each extracted metabolite, it would be obvious to one of skill in the art to combine the teachings of Li et al. for differential profiling mass spectrometry data of samples including clustered data with the teaching of Kang et al. for creating data peaks and groups of SWATH data according to retention time and mass. Li et al. also teach clustering their mass spectrometry data according to retention time and m/z (par. 0023 and 0031). Therefore combining the teachings of Li et al. and Kang et al. would be a combination of known elements to achieve a predictable result, as set forth in KSR. Both Li et al. and Kang et al. teach creating clusters of peaks, which Li et al. describes as “clusters.” Li et al. teach that samples are then differentially profiled by comparing the extracted features (i.e. the filtered output) with the filtered output for a second biological sample, or by comparing the filtered output with molecular features extracted from known compounds. It would be obvious to one of ordinary skill to substitute the “second biological sample” of Li et al. for the SWATH data of Kang et al. and then compare the aligned mass spectral data and SWATH data to a reference library which reads on the “known compounds” as taught by Li et al. (par. 0031). Such is a combination of known elements that would achieve a predictable result.
Regarding dependent claims 2-10, 12-15 and 18-19
Kang et al. teach mass, intensity (Figure 4) and retention time (Tables 2 and 3), as in claims 2-3.
Kang et al. teach metabolites according to their mass, retention time, intensity spectra for ions (Tables 2 and 3) and Li et al. teach isotope and adduct value (par. 0025-0026) and grouping according to retention time, m/z, isotope clustering and adduct formation (par. 0028), as in claim 4.
Li et al. teach differential profiling of metabolites (par. 0076) including comparison of known compounds from a biological sample and manual peptide mass fingerprinting (par. 0075)(i.e. reference library comprises predetermined characteristics of predetermined metabolites, determined from samples containing the compounds), as in claims 5 and 6.
Li et al. teach identification of compounds in a sample by profiling against known compounds (par. 0027), as in claim 7.
Li et al. teach differential profiling (par. 0020) and plotting the difference between samples which would make obvious creating a score because one of ordinary skill would know to create a value or rank to express difference, as in claim 8.
Li et al. teach profiling multiple samples (par. 0029) with different abundance (par. 0029), as in claims 9 and 10.
Regarding claims 12 and 13, Kang et al. teach a method of varying window with (page 590, col. 1-2) and software for selecting needed window with (Figure 1) wherein it would be obvious to one of skill in the art to modify window width based on the mass range needed to span, as taught by Kang et al.
Li et al. teach LC-MS (par. 0033) and TOF0MS (par. 0034), as in claims 14-15.
Li teach samples of cells or tissue (par. 0032 and 0076) , urine (par. 0034) or blood (par. 0064)(i.e. bodily fluid), as in claim 18.
Li teach samples from plants (par. 0021, 0078, 0081), as in claim 19.
Claims 11, 16 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al. in view of Kang et al. as applied to claims 1-10, 12-15 and 18-19 above, and further in view of Callender et al. (Journal or Pharmaceuticals, vol. 10 (2020) pgs. 588-596).
Li et al. in view of Kang et al. make obvious claims 1-10, 12-15 and 18-19 as set forth above.
Li et al. in view of Kang et al. make obvious claim 16, steps (i) to (vii), which recite the same process steps as claim 1, step (i)-(vii).
Regarding claim 17 which depends from claim 16, Li et al. teach identification of compounds in a sample by profiling against known compounds (par. 0027) which suggests signature metabolites are analyzed, as in claim 17.
Li et al. in view of Kang et al. do not teach performing a simple linear regression model (SLRM) analysis for the raw data set and SWATH data cluster to generate a SLRM value, as recited in claim 11 and claim 16, step (viii).
Li et al. in view of Kang et al. do not teach selecting those metabolites which have a SLRM correlation coefficient of at least 0.7 as a signature.
Callender et al. however teach methods of quantifying metabolites (Diacylglycerols) with mass spectrometry and linear regression; a mass spectral data processing algorithm incorporates a multiple linear regression model (Title and Abstract).
Callender et al. teach creating mass spectra of adducts and comparing mass spectra peaks to a control (Figure 1). Callender et al. teach a linear regression model and calculating coefficients (Table 3).
Callender et al. do not specifically teach a selecting metabolites that have a correlation of at least 0.7 as the signature. However, the claim is not specific with regard to which metabolites are being analyzed, under what conditions or reagents and how the mathematics of the linear regression model is applied or calibrated so as to require “0.7” as the signature coefficient. Therefore one of ordinary skill would know how to calibrate the data or initial samples to arrive at a coefficient of “0.7.” The combination of Callender et al. and what is well known would be obvious.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have combined the method of Li et al. in view of Kang et al. which make obvious clustering and comparing raw mass spectrometry data with SWATH data for metabolites, with the method of Callender et al. for performing linear regression analysis on mass spectrometry data sets. Callender et al. teach that linear regression is useful for calibration and quantification of molecular species in a sample (Abstract and pages 264-265, connecting par.) One of skill in the art would have had a reasonable expectation of success at performing linear regression on the data clusters/peaks of Li et al. and Kang et al. because Callender et al. also teach mass spectrometry data, including raw mass spectral data of isotopes (page 271, col. 2, par. 4) and adduct clusters (Figure 1 and page 267, col. 2, par. 2).
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
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/Anna Skibinsky/
Primary Examiner, AU 1635