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 .
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 14 and 15 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter. Regarding claim 14, a computer program product comprised of instructions does not fall within the four categories of invention. Regarding claim 15, the broadest reasonable interpretation of “computer-readable storage medium” encompasses non-statutory subject matter such as transitory signals.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2111.04 states “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed”.
Claims 1-13 contain no positively recited method steps, just the preamble “Method for structure elucidation of the structure of an unknown chemical compound from a measured spectrum of a sample” and numerous wherein clauses that do not recite any active steps.
The claims given their broadest reasonable interpretation encompass any mental process or mathematical calculation for relating a structure to a measured spectrum.
Thus claims 1-13 recite a mental process and/or mathematical concepts, which is/are an abstract idea.
Claims 1-11 contain no additional elements. Thus these claims are considered ineligible as directed to an abstract idea without a practical application or significantly more.
Claims 12-13 contain additional elements (computer, data processing apparatus comprising means) recited at a high degree of generality such that they amount to mere instructions to implement the abstract idea, which per MPEP 2106.05(f) means they do not provide a practical application or significantly more and are ineligible.
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 1-15 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. Claim 1 fails to recite any active, positive steps, which per MPEP 2173.05(q) renders the claim indefinite. Claims 2-15 inherit the issue of claim 1. See also MPEP 2111.04 shows that wherein clauses that do not require steps to be performed are not limiting. Claims 2-4, 7, 8 contain the language “preferably”. Per MPEP 2173.05(d):“If stated in the claims, examples and preferences may lead to confusion over the intended scope of a claim.” Thus the scope of these claims is only unclear due to stating a preference. Claim 1 has two distinct instances of “a first machine learning model”. It is unclear if they’re intended to be the same or different. Also any subsequent references to the models are unclear, because it cannot be ascertained which one of the models is being referred to. Regarding claim 5, the generator has no antecedent basis.
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.
Claim(s) 1-3, 7, 8, 10, 12-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “NMR-TS: de novo molecule identification from NMR spectra” to Zhang.
Regarding claim 1, Zhang discloses:
1. Method for structure elucidation of the structure of an unknown chemical compound from a measured spectrum of a sample (figure 1 step 0->Step 1 and see section 2.2), wherein structures of candidate chemical compounds are generated and predicted spectra are generated from the generated structures (abstract “NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra and see figure on page 1, and see page 2 column 2” NMR-TS outputs a list of candidate molecular structures that fit the input spectrum), wherein the predicted spectra are compared with the measured spectrum (Figure 1 step 3), and wherein one of the predicted spectra is selected and the structure corresponding to the selected predicted spectrum is determined as the structure of the unknown chemical compound (section 3 highest WS), and wherein a) a first machine learning model generates the structures of candidate chemical compounds and a second machine learning model generates the predicted spectra from the structures generated by the first machine learning model, and/or b) a first machine learning model generates the structures of candidate chemical compounds, wherein the first machine learning model is trained for generating realistic structures from a molecular and/or empirical formula (Figure 1 step 1 RNN based SMILES generator, section 2.2 NMR-TS pretrains an RNN model using the input SMILES database to obtain an RNN model that can generate various valid SMILES strings depending on the input prefixes), and/or c)a second machine learning model generates the predicted spectra from the generated structures, the second machine learning model having a residual neural network.
Regarding claim 2, Zhang discloses:
2. Method according to claim 1, wherein the first machine learning model has one or more artificial neural networks, preferably graph neural networks, in particular a pair of generative adversarial networks (section 2.2 RNN model; claim scope encompasses one or more neural networks; graph neural networks and adversarial networks are only preferred embodiments).
Regarding claim 3, Zhang discloses:
3. Method according to claim 1, wherein the first machine learning model is trained with a first training dataset, preferably for the generation of realistic structures, in particular from molecular and/or empirical formulas, the first training dataset preferably comprising structures of a plurality of real molecules (section 2.2 NMR-TS pretrains an RNN model using the input SMILES database to obtain an RNN model that can generate various valid SMILES strings depending on the input prefixes, see also section 2.1 SMILES database includes strings showing molecular/empirical formulas).
Claim 7 is rejected for the same reason as claim 1 as it further limits the second model. The second model is only present in the alternative in claim 1. Examiner has cited art for a different alternative.
Claim 8 is rejected for the same reason as claim 1 as it further limits the second model. The second model is only present in the alternative in claim 1. Examiner has cited art for a different alternative.
Regarding claim 10, Zhang discloses: wherein the measured spectrum is an NMR spectrum and/or wherein a spectrum, in particular an NMR spectrum, of the sample is measured (figure 1 step 0).
Regarding claim 12-15, Zhang discloses a computer-based data processing apparatus executing instructions (abstract Python program).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over “NMR-TS: de novo molecule identification from NMR spectra” to Zhang in view of “MolGAN: An implicit generative model for small molecular graphs” to De Cao.
Regarding claim 4-6, Zhang fails to disclose and De Cao discloses:
4. Method according to claim 1; wherein the first machine learning model is trained using a generator and a discriminator, preferably wherein the generator and the discriminator mutually train each other and/or wherein the discriminator is used only in the training and/or not used in actual structure elucidation and/or in an application phase (fig. 1, abstract; discriminator operating only in training is known property of GANs).
5. Method according to claim 1 wherein the first machine learning model, in particular the generator, generates and/or is trained to generate several structures from a given molecular and/or empirical formula, in particular using random variables and/or a random noise generator (fig. 1, abstract; generators in GANS are known to work by creating fake data using a random noise generator).
6. Method according to claim 4, wherein the discriminator is trained to differentiate between real structures, in particular from the first training data set, and structures generated by the generator (fig. 1 abstract known property of discriminator is to differentiate between real and fake data).
It would have been obvious to one of ordinary skill in the art to combine these teachings with Zhang by using generative adversarial neural networks. The motivation for the combination is simple substitution of one element for another to yield predictable results. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). GANs are a well-known alternative to RNN and one of ordinary skill in the art would be free to attempt any alternative.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over “NMR-TS: de novo molecule identification from NMR spectra” to Zhang in view of "A New Algorithm for Reliable and General NMR Resonance Assignment” to Schmidt.
Regarding claim 9, Zhang fails to disclose and Schmidt discloses: wherein an expected or mean value and a corresponding measure of dispersion, in particular a standard deviation, are calculated for each spectrum feature, in particular chemical shift, of the predicted spectrum (page 12819 This range is defined by the statistical mean f(a) and the standard deviationσ(a) (Figure 1b) that can be obtained, for instance, from the Biological Magnetic Resonance Data Bank (BMRB)). It would have been obvious to one of ordinary skill in the art to combine this teaching with Zhang by assessing a plurality of runs/iterations and using mean values+standard deviation. The motivation for the combination is to ensure accuracy of data.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over “NMR-TS: de novo molecule identification from NMR spectra” to Zhang in view of "Introduction to Mass Spectrometry".
Regarding claim 11, Zhang fails to disclose and "Introduction to Mass Spectrometry" discloses wherein the molecular and/or empirical formula is determined by measuring a mass spectrum of the sample (see entire document). It would have been obvious to one of ordinary skill in the art to combine this teaching with Zhang by calculating a formula based on mass spec data. The motivation for the combination is simple substitution of one element for another to yield predictable results. KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398 (2007). Zhang differs from the claimed invention in how the formulae are identified. Having disclosure identifying how to determine a formula from mass spec data would allow one of ordinary skill in the art to determine needed data in a predictable way.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Clevert (US 20220189587 A1) discloses generation of structure representation based on NMR data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Zeender can be reached at 571-272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN A MITCHELL/Primary Examiner, Art Unit 3627
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