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
Claims 1 and 12-13 are amended.
Claim 15 is canceled.
Claims 1-14 are pending.
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 2-4, 6-7, and 11-12 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 2-4, 6-7, and 11-12 “recite the first process” and/or “the second process”, however claim 1 has been amended to recite the first batch process and the second batch process, thus it is now unclear what the first process refers to as it could be the first process data or the first batch process and similarly for the second. For the purposes of examining they are considered to refer to the first and second batch processes respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-9, 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bartee (US 20080109200 A1) in view of Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression Fernando di Sciascio 2008, hence forth NPL1.
In claim 1, Bartee discloses a computer-implemented (Par. 26) method for predicting performance of one or more strains in one or more processes (Par. 26 “batch”), the strains being capable of fermentation of biomass (Par. 31) for production of at least bio-ethanol (Par. 167), the method comprising receiving a first process data set related to a performance of a first strain in a first batch process for producing bio-ethanol (Par. 39, 97-101 “performance” “at least one batch process”) at a first site (Par. 35 “lab values”, examiner considers the lab values to be reference values determined in a Lab, i.e. a first site), receiving a second process data set related to a performance of a second strain in the first batch process for producing bio-ethanol at the first site (Par. 104, 114 “recent process history”), receiving a third process data set related to a performance of the first strain in a second batch process (Par. 104, 114 “recent process history”) for producing bio-ethanol at a second site (Par. 35 ‘real-time measurements of the biofuel in the fermentation system’ examiner considers the real time measurements to be taken at a separate location from the lab), the second site being different from the first site (Par. 35 lab values vs real time values), and wherein the first, second and third process data sets each include one or more process profiles and/or process responses (Par. 35, 111), determining a first correlation between the first process data set and the second process data set (Par. 40, 114 also see Fig. 3, examiner notes that the outputs are compared to get the best possible target and used in future processes), and determining a second correlation between the first process data and the third process data (Par. 40, 114 also see Fig. 3, examiner notes that the outputs are compared to get the best possible target and used in future processes), a fourth process data set related to a performance of the second batch strain in the second process for producing bio-ethanol at the second site by missing data imputation (Par. 104, 114 “recent process history”), wherein the fourth process data set is estimated based on the first correlation and the second correlation (Par. 40, 114 also see Fig. 3, examiner notes that the outputs are compared to get the best possible target and used in future processes); and using fourth process data set to produce bio-ethanol using the second strain in the second batch process at the second site (Par. 114 “a predictive future horizon”, Par. 167 “Thus, the biofuel process may be controlled in accordance with these trajectories to produce biofuel in a substantially optimum manner” examiner notes that predictive modeling is used for the trajectory to control the process); wherein the first batch process at the first site and the second batch process at the second site are carried out at remote locations with respect to each other (see Fig. 5, 7A, Par. 35 “lab values”, examiner considers the lab values to be taken in a lab which is a separate location from the production plant, thus meets the BRI of “at remote locations with respect to each other”).
Bartee does not explicitly disclose reconstructing a fourth process data set related to a performance of the second strain in the second batch process for producing bio-ethanol at the second site by missing data imputation and using the reconstructed fourth process data set to produce bio-ethanol using the second strain in the second batch process at the second site. (Emphasis added)
NPL 1 teaches reconstructing a fourth process data set related to a performance of the second strain in the second batch process for producing biomass (Fig. 3, section 4.2) via fermentation by missing data imputation (section 4.1 “missing data”) and using the reconstructed fourth process data set as a prediction of the performance of the second strain in the second batch process (Section 6 “predictive”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to do reconstructing a fourth process data set related to a performance of the second strain in the second batch process for producing bio-ethanol at the second site by missing data imputation and using the reconstructed fourth process data set to produce bio-ethanol using the second strain in the second batch process at the second site based on the teachings of NPL1 in Bartee in order to find the best fit for the data (NPL1 section 4.1) thus leading to a more accurate prediction.
In claim 2, Bartee does not explicitly disclose wherein the reconstructed fourth process data set is used for fitting a predictive model configured to predict the performance of the second strain in the second process at the second site.
NPL1 teaches wherein the reconstructed process data set is used for fitting a predictive model configured to predict the performance of the second strain in the second process (NPL1 section 4.1, section 6).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to wherein the reconstructed fourth process data set is used for fitting a predictive model configured to predict the performance of the second strain in the second process at the second site based on the teachings of NPL1 in Bartee in order to find the best fit for the data (NPL1 section 4.1) thus leading to a more accurate prediction.
In claim 3, Bartee discloses wherein a predictive model is employed for adjusting operational parameters in order to improve the performance of the second strain in the second process at the second site (Par. 114).
In claim 4, Bartee discloses wherein the first process at the first site is carried out in a laboratory (Par. 35 “lab values”), and wherein the second process at the second site is carried out in a plant (Par. 35 ‘real-time measurements of the biofuel in the fermentation system’), the plant optionally being an industrial-scale bio-ethanol production plant (is optional and thus not required by the BRI to meet the claim).
In claim 5, Bartee discloses wherein one or more small- scale laboratory experiments are carried out in the laboratory for determining at least one of the first process data set or the second process data set (Par. 167).
In claim 6, Bartee discloses wherein the first process at the first site is modelled by means of a computational model (Par. 116), wherein the computational model is used for determining at least one of the first process data set or the second process data set (Par. 116).
In claim 7, Bartee discloses wherein missing data related to the performance of the second strain in the second process at the second site is predicted at least in part using a regression model (Par. 118, 139).
In claim 8, Bartee does not explicitly disclose wherein the regression model includes at least one of: multivariate regression, principal component regression, partial least squares regression, or trimmed scores regression for missing data imputation.
NPL1 teaches wherein the regression model includes at least one of: multivariate regression, principal component regression, partial least squares regression, or trimmed scores regression for missing data imputation (Section 3.1 page 5 “multivariate regression”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to wherein the regression model includes at least one of: multivariate regression, principal component regression, partial least squares regression, or trimmed scores regression for missing data imputation based on the teachings of NPL1 in Bartee in order to find the best fit for the data (NPL1 section 4.1) thus leading to a more accurate prediction.
In claim 9, Bartee does not explicitly disclose wherein prior to determining the second correlation, data arrays in the data set relating to different batches in the first process data and the third process data are shuffled with respect to each other.
NPL1 teaches wherein prior to determining the second correlation, data arrays in the data set relating to different batches in the first process data and the third process data are shuffled with respect to each other (section 2.2 page 4 “switching”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to wherein prior to determining the second correlation, data arrays in the data set relating to different batches in the first process data and the third process data are shuffled with respect to each other based on the teachings of NPL1 in Bartee in order to find the best fit for the data (NPL1 section 4.1) thus leading to a more accurate prediction.
In claim 11, Bartee does not explicitly disclose wherein missing data related to the performance of the second strain in the second process at the second site is predicted at least in part using a trained artificial neural network model.
NPL1 teaches wherein missing data related to the performance of the second strain in the second process at the second site is predicted at least in part using a trained artificial neural network model (section 1 page 2 and section 3.1).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to wherein missing data related to the performance of the second strain in the second process at the second site is predicted at least in part using a trained artificial neural network model based on the teachings of NPL1 in Bartee in order to find the best fit for the data (NPL1 section 4.1) thus leading to a more accurate prediction.
In claim 12, Bartee discloses wherein the first process at the first site and the second process at the second site are carried out in industrial-scale bio-ethanol production plants different from each other (see Fig. 5, 7a).
In claim 13, Bartee discloses wherein the process data sets include for a plurality of time points a value indicative for at least one of a sugar consumption, ethanol production, pH value, reaction temperature, composition of biomass, enzyme composition, yeast cell count, or glycerol production (Par. 74 “sugar” 167 “ethanol”).
In claim 14, Bartee discloses computational means for carrying out the method according to claim1 (Par. 26)
Claim(s) 10 are rejected under 35 U.S.C. 103 as being unpatentable over Bartee in view of NPL1 in view of KASKAO-PEREJRA (RU 2553550 C2) translation attached.
In claim 10, Bartee in view of Npl1 teaches all of claim 9. Bartee does not explicitly disclose wherein the data arrays are shuffled randomly or pseudo-randomly.
KASKAO-PEREJRA teaches wherein the data arrays are shuffled randomly or pseudo-randomly (page 70 last paragraph “random shuffles”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled wherein the data arrays are shuffled randomly or pseudo-randomly based on the teachings of KASKAO-PEREJRA in the combination of Bartee and NPL1 in order to determine a lack of correlation for the data (KASKAO-PEREJRA page 70 last paragraph) thus leading to a more accurate prediction.
Response to Arguments
Applicant's arguments filed 10/23/2025 have been fully considered but they are not /persuasive. Regarding applicant’s 103 arguments on pages 5-9, the examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “unique parameters need to be considered in the fermentation of biomass into bio-ethanol (Specification at p. 3, lines 3-16). One of the main problems of bio-ethanol production from biomass is that variety exists between batches or sources of biological feedstock at different sites. For example, batches or sources of feedstock may vary due to a different climate or a different soil, a different variant of said feedstock, etc. (Specification at p. 6, lines 24-28). In addition, strains may vary in their ability to ferment biomass in the production of bio-ethanol. Advantageously, the claimed invention provides for a method that is able to predict and optimize the performance of a particular strain in the production of bio-ethanol in a batch process at a certain site, such as a production plant, where no data are available for the particular strain at the site”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Further the examiner notes in regards to NPL1, applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Thus, the rejections of claims 1-14 are maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20040002108 A1, Predicting The Significance Of Single Nucleotide Polymorphisms (SNPs) Using Ensemble-based Structural Energetics; US 20130130334 A1, BIOFUEL AND ELECTRICITY PRODUCING FUEL CELLS AND SYSTEMS AND METHODS RELATED TO SAME; US 20100093046 A1, ENERGY PRODUCTION WITH HYPERTHERMOPHILIC ORGANISMS.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857