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 .
DETAILED ACTION
Claims 1-20 are pending.
Response to Amendment
Applicant amended claim 3 and claim 11 and added new claims 18-20.
The objection to the drawings is withdrawn in view of the amendment.
The objection to the specification is withdrawn in view of the amendment.
The rejection of claim 11 under 35 U.S.C. 112 (b) is withdrawn in view of the amendment.
Maintained Rejection
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 6-7, 12, and 16 remain rejected and claim 20 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lalou (Food chemistry 244 (2018): 266-274, of record in IDS).
Regarding claims 1, 16, and 20, Lalou teaches mixing Hanseniaspora uvarum and Saccharomyces cerevisiae at different ratios (i.e., a selected microbial strain mixture including at least two microbial strains having different quantitative composition ratios) (Table 1 column “H/S”). Lalou teaches performing 27 different fermentation processes of grape must using the different ratios of the microbial strains (Table 1, Abstract) (i.e., concurrently performing fermentation processes for each microbial strain mixture wherein the fermentation processes in each cycle involve culturing a food product with each microbial strain mixture). Lalou teaches determining the responses for each fermentation, such as the cell viability and the concentration of ethanol, alcohols, esters, and acetoin (i.e., determining, for each of the fermentation processes, at least one performance indicator of the microbial strain mixture) (Table 1, para. 2.4.1, 2.4.2). Lalou teaches that Taguchi design and ANOVA test were used to examine the effect of parameters that should be optimized in order to control the alcoholic fermentation of the concentrated grape must (i.e., providing the at least one performance indicator for each of the fermentation processes to a statistical model) (Abstract, para. 2.4.1 and para. 2.4.2). Lalou teaches selecting the optimal conditions that were further tested in next cycle (para 3.1.4) (i.e., a subsequent set of plurality of microbial strain mixtures with different quantitative composition ratios for selection in a next cycle), and teaches that the optimized conditions resulted in an alcoholic product that meets ethanol and residual sugar content requirements (Abstract). Lalou teaches that the fermentations were carried out in duplicate (i.e., repeating the method cycles).
Regarding claim 2, Lalou teaches that fermentation for each strain mixture ratio was repeated more than one time (Table 1, para. 2.4.1).
Regarding claim 3, Lalou teaches that based on the Taguchi design and ANOVA test, an optimized cell ratio was determined to be used in next cycle, and teaches comparing the fermentation by Hanseniaspora uvarum and Saccharomyces cerevisiae mixture to the fermentation by Saccharomyces cerevisiae alone (Table 2) as well as comparing the results of the fermentations with different H/S ratios (Fig. 1).
Regarding claim 4, Lalou teaches multiple fermentations using yeast mixture with different cell ratios and teaches comparing the fermentation results to that of predetermined ratio (Table 1, Run No. 1 vs Run no. 10 and 19, Fig. 1). Lalou teaches that all 27 experiments were carried out in duplicate in 100 mL Erlenmeyer flasks (i.e., the fermentation process is concurrently performed) (para. 2.4.1). Lalou teaches measuring the cell viability and the concentration of ethanol, alcohols, esters, and acetoin for each fermentation (i.e., at least one reference performance indicator of the reference microbial strain mixture is determined) (Table 1). Lalou teaches analyzing the fermentation results from the Taguchi design using statistical software ANOVA, and teaches that the conditions for the subsequent fermentation experiments were selected on the basis of optimized responses taking into account the contribution of each parameter to their formation (para. 2.4.2. Data analysis).
Regarding claim 6, Lalou teaches measuring the acetoin concentration following the fermentation (i.e., acidification indicator) (Table 1, Fig. 1).
Regarding claim 7, Lalou teaches measuring the alcohol concentration following the fermentation (i.e., performance indicator is associated to a fermentation process parameter) (Table 1, Fig. 1).
Regarding claim 12, Lalou teaches that, for each fermentation, multiple responses were recorded, such as the cell viability and the concentration of ethanol, alcohols, esters, and acetoin (Table 1).
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.
Claims 1-10, 12-13, and 16 remain rejected under 35 U.S.C. 103 as being unpatentable over Lalou in view of Fernandez (Control Engineering Practice 103 (2020): 104608).
Regarding claims 1-7, 12, and 16, Lalou teaches mixing Hanseniaspora uvarum and Saccharomyces cerevisiae at different ratios (i.e., microbial strain mixture including at least two microbial strains having different quantitative composition ratios) (Table 1). Lalou teaches performing 27 different fermentation processes of grape must using the different ratios of the microbial strains (Table 1, Abstract). Lalou teaches determining the responses for each fermentation, such as the cell viability and the concentration of ethanol, alcohols, esters, and acetoin (i.e., determining, for each of the fermentation processes, at least one performance indicator of the microbial strain mixture) (Table 1, para. 2.4.1, 2.4.2). Lalou teaches that Taguchi design and ANOVA test were used to examine the effect of parameters that should be optimized in order to control the alcoholic fermentation of the concentrated grape must (i.e., providing the at least one performance indicator for each of the fermentation processes to a statistical model) (Abstract, para. 2.4.1 and para. 2.4.2). Lalou teaches selecting the optimal conditions that were further tested in next cycle (para 3.1.4), and teaches the optimized conditions resulted in an alcoholic product that meets ethanol (79 g/kg) and residual sugar (164 g/kg) content requirements (Abstract). Lalou teaches that fermentation for each strain mixture ratio was repeated 3 times (Table 1). Lalou teaches that based on the Taguchi design and ANOVA test, an optimized cell ratio was determined to be used in next cycle, and teaches comparing the fermentation by Hanseniaspora uvarum and Saccharomyces cerevisiae mixture to the fermentation by Saccharomyces cerevisiae alone (Table 2). Lalou teaches multiple fermentations using yeast mixture with different cell ratios, and teaches comparing the fermentation results to that of predetermined ratio (Table 1, Run No. 1 vs Run no. 10 and 19). Lalou teaches that all 27 experiments were carried out in duplicate in 100 mL Erlenmeyer flasks (i.e., the fermentation process is concurrently performed) (para. 2.4.1). Lalou teaches measuring the cell viability and the concentration of ethanol, alcohols, esters, and acetoin for each fermentation (i.e., at least one reference performance indicator of the reference microbial strain mixture is determined) (Table 1). Lalou teaches analyzing the fermentation results from the Taguchi design using statistical software ANOVA and teaches that the conditions for the subsequent fermentation experiments were selected on the basis of optimized responses taking into account the contribution of each parameter to their formation (para. 2.4.2. Data analysis). Lalou teaches measuring the acetoin concentration following the fermentation (i.e., acidification indicator) (Table 1, Fig. 1). Lalou teaches measuring the alcohol concentration following the fermentation (i.e., performance indicator is associated to a fermentation process parameter) (Table 1, Fig. 1). Lalou does not teach the statistical model is a Bayesian optimization model wherein the objective function to be optimized is approximated by a Gaussian process.
However, Fernandez teaches Bayesian estimators based on Gaussian processes were designed to describe the variation of cell and ethanol concentrations during fermentation of wine in order to make the right decisions to obtain a high
quality product (Abstract). Fernandez teaches that this method allows real-time reporting, without further interventions, of what is happening inside the reactor is proposed to take timely corrective actions according to the criteria of the winemaker, avoiding the risk of losing production or having to face unforeseen expenses.
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lalou’s method by using Bayesian estimators based on Gaussian processes as suggested by Fernandez. One of ordinary skill in the art would be motivated to do so in order to monitor the result of the fermentations, optimize the fermentation, and obtain a high-quality product. Since Lalou and Fernandez teach a desire to monitor the results of fermentation and optimize the reaction using statistical model, there is a reasonable expectation of success.
Regarding claims 8-10, Lalou teaches determining the effect of substrate on the fermentation and teaches using three different must from 3 harvest years (i.e., additives comprises metabolites, vitamins or chemicals) (Fig. 2, para. 3.2). One of ordinary skill in the art would be motivated to optimize the fermentation using different substrates such as must from different years as suggested by Lalou in order to obtain a high-quality product.
Regarding claim 13, Fernandez teaches the method can track the concentration of any desired compound, as long as training data is available (page 2 left column para. 3). One of ordinary skill in the art would be motivated to store the fermentation data in a database in order to be used as training data as suggested by Fernandez.
Claims 1-4, 6-7, 11-12, and 16 remain rejected under 35 U.S.C. 103 as being unpatentable over Lalou in view of Arevalo (Journal of applied microbiology 123.6 (2017): 1360-1372).
Regarding claims 1-4, 6-7, 11-12, and 16, Lalou teaches mixing Hanseniaspora uvarum and Saccharomyces cerevisiae at different ratios (i.e., microbial strain mixture including at least two microbial strains having different quantitative composition ratios) (Table 1). Lalou teaches performing 27 different fermentation processes of grape must using the different ratios of the microbial strains (Table 1, Abstract). Lalou teaches determining the responses for each fermentation, such as the cell viability and the concentration of ethanol, alcohols, esters, and acetoin (i.e., determining, for each of the fermentation processes, at least one performance indicator of the microbial strain mixture) (Table 1, para. 2.4.1, 2.4.2). Lalou teaches that Taguchi design and ANOVA test were used to examine the effect of parameters that should be optimized in order to control the alcoholic fermentation of the concentrated grape must (i.e., providing the at least one performance indicator for each of the fermentation processes to a statistical model) (Abstract, para. 2.4.1 and para. 2.4.2). Lalou teaches selecting the optimal conditions that were further tested in next cycle (para 3.1.4), and teaches the optimized conditions resulted in an alcoholic product that meets ethanol (79 g/kg) and residual sugar (164 g/kg) content requirements (Abstract). Lalou teaches that fermentation for each strain mixture ratio was repeated 3 times (Table 1). Lalou teaches that based on the Taguchi design and ANOVA test, an optimized cell ratio was determined to be used in next cycle, and teaches comparing the fermentation by Hanseniaspora uvarum and Saccharomyces cerevisiae mixture to the fermentation by Saccharomyces cerevisiae alone (Table 2). Lalou teaches multiple fermentations using yeast mixture with different cell ratios, and teaches comparing the fermentation results to that of predetermined ratio (Table 1, Run No. 1 vs Run no. 10 and 19). Lalou teaches that all 27 experiments were carried out in duplicate in 100 mL Erlenmeyer flasks (i.e., the fermentation process is concurrently performed) (para. 2.4.1). Lalou teaches measuring the cell viability and the concentration of ethanol, alcohols, esters, and acetoin for each fermentation (i.e., at least one reference performance indicator of the reference microbial strain mixture is determined) (Table 1). Lalou teaches analyzing the fermentation results from the Taguchi design using statistical software ANOVA and teaches that the conditions for the subsequent fermentation experiments were selected on the basis of optimized responses taking into account the contribution of each parameter to their formation (para. 2.4.2. Data analysis). Lalou teaches measuring the acetoin concentration following the fermentation (i.e., acidification indicator) (Table 1, Fig. 1). Lalou teaches measuring alcohol concentration following the fermentation (i.e., performance indicator is associated to a fermentation process parameter) (Table 1, Fig. 1). Lalou does not teach selecting microbial strains from a group of at least 10 different microbial strains.
However, Arevalo teaches a group of yeast with at least 10 different microbial strains including Saccharomyces cerevisiae (Table 2), and teaches that the yeasts are used in food industries as starter culture for fermentation (Title).
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lalou’s method by selecting two yeasts from the group of yeasts taught by Arevalo, as suggested by Arevalo. One of ordinary skill in the art would be motivated to do so in order to test different yeasts and their effect on the fermentation.
New Rejection Necessitated by the Amendment
Claims 1-4, 6-7, 11-12, 16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lalou in view of Bourdichon (Bourdichon et al. "Food fermentations: microorganisms with technological beneficial use." International journal of food microbiology 154.3 (2012): 87-97).
Regarding claims 1-4, 6-7, 11-12, and 16, Lalou teaches mixing Hanseniaspora uvarum and Saccharomyces cerevisiae at different ratios (i.e., microbial strain mixture including at least two microbial strains having different quantitative composition ratios) (Table 1). Lalou teaches performing 27 different fermentation processes of grape must using the different ratios of the microbial strains (Table 1, Abstract). Lalou teaches determining the responses for each fermentation, such as the cell viability and the concentration of ethanol, alcohols, esters, and acetoin (i.e., determining, for each of the fermentation processes, at least one performance indicator of the microbial strain mixture) (Table 1, para. 2.4.1, 2.4.2). Lalou teaches that Taguchi design and ANOVA test were used to examine the effect of parameters that should be optimized in order to control the alcoholic fermentation of the concentrated grape must (i.e., providing the at least one performance indicator for each of the fermentation processes to a statistical model) (Abstract, para. 2.4.1 and para. 2.4.2). Lalou teaches selecting the optimal conditions that were further tested in next cycle (para 3.1.4) and teaches that the optimized conditions resulted in an alcoholic product that meets ethanol (79 g/kg) and residual sugar (164 g/kg) content requirements (Abstract). Lalou teaches that fermentation for each strain mixture ratio was repeated 3 times (Table 1). Lalou teaches that based on the Taguchi design and ANOVA test, an optimized cell ratio was determined to be used in next cycle and teaches comparing the fermentation by Hanseniaspora uvarum and Saccharomyces cerevisiae mixture to the fermentation by Saccharomyces cerevisiae alone (Table 2). Lalou teaches multiple fermentations using yeast mixture with different cell ratios and teaches comparing the fermentation results to that of predetermined ratio (Table 1, Run No. 1 vs Run no. 10 and 19). Lalou teaches that all 27 experiments were carried out in duplicate in 100 mL Erlenmeyer flasks (i.e., the fermentation process is concurrently performed) (para. 2.4.1). Lalou teaches measuring the cell viability and the concentration of ethanol, alcohols, esters, and acetoin for each fermentation (i.e., at least one reference performance indicator of the reference microbial strain mixture is determined) (Table 1). Lalou teaches analyzing the fermentation results from the Taguchi design using statistical software ANOVA and teaches that the conditions for the subsequent fermentation experiments were selected on the basis of optimized responses taking into account the contribution of each parameter to their formation (para. 2.4.2. Data analysis). Lalou teaches measuring the acetoin concentration following the fermentation (i.e., acidification indicator) (Table 1, Fig. 1). Lalou teaches measuring alcohol concentration following the fermentation (i.e., performance indicator is associated to a fermentation process parameter) (Table 1, Fig. 1). Lalou does not teach selecting microbial strains from a group of at least 50 different microbial strains.
However, Bourdichon teaches an inventory of at least 50 microorganisms used in food fermentations and that these strains have a technologically beneficial use. The strains include Hanseniaspora uvarum and Saccharomyces cerevisiae (Abstract, Table 3).
It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Lalou’s method by selecting two yeasts from the group of yeasts taught by Bourdichon, as suggested by Bourdichon. One of ordinary skill in the art would be motivated to do so in order to test different yeasts and their effect on the fermentation.
Response to Arguments
Applicant's arguments filed 08/13/2026 have been fully considered but they are not persuasive.
Applicant argues that the claim requires an iterative arrangement in which each cycle builds on the results of the cycle before it, and in which the statistical model (not the operator) determines the experiments to be run next. Applicant argues that Lalou does not disclose this iterative arrangement and that the analysis identified only one combination of conditions.
In response to the argument, Lalou teaches mixing two microbial strains at different ratio (i.e., selecting a set of plurality of microbial strain mixtures having different quantitative composition ratios), performing fermentation of grape must for each ratio (i.e., concurrently performing fermentation processes for each microbial strain mixture wherein the fermentation processes in each cycle involve culturing a food product with each microbial strain mixture), determining the cell viability and concentration of ethanol, alcohol, ester, and acetoin (i.e., determining, for each of the fermentation processes, at least one performance indicator), using a statistical model, Taguchi and ANOVA, to examine the effect of parameters that should be optimized in order to control the alcoholic fermentation (i.e., providing the at least one performance indicator for each of the fermentation processes to a statistical mode), and selecting the optimal conditions that were further tested in next cycle. It is understood from Lalou that based on the information received from the statistical model, an optimal ratio was chosen and tested in next cycle (i.e., statistical model configured to optimize and determine a subsequent set of plurality of microbial strain mixtures with different quantitative composition ratios for selection in a next cycle). Lalou teaches that the fermentations were carried out in duplicate (i.e., repeating the method cycles). The claim does not limit the number of repetitions of the method cycle, the statistical model, nor the number of subsequent set of plurality of microbial strain mixtures with different quantitative composition ratios. The single combination Applicant is arguing that Lalou teaches reads on the instant limitation “a subsequent set of plurality of microbial strain mixtures with different quantitative composition ratios for selection in a next cycle”.
Furthermore, the limitation “statistical model which is configured to optimize a predefined objective function, wherein the statistical model is configured to determine, based on said determined performance indicators, a subsequent set of plurality of microbial strain mixtures with different quantitative composition ratios for selection in a next cycle” do not require performing active steps and recite features of the statistical model.
Applicant argues that claim 4 requires a reference microbial strain mixture having a predetermined composition ratio to be included in the selected set and fermented concurrently with the other mixtures, and argues that the only reference in Lalou is a reference substrate, not a reference strain mixture.
In response to the argument, Lalou teaches in Table 1 different microbial strain mixture having a predetermined ratio (Runs 1-27). Any one of these mixtures can be considered a reference set fermented with the other mixtures.
Applicant argues that Fernandez teaches Bayesian state estimators to estimate online cell and ethanol concentrations and argues that combining Lalou with Fernandez yields, Lalou's one-shot Taguchi array with a Gaussian process estimator watching the reactor but not the iterative arrangement of claim 1, because neither reference contains the feature that makes the arrangement iterative.
As explained above, Lalou reads on the limitation of claim 1. Fernandez is relied upon for teaching another statistical model used to optimize fermentation. Since Lalou and Fernandez both teach a desire to optimize fermentation using statistical models, there is a reasonable expectation of success.
Applicant argues that claim 11 does not merely require that at least 10 strains be named somewhere but requires a group of microbial strains, from which microbial strains are selectable for forming microbial strain mixtures with chosen quantitative composition ratios.
In response to the argument, Lalou teaches two strains. Arevalo is relied upon for teaching that other strains are used in fermentation. Arevalo teaches a group of microorganisms that are suitable for use in fermentation and includes the strain of Lalou. One of ordinary skill in the art can pick other strains from the group in order to perform a fermentation. In response to 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). The rejection of the claims did not rely on inherency nor optimization.
Applicant argues that new claim 20 requires that for each method cycle at least some of the mixtures of the selected set are determined with a quantitative composition ratio different from that of any mixture determined in any preceding method cycle and that Lalou cannot meet this on any reading, because the conditions carried forward from the Taguchi analysis used a single H/S ratio of 10: 1 across all three substrates.
In response to the argument, Lalou teaches in Table 2 that run No. 1 has a ratio of mixed yeast of 1 whereas other cycles, for example run No. 10 or 19 have different yeast ratios of 10 and 100, respectively.
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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/MARY A CRUM/ Examiner, Art Unit 1657
/THANE UNDERDAHL/ Primary Examiner, Art Unit 1699
