DETAILED ACTION
Applicant’s response, filed 10 Sept. 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10 Sept. 2026 has been entered.
Status of Claims
Claim 19 is cancelled.
Claims 1-18 and 20-21 are pending.
Claims 6-10 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 09 Jan. 2026.
Claims 1-5, 11-18, and 20-21 are rejected.
Priority
The effective filing date of the claimed invention is 03 June 2024.
Claim Interpretation
Claims 1 and 21 recite “multi-omics data”. Applicant’s specification at para. [0911] discloses that the collection of the multi-omics dataset comprises gene expression, metabolite levels, and metabolic fluxes. Accordingly, under the broadest reasonable interpretation of the claim, multi-omics data is interpreted to encompass any two or more “omics” data, such as metabolite levels and metabolite fluxes (i.e. metabolomics and fluxomics).
Claims 1 and 21 recite “rapid sampling system”, which in light of Applicant’s specification at para. [0045], is understood by one of ordinary skill in the art to refer to an automated reactor sampling system.
Claim Rejections - 35 USC § 112(a)
The rejection of claims 1-5, 11-18, and 20-21 under 35 U.S.C. 112(a) in the Office action mailed 10 June 2026 has been withdrawn in view of claim amendments received 10 Sept. 2026.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-5, 11-18, and 20-21 rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention. This rejection is newly recited after further consideration of the claims.
Claims 1 and 21, and claims dependent therefrom, recite “performing automated parallelized testing…by collecting dynamic multi-omics data via a rapid sampling system, wherein collecting the dynamic multi-omics data comprises: automatically drawing samples…and dispensing a cryogenic quenching agent…” (claim 1) and “A system comprising: one or more computers; a rapid sampling system; a bioreactor; and one or more storage devices…cause the one or more computers to perform operations…the operations comprising:….performing automated parallelized testing of the selected proper subset of the set of variants by collecting dynamic multi-omics data via the rapid sampling system, wherein collecting…comprises: automatically drawing samples… , and dispensing a cryogenic quenching agent” (claim 21).
However, Applicant’s specification does not provide support for collecting multi-omics data via a rapid sampling system. Instead, Applicant’s specification at para. [1445] discloses the rapid sampling system automates the collection of physical samples, and may be integrated with separate analytical and mass spectrometry instruments by coordinating the physical transfer of these samples to the analytical and mass spectrometry instruments via robotic handling ([0045]; [1445]). Applicant’s specification at FIG. 21 discloses the rapid sampling system, which does not include any analytical or mass spectrometry instruments. Therefore, Applicant’s specification provides support for the rapid sampling system collecting samples, that are then analyzed by other instruments to obtain the multi-omics data. However, Applicant’s specification does not provide support for the rapid sampling system that directly obtains the multi-omics data.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitation above recited in claims 1 and 21, and claims dependent therefrom, to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. THS IS A NEW MATTER REJECTION. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claims 1 and 21, and claims dependent therefrom, recite “automatically adjusting controls of a bioreactor to implement the adjusted biological synthesis process which reduces the at least one bottleneck” and previously recites “the adjusted biologic synthesis process includes at least one variant of the set of variants that reduces the at least one bottleneck”. Accordingly, claims 1 and 21 encompass automatically adjusting controls of a bioreactor to implement an adjusted biologic synthesis process including a variant of a biologic synthesis process.
The only mention of adjusting controls of the bioreactor in Applicant’s specification are at para. [0622]-[0623] and [0633]-[0634]. Applicant’s specification at para. [0622]-[0623] and [0633]-[0064] describes a process comprising receiving information about a strain of an organism, receiving a set of bioreactor process conditions for a bioreactor containing the strain, generating a prediction of performance of the strain using the set of bioprocess reactor conditions, determining adjusted bioreactor process conditions based on predicted performance, and automatically adjusting controls of the bioreactor based on the adjusted bioprocess conditions, wherein adjusting controls comprises adjusting feed rates, pH levels, temperature of dissolved oxygen levels. Accordingly, Applicant’s specification provides support for adjusting bioprocess reactor conditions according to a set of bioreactor conditions that were determined to result in good performance for some predetermined strain.
In contrast, claim 1 involves automatically adjusting controls of a bioreactor to implement an adjusted biologic synthesis process including a variant that reduces the bottleneck, which encompasses implementing a biologic synthesis process beyond merely adjusting conditions of a bioreactor. For example, dependent claim 3 recites that the set of variants (for which at least one is included in the adjusted biologic synthesis process) comprises “a biologic product substitution variant, a biological product elimination variant, a biologic product expression variant, a biologic product activation variant, a biologic product activity variant, or a biologic product transformation variant”. Claim 4 further limits the bottleneck to include “a protein expression level bottleneck…a product activation bottleneck, a process stability bottleneck”, etc. Accordingly, claim 1 determines an adjusted biologic synthesis process comprising a variant of a biologic synthesis process, which encompass modifying a reaction or synthesis pathway within a bioreactor and not merely adjusting the bioreactor conditions. For example, Applicant’s specification at FIG. 35, para. [1626], and para. [1717] discloses a set of variants of a biological synthesis process, each variant including various process parameters, and further discloses process parameters of the variants may include the presence or absence of catalysts, edits to the genotype of the cell line, or the like, which result in improved synthesis of the biological product. Applicant’s specification does not provide support for automatically adjusting controls of a bioreactor to implement an adjusted bioreactor condition comprising a genetically edited cell line, for example, such as providing disclosure of automated bioreactors for genetic editing.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitation above recited in claims 1 and 21, and claims dependent therefrom, to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. THS IS A NEW MATTER REJECTION. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Examiner comment:
It is noted that Applicant’s specification does provide support for implementing the adjusted biologic synthesis process and/or altering an existing or ongoing biologic synthesis process according to the biologic synthesis process in a bioreactor ([1623], FIG. 39, and [0766])
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-5, 11-18, and 20-21 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. This rejection is newly recited by claim amendment and further consideration of the claims.
Claims 1 and 21, and claims dependent therefrom, are indefinite for recitation of “the drawn cell sample” in the last line of the “performing automated parallelized testing” step. There is insufficient antecedent basis for this limitation in the claims, because claims 1 and 21 do not previously recite a drawn cell sample. Therefore it is not clear what sample is being referenced.
Claims 1 and 21, and claims dependent therefrom, are indefinite for recitation of “performing automated parallelized testing…by collecting dynamic multi-omics data via a rapid sampling system, wherein collecting the dynamic multi-omics data comprises: automatically drawing samples…and dispensing a cryogenic quenching agent…” (claim 1) and “A system comprising: one or more computers; a rapid sampling system; a bioreactor; and one or more storage devices…cause the one or more computers to perform operations…the operations comprising:….performing automated parallelized testing of the selected proper subset of the set of variants by collecting dynamic multi-omics data via the rapid sampling system, wherein collecting…comprises: automatically drawing samples… , and dispensing a cryogenic quenching agent” (claim 21). Given the collecting only comprises drawing a sample and quenching the sample using a sampling system, it is unclear if the system is only required to collect the samples (which inherently collects multi-omics data contained in the sample), or if the claim intends to physically require performing assays on the samples (e.g. mass spectrometry). If Applicant intends the latter, then the metes and bounds of what is meant by the rapid sampling is not clear given a sampler generally cannot perform assays. If applicant intends for the rapid sampling system to perform the assays to collect the multi-omics data, then Applicant’s specification does not provide support for this limitation as discussed in the 112(a) above. As such, the metes and bounds of the claimed system are unclear. For purpose of examination, the limitation is interpreted to mean the rapid sampling system draws samples and dispenses a cryogenic quenching agent.
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 1-5, 11-18, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly recited portion is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim 1 being representative) is directed to a method of optimizing a biological synthesis process. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 21 recite the following steps which fall under the mental processes and/or mathematical concepts grouping of abstract ideas:
identifying at least one bottleneck in the biological synthesis process;
evaluating a set of variants of the biological synthesis process, wherein the set of variants comprises a plurality of variants, the evaluating comprising:
processing, for each variant in a set of variants of the biological synthesis process, data characterizing the variant using a machine learning model to generate: (i) a score for the variant characterizing an effect of the variant on reducing the at least one bottleneck, and (ii) a score uncertainty for the score for the variant;
selecting a proper subset of the set of variants for experimental validation based on at least the score uncertainties for the variants;
determining, for each variant in the selected proper subset of the set of variants, a measured performance metric for the variant based on the dynamic multi-omics data; and
retraining the machine learning model on the measured performance metrics for the variants; and
selecting an adjusted biological synthesis process using the retrained machine learning model, wherein the adjusted biological synthesis process includes at least one variant of the set of variants that reduces the at least one bottleneck of the biological synthesis process.
The identified claim limitations falls into the group of abstract ideas of mental processes for the following reasons. In this case, identifying a bottleneck of a biological synthesis process can be practically performed in the mind by analyzing a particular synthesis process, such as a metabolite synthesis process, and identifying a bottleneck of protein expression of the enzyme that creates the metabolite. Furthermore, evaluating a set of variants of the process as claimed can be practically performed in the mind for the following reasons. Processing data characterizing a variant, for each variant of a set of variants, using a machine learning model to generate a score and a score uncertainty can be practically performed in the mind by inputting process data for the variant (e.g. numerical data representing cell growth, metabolite concentrations, etc.) into a linear regression model, and performing weighted addition to calculate a score for the variant and an associated confidence interval. Selecting a proper subset of variants based on the score uncertainties can be practically performed in the mind by selecting the top 2 variants with the lowest uncertainties. Determining, for each variant in the selected proper subsets, a ground truth score for the variant based on collected multi-omics data can be practically performed in the mind by analyzing the collected information to determine a true value for a given synthesis process (e.g. a measured amount of byproduct). Selecting an adjusted biological synthesis process using the retrained machine learning model can be practically performed in the mind for the same reasons discussed above regarding using the machine learning model to generate a score (albeit using a retrained model). That is, other than reciting these limitations are carried out by a computer, nothing in the claims precludes the steps from being practically performed in the mind. See MPEP 2106.04(a)(2) III. Therefore, these limitations recite a mental process. See MPEP 2106.04(a)(2) III.
The limitations of using a machine learning model to generate a score and a score uncertainty and retraining the machine learning model on the ground truth scores recite a mathematical concept. The limitations amount to a textual equivalent to performing mathematical calculations. For example, using a machine learning model to generate a score encompasses applying a linear regression model to perform weighted addition to calculate a score and confidence interval. Similarly, retraining a machine learning model (e.g. a linear regression model), encompasses iteratively performing weighted addition to calculate an output, and then calculating a loss function (e.g. a difference between the model output and known truth value) and adjusting model parameters at each iteration to minimize the loss. Therefore, these limitations recite a mathematical calculation. See MPEP 2106.04(a)(2) I. C.
Dependent claims 2-5, 11-18, and 20 further recite an abstract idea and/or further limit the abstract idea of claim 1. Dependent claims 2-4 further limit the biological synthesis process, set of variants, and bottleneck of claim 1, and thus are part of the abstract idea of claim 1 discussed above. Dependent claim 5 further recites the mental process of performing data comparisons between a simulation or an experimental result. Dependent claim 11 further recites the mental process of evaluating variants according to a ranking order of the set of variants. Dependent claim 12 further recites the mental process of determining the ranking order based on the score. Dependent claim 13 further recites the mental process of comparing a distance between the respective variant and biological synthesis process, comparing two measurements of at least one objective, and comparing two measurements of a feature. Dependent claim 14 further recites the mental process of selecting a first set of variants based on the ranking order, evaluating the first set of variants based on at least one objective, and selecting a second set of variants based on the evaluation of the first set. Dependent claim 15 further recites the mental process of evaluating a simulation of variants or evaluating an experimental result. Dependent claim 16 further limits the variants of the set of second variants selected in claim 14, and thus are part of the mental process of claim 14. Dependent claim 17 further limits the variants of the first set of variants evaluated in claim 14, and thus are part of the mental process of claim 14. Dependent claim 18 further limits the variants of the first and second set of variants evaluated and selected, respectively, in claim 14, and thus are part of the mental process of claim 14. Claim 20 further recites the mental process of generating at least one explanation of an effect of at least one variant of the biological synthesis process on the at least one bottleneck. Therefore, claims 1-5, 11-18, and 20-21 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Claims 2-5, 11-18, and 20 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements of claims 1 and 21 include:
one or more computers;
one or more storage devices (claim 21 only);
a rapid sampling system (claim 21);
a bioreactor (claim 1);
performing automated parallelized testing of the selected proper subset of the set of variants by collecting dynamic multi-omics data via a rapid sampling system, wherein collecting the dynamic multi-omics data comprises: automatically drawing samples from a fermentation system at a sequence of time points, and dispensing a cryogenic quenching agent to perform near-instantaneous quenching of cellular metabolism in the drawn samples to halt post-sampling enzymatic activity and preserve metabolite concentrations within the drawn cell sample (claims 1 and 21);
automatically adjusting controls of a bioreactor to implement the adjusted biologic synthesis process which reduces the at least one bottleneck (claims 1 and 21);
First, the additional elements of one or more computers, one or more storage devices, and transmitting data are generic computer components or functions. The claims only use the one or more computers and/or storage devices as a tool to carry out the abstract idea identified above. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the step of performing automated parallelized testing of the selected proper subset by collection multi-omics information via a rapid sampling system only serves to collect information for use by the abstract idea (i.e. to determine a ground truth score, retrain the model, and select an adjusted biologic synthesis process, which does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g).
Last, the additional element of automatically adjusting controls of a bioreactor to implement the adjusted biologic synthesis process which reduces the at least one bottleneck is not sufficient to integrate the recited judicial exception into a practical application because it amounts to mere instructions to apply the exception. MPEP 2106.05(f) states when determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". In the instant case, the claims recite adjusting controls of a bioreactor to implement an adjusted synthesis process to reduce a bottleneck, but provide no details on how the controls are adjusted and provides no restriction on how the result of implementing the adjusted biologic synthesis process to reduce the bottleneck is accomplished. For example, claim 3 recites the set of variants may include a biologic product activity variant or a biologic product activation variant, and claim 4 recites the bottleneck may be a product activation bottleneck or a process stability bottleneck, such that the adjusted biological synthesis process being implemented includes the biologic product activity variant or biologic product activation variant to result in better product activation or stability. However, the claims provide no details on how controls of a bioreactor are adjusted to implement this adjusted biological synthesis which results in reducing the bottleneck. As a result, the limitation amounts to the words “apply it”, which does not provide integration.
Therefore, the additional elements amount to mere instructions to apply an exception and/or amount to insignificant extra-solution activity, and as such, the claims as a whole do not integration the abstract idea into a practical application. Thus claims 1-5, 11-18, and 20-21 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
Claims 2-5, 11-18, and 20 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements of claims 1 and 21 are outlined above.
First, the additional elements of one or more computers, one or more storage devices, and transmitting data are conventional computer components or functions. The claims only use the one or more computers and/or storage devices as a tool to carry out the abstract idea identified above. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Further regarding claim 1, performing automated parallelized testing of variants of a biological synthesis process by collecting multi-omics data via a rapid sampling system and adjusting controls of a bioreactor to implement an adjusted biologic synthesis process are well, understood, routine, and conventional. This position is supported by Marcellin et al. (Advances in analytical tools for high throughput strain engineering, 2018, Current Opinion in Biotechnology, 54, pg. 33-40; previously cited), Falco et al. (Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas, 2022, RSC Adv., 12, pg. 25528-25548; previously cited), Hemmerich et al. (Microbioreactor Systems for Accelerated Bioprocess Development, 2018, Biotechnol. J., 13, pg. 1-9; previously cited), and Pinu et al. (Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols, 2017, Metabolites, 7(53), pg. 1-20; newly cited).
Marcellin reviews tools for high throughput strain engineering for bioreactors (Abstract; Figure 2), and discloses industrial biofoundries constructs hundreds of strains per week (i.e. bioprocess variants) which are characterized in an automated culture system and one or more sample collect per strain for molecular characterization (pg. 35, col. 2, para. 2; Figure 1, e.g. design, build, test loop). Marcellin further discloses analyzing samples in parallel through liquid chromatography (LC) and mass spectrometry, which collects metabolomics and fluxomics data (i.e multi-omics data), to increase throughput, and further discloses multiple vendors offer this option (pg. 36, col. 1, para. 2 to col. 2, para. 2; Figure 1). Marcellin discloses this is an iterative process in which bioreactor strains and pathways are designed, built, tested, learned and learned from to build pathways for producing a given molecule (i.e. an adjusted biologic synthesis process) (Figure 1; pg. 34, col. 1, para. 2 to col. 2, para. 1).
Falco similarly reviews analytical techniques of metabolic fluxes in metabolic engineering (Abstract), and discloses bioreactors are the preferred method for continuous cultures of microorganism and automated sampling platforms for performing rapid sampling in bioreactors, followed by detection by LC-MS of metabolites (as discussed in Marcellin) (pg. 25533, col. 2, para. 4; pg. 25535, col. 2, para. 1; pg. 25539, col. 2, para. 3). Falco discloses automated samplers integrated with equipment for performing the omics analysis (pg. 25535, col. 2, para. 1). Falco further discloses the collection of samples is a critical step because all metabolic reactions need to be stopped as quickly as possible, and discloses the addition of cold solvents below -20 degrees Celsius (i.e. a cryogenic quenching agent) to cells became the most common method of quenching in the early 90s (pg. 25535, col. 1, para. 3; Fig. 4).
Pinu reviews quenching and extraction protocols (Abstract), and discloses quick sampling of microbial cultures from bioreactors is used by many and semi-automated and fully automated sampling techniques have been developed (pg. 9, para. 2 and 5). Pinu discloses various references that use automated sampling devices, including devices with specifically designed valves or tubes to quench biological samples quickly (pg. 9, para. 6-7), demonstrating the conventionality of rapid sampling systems capable of automatically dispensing quenching reagents.
Last, Hemmerick reviews microbioreactor systems for bioprocess development and optimizing bioprocesses from big strain libraries (Abstract; pg. 2, col. 1, para. 2), and discloses microbioreactors combine higher experimental throughput with extensive bioprocess monitoring and control (Abstract; Table 1, e.g. commercially available systems with temperature control, DO control, etc.), and further discloses in combination with liquid handling systems, software modules connecting input data streams with hardware actors are used to control individual cultures (pg. 5, col. 2, para. 6 to pg. 6, col. 1, para. 1). Overall, the cited references demonstrate the conventionality of using rapid sampling systems to collect multi-omics data (metabolomics and fluxomics) from culture variants, followed by automated quenching cellular metabolism using a cryogenic agent, in parallel in addition to automated microbioreactors equipped with controllers for adjusting bioprocess parameters.
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 10 Sept. 2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the claims provide a specific technical solution by combining machine learning uncertainty selection with rapid physical sampling and real-time hardware feedback, and the closed physical computational loop directly improves the physical bioprocess control (Applicant’s remarks at pg. 9, para. 4).
This argument is not persuasive. MPEP 2106.05(f) states when determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". In the instant case, the claims recite adjusting controls of a bioreactor to implement an adjusted synthesis process to reduce a bottleneck, but provide no details on how the controls are adjusted and provides no restriction on how the result of implementing the adjusted biologic synthesis process to reduce the at least one bottleneck is accomplished. As a result, the limitation amounts to the words “apply it”, which does not provide integration as discussed above in MPEP 2106.05(f).
Applicant remarks the claims do not merely recite the outcome of reducing a bottleneck, and instead the claims recite a specific, step-by-step technological process including processing data characterizing each variant to generate a score, selecting a subset of variants, validating the subset via multi-omics data of a rapid-sampling system, retraining the machine learning model, and using the retrained model to automatically adjust controls of a bioreactor, which restricts how the process optimization is achieved, and thus the claims do not merely recite the idea of a solution or outcome (Applicant’s remarks at pg. 9, para. 5 to pg. 10, para. 1).
This argument is not persuasive. It is acknowledged the claims include several steps for evaluating variants, which culminates in retraining a machine learning model on performed metrics and selecting an adjusted biological synthesis process that reduces the bottleneck of the biology synthesis process using the retrained model. However, the additional element of claims 1 and 21 of “automatically adjusting controls of a bioreactor to implement the adjusted biologic synthesis process” does not provide details of how a solution to a problem is accomplished, and thus amounts to mere instructions to apply the exception. As explained above, the claims do not provide any details regarding how controls of a bioreactor would be adjusted in order to actually implement a biological synthesis process that reduces the bottleneck. Accordingly, claims 1 and 21 attempt to cover any solution of adjusting bioreactor controls to the identified problem of a bottleneck, with no restriction how the bioreactor controls are adjusted and no description of the mechanism of adjusting bioreactor controls accomplishes the result of implementing a biosynthesis process with a reduced bottleneck. As such, the additional element of adjusting the bioreactor controls amounts to mere instructions to apply the exception. See MPEP 2106.05(g).
Applicant remarks the physical components in the claim are not generic tools performing routine tasks, and the claims integrate physical hardware including a “fermentation system”, “a sampling system”, “a dispenser for a cryogenic quenching agent” and “controls of a bioreactor” into an interlinked automated feedback loop, and the physical operation of dispensing a cryogenic quenching agent to perform near-instantaneous quenching of cellular metabolism alters the physical state of cell samples to preserve intracellular metabolite concentrations (Applicant’s remarks at pg. 10, para. 2).
This argument is not persuasive. MPEP 2106.05(b) states when determining whether a machine recited in a claim provides significantly more, the following factors are relevant: I. the particularity or generality of the elements of the machine or apparatus; II. whether the machine or apparatus implements the steps of method; and III. whether its involvement is extra-solution activity or field of use.
It is acknowledged the claims do include physical hardware, including a computer with memory, a rapid sampling system, and a bioreactor. However, the computer is merely used as on object on which the method operates and performs the various limitations reciting an abstract idea. Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. See CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). The rapid sampling system is merely used to collect multi-omics data for use by the abstract idea, and thus its involvement is merely extra-solution activity. The 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. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009. Last, while the bioreactor does broadly apply the abstract idea, MPEP 2106.05(b) further explains a consideration of whether the claims integrate the judicial exception by use of a particular machine is whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim, and furthermore, that Examiners may find it helpful to evaluate other considerations such as the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)) when making a determination on whether an element is a particular machine. In the instant case, the control of the bioreactor amounts to mere instructions to apply the exception for the reasons discussed above.
Applicant remarks the claims are narrowly confined to the specific technical application of optimizing a biological synthesis process through an active-learning loop utilizing score uncertainty filtering, multi-omics data, model retraining, and automated bioreactor control, and therefore, the claims are patent eligible (Applicant’s remarks at pg. 10, para. 3-4).
This argument is not persuasive. Requiring more than mere instructions to apply an exception does not mean that the claim must be narrow in order to be eligible. The courts have identified some broad claims as eligible, and some narrow claims as ineligible see e.g., Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Furthermore, while preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Therefore, simply because the claims may be confined to biological synthesis process is not sufficient to provide integration. The additional elements recited in the claim are not sufficient to integrate the recited judicial exception into a practical application for the reasons already set forth above, and in the outstanding rejection.
Claim Rejections - 35 USC § 103
The rejection of claims 1-5, 11-18, and 20-21 under 35 U.S.C. 103 as being unpatentable over Famili (2009), Costello (2023), and Gamble (2021) in the Office action mailed 10 June 2026 has been withdrawn in view of claim amendments received 10 Sept. 2026. However, a new grounds of rejection is set forth below.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 11-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Famili (2009) in view of Costello (2023), Pinu (2017), and Gamble (2021). This rejection is newly recited and necessitated by claim amendment.
Cited references:
Famili et al., US 2016/0364520 A1; Pub. Date: 2016, effectively filed: 2009 based on priority to U.S. Provisional App. No, 61/244,809 and 61/155,660 (previously cited);
Costello et al. US 2023/0097018 A1 (previously cited); and
Pinu et al. Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols, 2017, Metabolites, 7(53), pg. 1-20; newly cited; and
Gamble et al., Machine Learning Optimization of Photosynthetic Microbe Cultivation and Recombinant Protein Production, 2021, bioRxiv, pg. 1-35 (previously cited)
Regarding claims 1 and 21, Famili discloses a method for developing cellular models of metabolism for producing small molecule chemicals or metabolites (i.e. a biological synthesis processes) ([0006]; [0088]; [0265])., and a system for implementing the method comprising a processor and memory ([0167]-[0168]), wherein the method comprises the following steps:
Famili discloses providing a constraint set specifying a particular set of environmental conditions on flux of mass through a reaction network (i.e. at least one bottleneck) for the biological synthesis process ([0153]; [0172]-[0173]). Famili also discloses an example of ATP energy as a constraint on product formation ([0016]; FIG. 9), and another example in which byproduct formation is a bottleneck for product yield ([0240]).
Famili discloses evaluating different in-silico media formulations or target reactions designed for increasing cell productivity, reducing growth rate, reducing media nutrients, and combinations thereof of the synthesis process (i.e. a set of variants of the biological synthesis process) and rank-ordering the scenarios ([0240]-[0241]; Table 7; [0346]; [0350]). Famili discloses the evaluating comprises the following:
Famili discloses processing, for the different in-silico media formulations (i.e. each variant), data characterizing the variant by performing simulations and using an optimization strategy to minimize an objective function ([0040], e.g. inputs into the mode; [0067]; [0126]) to calculate an objective of the variants ([0223], e.g. objective functions; [0233]-[0235], e.g. objective to minimize byproduct formation, maximize product production used) and a percent increase in product titers for each of the variants over the base case (i.e. a score for the variant characterizing an effect on reducing the at least one bottleneck) ([0236]-[0237]).
Famili discloses selecting one or more most promising synthesis processes based on the rank-order of the calculated performance of the set of variants (i.e. a proper subset of the set of variants based on the score) ([0240]; [0346]; [0426]-[0429], e.g. computational results reviewed and the most promising designs determined for experimental validation).
Famili discloses experimentally validating the identified promising synthesis processing and creating the candidate selection system in a cel line ([0353]; [0360]) and performing biomarker profiling of metabolites ([0413]). Famili discloses the construction of cell lines designs are implemented in parallel ([0432]), demonstrating biomarker data is collected in parallel.
Famili discloses selecting the most promising cell engineering design (i.e. an adjusted biologic synthesis process) based on a ranking (i.e. a performance metric) of the in-silico variant evaluations ([0046]-[0047]; [0240], e.g. in silico media formulations rank ordered by performance). Famili discloses an example in which the selected engineering design is a design that lowers the energy requirement of the network to improve product formation when product formation is limited by energy (i.e. reducing the at least one bottleneck of ATP energy) ([0220]), and selecting a design with the highest productivity increases and byproduct reduction when byproduct formation limits product yield (i.e. reducing the at least one bottleneck) ([0240]).
Famili further discloses implementing the adjusted biologic synthesis process in a bioreactor ([0235]).
Further regarding claims 1 and 21, Famili does not disclose the following limitations:
Regarding claims 1 and 21, Famili does not disclose the processing is performed using a machine learning model, determining, for each variant in the selected proper subset of the set of variants, a measured performance metric for the variant based on the dynamics multi-omics data, retraining the machine learning model on the measured performance metrics, and that the selecting an adjusted synthesis process is using the retrained machine learning model. However, Famili does discloses applying a computational metabolic network model to simulate metabolic networks for process variants ([0049], experimentally validating top ranked simulated variants, and then determining second generation modifications of the variants to feed into future iterations of the method ([0435-[0436]]), demonstrating the method of is iterative. Famili also discloses the network model is created using multi-omics data ([0279]).
Famili further does not disclose performing automated testing of the selected subset of variants by collecting dynamics multi-omics data via a rapid sampling system, wherein collecting the dynamic multi-omics data comprises: automatically drawing samples from a fermentation system at a sequence of time points, and dispensing a cryogenic quenching agent to perform near-instantaneous quenching of cellular metabolism in the drawn cell samples to halt post-sampling enzymatic activity and preserve metabolite concentrations within the drawn cell sample;
However, these limitations were known in the art before the effective filing date of the claimed invention in view of Costello and Pinu.
Regarding claims 1 and 21, Costello discloses a method for simulating a virtual strain of an organism to predict metabolomics data using a machine learning model representing a metabolic pathway dynamics model (as used in Famili) in bioreactors (Abstract; [0007])-[0008]; [0023]; [0082]; [0132]). Costello discloses the machine learning model predicts production of the different virtual strains (i.e. a score for each of the variants) ([0237]) and ranks the scores/production of the strains ([0237]-[0238]). Costello discloses using multiple rounds of design, build, test, and learn (DBTL) cycles ([0123]) by repeating the process to create new strains, which further improve the accuracy of the model in the next round ([0061]). Costello discloses using multiple round of DBTL cycles involves creating a simulated strain having a desired characteristic such as improved yield of product of a metabolic pathway, receiving time-series proteomics and metabolomics data (i.e. dynamic multi-omics data) of the created strain as a time series of 80,000 data points using rapid sampling (i.e. automatically drawing samples from a fermentation system at sequence of time points), and then re-training the machine learning model using time-series proteomics and metabolic data of the created strain, which would also require corresponding measured production for training the prediction (i.e. performance metrics) ([0104]; [0191]; FIG. 8). Costello further discloses the disclosed method takes a data driven approach and does not require deep knowledge of the pathway and final product, which provides a general method applicable to any host, pathway or metabolite ([0122]).
Further regarding claims 1 and 21, while Costello does not disclose collecting the dynamics multi-omics data comprises dispensing a cryogenic quenching agent as claimed, Pinu reviews quenching and extraction protocols (Abstract), and discloses using automated sampling devices to sample cultures from a bioreactor, including sampling devices with specifically designed valves or tubes to quench biological samples quickly (pg. 9, para. 2 and 6-7). Pinu discloses liquid nitrogen (i.e. a cryogenic agent) is used as a quenching agent, and slows down the cell metabolism and turnover of metabolites to provide extra time for metabolite extraction (pg. 5, para. 5).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Famili to have utilized network simulations produced by a machine learning model representing a metabolic network model, collected dynamic-multi-omics, created a performance metric for the variant using the multi-omics data, retrained the machine learning model on the performance metrics, and used the retrained machine learning model to select an adjusted process, as shown by Costello above, rather than using a computational metabolic network model for the iterative simulations as performed in Famili. One of ordinary skill in the art would have been motivated to combine the methods of Famili and Costello, in order to facilitate the simulations of metabolic networks without requiring deep knowledge of the pathways and final products, as shown by Costello ([0122]). This modification would have had a reasonable expectation of success because each of Famili and Costello disclose an iterative process of designing pathways/strains, simulating a metabolic network, selecting high performing variants, experimentally validating the variant, and repeating the process to achieve improved process performance, and furthermore, the metabolic network model of Famili used for the simulations could be represented as a machine learning model according to the method of Costello.
It would have been further prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the method of Famili in view of Costello to have dispensed a cryogenic agent to quench cellular metabolism in sampled samples, as shown by Pinu above. One of ordinary skill in the art would have been motivated to combine the methods of Famili in view of Costello with Pinu, in order to provide extra time for metabolite extraction as shown by Pinu (pg. 5, para. 5). This modification would have had a reasonable expectation of success given Costello collects multi-omics data using rapid sampling, and thus the rapid sampling system with quenching of Pinu are applicable to Costello.
Further regarding claims 1 and 21¸ Famili in view of Costello do not disclose that the subset of variants is selected based on an uncertainty score generated by the machine learning model, and further do not disclose automatically adjusting controls of a bioreactor to implement the adjusted biologic synthesis process.
However, Gamble discloses a method for optimizing microbe cultivation using machine learning optimization (Abstract), which involves applying a Bayesian model to model the relationship between bioreactor parameter configurations and performance (pg. 6, para. 2-3). Gamble discloses the machine learning model calculates an interbatch performance variance (i.e. an uncertainty score) by including multiple replicates of a given bioreactor condition, and determines the performance for a given bioreactor based on this inter-batch variance (pg. 6, para. 3; pg. 25, see equations #3-4). Gamble discloses taking the bioreactor configuration with the highest expected performance for iterating (pg. 25, para. 5). Gamble further discloses the settings for each bioreactor parameter were guided by the output of the machine learning model (pg. 23, para. 3-4).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Famili in view of Costello, to have selected the subset of variants using an uncertainty score generated by the machine learning model as shown by Gamble above. One of ordinary skill in the art would have been motivated to combine the methods of Famili in view of Costello with Gamble in order to account for inter- and intra- batch variance of the bioreactor runs in selecting a best performing bioreactor condition, as shown by Gamble (pg. 25, para. 3). This modification would have had a reasonable expectation of success given Famili in view of Costello also determine a performance of bioreactor simulations using a machine learning model, such that the inter and intra batch variance of Gamble is applicable to the method of Famili in view of Costello.
It would have been further prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Famili in view of Costello to have controlled the settings for each bioreactor parameter according to the output of machine learning model (i.e. the best performing/adjusted synthesis process), as shown by Gamble (pg. 23, para. 3-4). One of ordinary skill in the art would have been motivated to combine the methods of Famili in view of Costello with Gamble in order to implement a bioreactor with improved product yield, as shown by Gamble (pg. 4, para. 1 and 3). This modification would have had a reasonable expectation of success given Famili discloses the method is used for improving bioreactor cultivations and for media optimization and culture conditions ([0059]; [0086]); [0329], and Costello also discloses the machine learning model can be used to guide media compositions for organisms in bioreactors ([0082]; [0131]. Therefore the method of adjusting bioreactor parameters of Gamble is applicable to the methods of Famili in view of Costello.
Last regarding the adjusting controls being performed “automatically”, the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.). See MPEP 2144.04 III.
Regarding claim 2¸ Famili discloses the biological synthesis process for the production of metabolites (i.e. a metabolite synthesis process) ([0265]; [0307]).
Regarding claim 3, Famili discloses the in-silico variants of the synthesis process include altering the media of the synthesis process by adding nutrients (biological addition variant) and removing nutrients (i.e. biological product elimination variant) ([0240]-[0241]), and a variants with different energy parameters such as ATP concentrations (i.e. biological production concentration variant) (FIG. 9; [0016]; [0220]). Famili also discloses the synthesis processes may include different target reactions (i.e. biological product transformation variant) (FIG. 22; [0029]). Costello similarly discloses the machine learning methods can be used
Regarding claim 4, Famili discloses the bottlenecks include ATP concentration (i.e. a product expression bottleneck) ([0016]; FIG. 9), amino acid uptake (i.e. a protein expression level bottleneck, given protein expression requires amino acids) ([0221]), and metabolic inefficiency of cells where they take up more nutrients then needed and create waste that limits product formation (i.e. a metabolite production rate bottleneck) ([0224]).
Regarding claim 5, Famili discloses comparing the in-silico simulations of the biological synthesis process, including the variants, with each other to identify a process with the highest productivity increases ([0240]; Table 7).
Regarding claim 11, Famili discloses evaluating variants of the synthesis process by rank-ordering the variants based on a productivity increases ([0240]).
Regarding claim 12¸ Famili discloses evaluating the variants comprises providing the “base case” of the synthesis process to benchmark improvements in productivity relative to the variants ([0232]) and calculating a percent increase in product titers for each of the variants over the base case (i.e. a score based on comparing a respective variant and the biological synthesis process) ([0236]-[0237]). Famili further discloses rank-ordering the variants based on productivity increase (i.e. the score) ([0237]). Famili in view of Costello disclose the productivity is a score generated by the machine learning model ([0238];
Regarding claim 13, Famili discloses the comparing comprises calculating a percent increase (i.e. a distance) in product titers for each of the variants over the base case e) ([0236]-[0237]). Alternatively, Famili also discloses comparing an objective of the variants to the base case (i.e. a measurement of at least one objective…)([0223], e.g. objective functions; [0233]-[0235], e.g. objective to minimize byproduct formation, maximize product production used).
Regarding claim 14, Famili discloses selecting one or more most promising synthesis processes (i.e. a first set of candidate variants) based on the rank-order of the set of variants ([0240]; [0346]; [0426]-[0429], e.g. computational results reviewed and the most promising designs determined for experimental validation).
Famili discloses testing the most promising process experimentally using mammalian cell culture in order to validate the in-silico synthesis process and demonstrate the technical feasibility of the superior synthesis process (i.e. evaluating based on an objective of respective variants of the first set) ([0046];[0240]; [0346], e.g. candidates rank-ordered and top candidate experimentally implemented).
Famili further discloses based on agreements between model predictions and experimental results, second generation modifications can be determined, except instead of comparisons of minimum byproduct forming phenotype(s) with a baseline synthesis process, it will be with the newly generated synthesis process, and second generation modifications will be suggested for all of the generated synthesis processes (i.e. a second set of variants selected based on experimental evaluation of the first set of variants) ([0435]-[0436]).
Regarding claim 15, Famili discloses evaluating the first set of candidate variants includes experimentally validating the first set of variants ([0240]; [0435]).
Regarding claim 16, Famili discloses the second set of variants applies a second generation modification to the first set of variants (i.e. at least one further variant of the first set of candidate variants) ([0435]-[0436]).
Regarding claim 17¸ Famili discloses the first set of variants include different cell-lines intended to have lower byproduct formation than the parent, with each cell-line having a different target gene knockout most likely to eliminate byproduct formation (i.e. at least two alternatives/variations of a gene/feature of the biological synthesis process) ([0426]-[0427]).
Regarding claim 18, Famili discloses the first set of variants include a single knockout gene (i.e. a single variation of a feature of the biological synthesis process) ([0426]-[0427], e.g. one gene knockout required). Famili further discloses making a second generation modification to the first set of variants ([0436]), thus producing a second set of variants with at least two different features of the biological synthesis process (e.g. the gene knockout of the first variant set + one other modification).
Regarding claim 20¸ Famili discloses explaining that byproduct formation was lowered in a variant compared with the “base case” values, even though the formulation was not designed specifically for reduced byproduct formation ([0237]).
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 10 Sept. 2026 regarding 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant remarks that Famili, Costello, or Gamble do not teach “automatically drawing samples from a fermentation system at a sequence of time points, and dispensing a cryogenic quenching agent to perform near-instantaneous quenching (Applicant’s remarks at pg. 12, para. 6 to pg. 11, para. 2).
This argument is not persuasive because it does not take into account the newly cited reference Pinu, which discloses the amended limitation. Pinu reviews quenching and extraction protocols (Abstract), and discloses using automated sampling devices to sample cultures from a bioreactor, including sampling devices with specifically designed valves or tubes to quench biological samples quickly (pg. 9, para. 6-7). Pinu discloses liquid nitrogen (i.e. a cryogenic agent) is used as a quenching agent, and slows down the cell metabolism and turnover of metabolites to provide extra time for metabolite extraction (pg. 5, para. 5).
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN L MINCHELLA whose telephone number is (571)272-6485. The examiner can normally be reached 7:00 - 4:00 M-Th.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
