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 .
Applicant’s initial filing of 4 November 2021, has been entered and fully considered.
Information Disclosure Statement
The information disclosure statements, filed 12/28/2022, 03/29/2023, 05/17/2023, 07/25/2023 and 09/25/2023, have been fully considered and have been entered. The IDSs comply with the provisions of 37 CFR 1.98(a)(4). Consequently, five corresponding 1449 forms are attached.
Priority
As detailed on the 6/16/2022 filing receipt, this application claims benefit over the provisional application # 62/844,975. Therefore this application has a priority as early as 05/08/2019.
Status of claims
Claims 1-42 are pending and are examined on the merits.
Claim Interpretation
The term “organism” recited in claims 1, 6, 15, 20, 29 and 34 is interpreted according to the paragraph [0069] in the disclosure:
As used herein the terms “organism” “microorganism” or “microbe” should be taken broadly. These terms are used interchangeably and include, but are not limited to, the two prokaryotic domains, Bacteria and Archaea, as well as certain eukaryotic fungi and protists.
Hence, a cultured cell, anticipate the “organism” recited in the claims.
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 6, 20 and 34 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.
Claim 6, 20, and 34 define that "determining first-scale screening conditions is further based at least in part upon environmental conditions determined from fermentation modeling of the organism at a third scale larger than the second scale." However, when the preceding claims to which they are cited define "the third scale is the same as the second scale" (claims 3, 17, and 31), both the third scale being larger than the second scale and the third scale being the same as the second scale are defined, thereby resulting in an unclear scope of protection of claims 6, 20 and 34.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 32 and 34 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 32 recites “third-scale” and claim 34 recites “third scale”, neither have an antecedent in claim 29. “Third scale” does have an antecedent, but in claim 30. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-42 are rejected under 35 USC 101 because the claimed inventions are directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture, or Composition of Matter
Claims 1-14 and 18 are directed to a 101 process, here a " computer-implemented method," for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a larger, second scale, with functional steps like “determining”, and “designing”.
Claims 15-17 and 19-28 are directed to a 101 machine or manufacturer, here a "system," for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a larger, second scale, with structural components like “one or more memories storing instructions”, and “one or more processors, operatively coupled to the one or more memories”.
Claims 29-42 are directed to a 101 machine or manufacturer, here "one or more non-transitory computer-readable media," for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a larger, second scale, with structural components like “one or more non-transitory computer-readable media”.
Step 2A Prong One: Identification of Judicially Recognized Exceptions
Claim 1 recites:
a. Determining first-scale screening conditions based at least in part upon contribution of second-scale conditions to performance parameters of first strains of an organism at the second scale, wherein the first-scale screening conditions include one or more proxies for second-scale conditions that cannot be replicated at first scale;
----This step recites a decision making process (determining first-scale screening conditions) based on data observation (contribution of second-scale conditions to performance parameters of first strains of an organism at the second scale), which equates to an abstract idea of mental processes.
b. Determining first-scale screening parameters based at least in part upon computer modeling of the metabolism of the organism at the second scale; and
----This step recites a mathematical operation (computer modeling) to generate outputs (first-scale screening parameters), which equates to an abstract idea of mathematical concepts.
c. Designing experiments for experimentally screening second strains of the organism under the first-scale screening conditions based at least in part upon the first-scale screening parameters.
----This step recites a judging process (determining experiments for experimentally screening second strains of the organism) based on data observation (based at least in part upon the first-scale screening parameters), which equates to an abstract idea of mental processes.
Claims 15 and 29 recite the same process steps as claim 1 and are therefore also abstract ideas.
Step 2A Prong Two: Consideration of a Practical Application?
The claimed method steps do not include any additional elements that integrate the recited judicial exceptions (abstract ideas and natural correlation) into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The preamble of claims 1, 15 and 29 recite a method of experimental design that generates first-scale performance data and the computing environment the design is executed. However, the process steps of the method do not include physical steps outside the computing environment. The claimed method steps are based on information having being gathered and then generate additional data. The claimed process is therefore drawn to abstract ideas and is a judicial exception.
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. A computer system comprising a processor and memory (claim 15).
2. Non-transitory computer readable storage medium (claim 29).
These additional elements are associated with a generic computing environment. Hence are applying the abstract idea using computers (MPEP §2106.05(f)). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer systema and storage medium are a recitation of generic computer structures that serve to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
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.
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 1-42 are rejected under 35 U.S.C. 103 as being unpatentable over Toeroek et al. ("Fed-batch like cultivation in a micro-bioreactor: screening conditions relevant for Escherichia coli based production processes." SpringerPlus 4.1 (2015): 490. Cited on the 5/17/2023 IDS), in view of Kensy et al. ("Scale-up from microtiter plate to laboratory fermenter: evaluation by online monitoring techniques of growth and protein expression in Escherichia coli and Hansenula polymorpha fermentations." Microbial Cell Factories 8.1 (2009): 68. Cited on the 12/28/2022 IDS)
Claim 1 claims a computer-implemented method of designing an experiment of organisms at a first scale to generate first-scale performance data for predicting performance of said organisms at a second, larger scale,
Toeroek discloses a method of screening conditions for E. coli based on the production process, and specifically discloses the following (page 1, Title and section “Abstract”). Particularly:
Toeroek provides “Recombinant protein production processes in Escherichia coli are usually operated in fed-batch mode; therefore, the elaboration of a fed-batch cultivation protocol in microtiter plates that allows for screening under production like conditions is particularly appealing” and “A highly reproducible fed-batch like microtiter plate cultivation protocol for E. coli in a micro-bioreactor system with advanced online monitoring capabilities was developed” (page 1, section “Abstract”), which teaches (b) determining a first scale screening parameter based at least in part on computer modeling of the metabolism of the organism at the second scale.
Toeroek provides “accurate process design allowed for cultivation up to cell densities of 10 g biomass l−1 without any limitations in oxygen supply [dissolved oxygen (DO) level above 30 %]. In the micro-bioreactor system (Bio‑Lector) online monitoring of cell growth, DO and pH was performed. Furthermore, the influence of the cultivation temperature, the applicability for different host strains as well as the transferability of results to lab-scale bioreactor cultivations was evaluated” (page 1, section “Abstract”), which teaches (c) an experiment designed to experimentally screen a second strain of the organism under the first scale screening conditions based at least in part on the first scale screening parameters.
Toeroek does not teach (a) determining first-scale screening conditions wherein the first-scale screening conditions include one or more proxies for second-scale conditions.
However, Kensy discloses an online evaluation monitoring method from microplates to laboratory fermentation scale-up.
Kensy provides “bioLector mainly consists of an optical measurement unit, an optic Y- fiber bundle, a X-Y mover and an orbital shaker” (page 3, col 2, 1st para.), “biomass concentrations were measured by 620 nm excited without an emission filter” (page 3, col 2, 1st para.) and the side-by-side comparison of the first-scale experimental set up vs the second-scale experimental set up (page 4, Fig. 1). Kensy therefore teaches (a) determining first scale screening conditions based at least in part on the contribution of second scale conditions to the performance parameter of the first strain of organism at said second scale, wherein the first scale screening conditions include one or more proxies to a second scale condition that cannot be replicated at a first scale.
“Statistical model” is very broad here in claim 2. The disclosure provides no further description to “statistical model”. hence any mathematical calculation anticipate “statistical model” in claim 2. Regarding claim 2, Toeroek provides that Escherichia coli recombinant protein production process typically employs a fed-batch cultivation mode, the microplate-based flow-on cultivation approach may make screening for similar production conditions particularly attractive (page 1, section “Abstract”), which teaches generating a first scale statistical model of a first scale performance of the second strain, and using the first scale statistical model to predict performance of the second strain at a third scale.
Regarding claim 3, Toeroek provides “Recombinant protein production processes in Escherichia coli are usually operated in fed-batch mode; therefore, the elaboration of a fed-batch cultivation protocol in microtiter plates that allows for screening under production like conditions is particularly appealing (page 1, section “Abstract”), which teaches the third scale is the same as the second scale.
Regarding claim 4, Toeroek provides that on-line monitoring of growth of cells in a microbial reactor system, monitoring of DO and pH, and, in addition, evaluation of culture temperature, conversion rate of different strains with respect to laboratory scale bioreactors; growth of two strains to the same biomass concentration can be compared (page 1, section “Abstract”), which teaches designing an experiment includes screening the second strain based at least in part on the predicted third-scale performance of the second strain.
Regarding claim 5, Toeroek provides “A highly reproducible fed-batch like microtiter plate cultivation protocol for E. coli in a micro-bioreactor system with advanced online monitoring capabilities was developed. A synthetic enzymatic glucose release medium was employed to provide carbon limited growth conditions without external substrate feed and the required buffer capacity to keep the pH value within 7 ± 1. Accurate process design allowed for cultivation up to cell densities of 10 g biomass l−1 without any limitations in oxygen supply [dissolved oxygen (DO) level above 30 %]. In the micro-bioreactor system (Bio‑Lector) online monitoring of cell growth, DO and pH was performed. Furthermore, the influence of the cultivation temperature, the applicability for different host strains as well as the transferability of results to lab-scale bioreactor cultivations was evaluated” ” (page 1, section “Abstract/Results”), which teaches determining first scale screening conditions is further based at least in part on environmental condition determined from fermentation modeling.
Regarding claim 6, Toeroek provides “Recombinant protein production processes in Escherichia coli are usually operated in fed-batch mode; therefore, the elaboration of a fed-batch cultivation protocol in microtiter plates that allows for screening under production like conditions is particularly appealing” (page 1, section “Abstract/Objectives”), which teaches determining first scale screening conditions is further based, at least in part, on environmental conditions determined from fermentation modeling of the organism at a third scale greater than the second scale.
Regarding claims 7 and 8, Kensy provides Fig. 1 (Fig. 1, page 4), wherein a BioLector microtiter plate (Working volume: 200 µl) fermentation vs a stirred tank fermenter (Working volume: 1.4L) is compared. Kensy therefore teaches the first scale was on plate scale and the second scale was on bench tank scale.
Kensy also anticipate a plate comprising wells, where each well has a volume in the range of 50 to 200 microliters, and the second scale is at the scale of a bench tank with a volume in the range of 200 ml to 10 liters.
Regarding claim 9, Toeroek provides “Recombinant protein production processes in Escherichia coli are usually operated in fed-batch mode; therefore, the elaboration of a fed-batch cultivation protocol in microtiter plates that allows for screening under production like conditions is particularly appealing” (page 1, section “Abstract”), and that the screening is performed according to conditions, and the conditions include the concentration of biomass (page 1, section “Abstract”). Toeroek therefore enables a person skilled in the art to set specific concentration thresholds for screening according to actual requirements
Regarding claim 10, Toeroek provides that E. coli recombinant protein production process typically employs a fed-batch culture mode, microplate-based fed-batch culture mode can make screening of similar production conditions especially attractive; the growth of both strains to the same biomass concentration can be compared (page 1, section “Abstract”). Therefore, Toeroek discloses that screening is performed according to similar production conditions and conditions include concentration of biomass. A person skilled in the art is thus able to set specific concentration thresholds for screening with reference to the conditions of actual production.
Regarding claim 11, Toeroek provides that that E. coli recombinant protein production process typically employs a fed-batch culture mode, microplate-based fed-batch culture mode can make screening of similar production conditions especially attractive; the growth of both strains to the same biomass concentration can be compared (page 1, section “Abstract”). Kensy provides that all online measurement data show deviations below 10% for both culture systems (page 1, section Abstract). Therefore, Toeroek and Kensy disclose technical methods conventional to those skilled in the art in determining optimal screening conditions taking into account the associated row screening of two culture modalities.
Regarding claim 12, Toeroek provides that E. coli recombinant protein production process typically employs a fed-batch culture mode, microplate-based fed-batch culture mode can make screening of similar production conditions especially attractive; the growth of both strains to the same biomass concentration can be compared. (page 1, section “Abstract”). Kensy provides that Microplate scale is 200 microliters, Stirred Tank Fermentation scale (1.4 L), all online measurement data show less than 10% deviation for both culture systems (page 1, section Abstract). Hence, Toeroek and Kensy disclose the technical methods conventional to those skilled in the art in determining optimal screening conditions for screening taking into account the association and bias of the two culture modalities.
Regarding claim 13, Toeroek provides that E. coli recombinant protein production processes often employ a fed-in cultivation mode, microplate-based fed-in
cultivation approaches can make screening of similar production conditions especially attractive; the growth of both strains to the same biomass concentration can be compared (page 1, section “Abstract”). Therefore, Toeroek teaches controlling the execution of experiments to screen the second strain at the first scale using the first scale screening conditions and the first scale screening parameters.
Regarding claim 14, Toeroek provides that E. coli recombinant protein production processes often employ a fed-in cultivation mode, microplate-based fed-in
cultivation approaches can make screening of similar production conditions especially attractive; growth of both strains to the same biomass concentration can be compared (page 1, section “Abstract”). Therefore, Toeroek suggests the first strain is identical to the second strain.
Claims 15-28 are the “computer system” version and claims 29-42 are the “computer disk” version of the claims 1-14 method. Toeroek discloses the “BioLecter software (BioLection 2.2.0.3)” (page 3, 2nd para.) as part of “the micro-bioreactor system (Bio‑Lector)” with online monitoring functionality (page 1, section “Abstract”). Kensy provides “by taking advantage of this simple and efficient microbioreactor array, a new online monitoring technique for biomass and fluorescence, called BioLector, has been recently developed. The combination of high-throughput and high information content makes the BioLector a very powerful tool in bioprocess development” (page 1, Section “Abstract/Background”).
Toeroek and Kensy thus suggest a computer system with software disk in the BioLector system. Therefore, the art applied to claims 1-14 also teaches claims 15-28 and 29-42.
It would have been prima facie obvious to combine Toeroek’s Fed-batch like cultivation in a micro-bioreactor for modeling screening conditions relevant for production processes and Kensy’s Scaling up modeling from microtiter plate to laboratory fermenter, because Kensy proven the scalability of MTPs (microtiter plates) to STFs (stirred tank fermenters) could make them ideally suited for scale-up and scale-down modeling (Kensy: page 14, col 1, 1st para). One would reasonably expect success for the combination as both Toeroek and Kensy are about experimental set up and they both use the BioLector system for data modeling.
Claims 1, 15, 29 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Famili et al. (“Mammalian cell line models and related methods”, US20160364520A1, published on 2016-12-15, cited on the 12/28/2022 IDS), in view of Goletz et al. (“Small scale cultivation method for suspension cells”, US20180135091A1, published on 2018-05-17. Cited on the 12/28/2022 IDS).
Regarding claim 1, Famili provides “the invention provides models and methods useful for optimizing cell lines. The invention provides methods and computer readable medium or media containing such methods. Such a computer readable medium or media can comprise commands for carrying out a method of the invention” [0007];
“Process design as used herein generally refers to the design and engineering of scale up from small to large scale processes, in particular as they are used in an industrial and commercial scale for culture of cells” [0086], and
“the system comprising: one or more memories storing instructions; and one or more processors. operatively coupled to the one or more memories, for executing the instructions Those of skill in the art will recognize that instructions for the software implementing a method and model of the present disclosure can be written in any known computer language, ... and compiled using any compatible compiler; and that the software can run from instructions stored in a memory or computer-readable medium on a computing system. A computing system can be a single computer executing the instructions or a plurality of computers in a distributed computing network executing parts of the instructions sequentially or in parallel” [0166-0167].
Famili therefore teaches a computer-implemented method of designing experiments for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a larger, second scale.
Famili provides “Process design as used herein generally refers to the design and engineering of scale up from small to large scale processes, in particular as they are used in an industrial and commercial scale for culture of cells .... The methods of the invention can also be used to identify the distinct and significant difference between, for example, ... (b) different bioreactor and/or shake flask culture conditions performed with the same cells. media, and cell culture parameters (to reduce batch-to-batch variability” [0086], and
“the knowledge obtained by analyzing the data in the context of the reconstructed model is used to identify design parameters that should be monitored or controlled in cell culture to prevent variability in cell culture condition that would result in scale up variability” [0087].
Famili therefore teaches causing the system to: a. determine first-scale screening conditions based at least in part upon contribution of second-scale conditions to performance parameters of first strains of an organism at the second scale.
Famili provides “In one embodiment, the present invention provides cell line metabolic models” [0043];
“Computational strategies listed above require large sets of experimental data for algorithmic training and in general do not provide a complete solution for media development and optimization in mammalian cell culture. An optimized medium using a laboratory scale cell culture is often not robust to scale-up changes at the manufacturing stage, and requires re-optimization .... Computational metabolic modeling can serve as a design and diagnostic tool to: ... interpret process changes, for example, scale-up” [0068-0069];
“Most large-scale processes are operated using animal serum free media .... Added benefits in using serum free media include increased consistency in growth and productivity” [0065], and
“Most large-scale processes are operated using animal serum free media .... Added benefits in using serum free media include increased consistency in growth and productivity” [0065].
Famili therefore teaches b. determining first-scale screening parameters based at least in part upon computer modeling of the metabolism of the organism at the second scale.
Famili provides “In yet another embodiment, the invention provides cell line engineering and novel selection system design .... This approach can be implemented in three stages: (1) identify essential metabolic reactions that are candidate targets for designing novel and superior selection systems using a reconstructed metabolic model of a cell line such as hybridoma, NS0, or CHO, ... (2) experimentally implement the top candidate selection system in a cell line using experimental techniques such as by first creating an auxotrophic clone, ... (3) evaluate the development and implementation of a model-based selection system in CHO cells by comparing experimentally generated cell culture data with those calculated by the reconstructed model” [0046]; and
“The final clones will be grown in identical conditions as the baseline CHO-S cell line so the change in genetic content is the only variable changing in the process and a straightforward evaluation of byproduct elimination can be made” [0434],
Famili therefore teaches c. designing experiments for experimentally screening second strains of the organism under the first-scale screening conditions based at least in part upon the first-scale screening parameters.
Famili does not disclose wherein the first-scale screening conditions include one or more proxies for second-scale conditions that cannot be replicated at first scale of step (a). However, Goletz discloses small scale cultivation method for suspension cells and Goletz teaches wherein the first-scale screening conditions include one or more proxies for second-scale conditions that cannot be replicated at first scale.
Goletz provides “The present invention pertains to small scale cell culture systems which simulate large scale perfusion cultures of suspension cells” [0001];
“As demonstrated by the present invention, perfusion cultures of eukaryotic cells in suspension can efficiently be simulated in small scale using a stirred cell culture wherein after certain time intervals a part of the medium is exchanged for fresh medium. Retention of the cells in the culture is achieved by sedimentation of the cells prior to removal of the culture medium” [0008]; and
“The small scale cell culture does not simulate the culture volume of the large scale perfusion culture. In specific embodiments, the small cell culture does not simulate method of separating the cells from the culture medium to be removed used in the large scale perfusion culture” [0057],
Goletz therefore teaches screening conditions include a proxy for second, larger scale condition that cannot be replicated in a smaller scale simulation such as using sedimentation to replace culture media in a smaller scale simulation
Claim 15 and claim 29 are the “system” version and the computer disk version for the method in claim 1. Since Famili disclosed “the invention provides methods and computer readable medium or media containing such methods. Such a computer readable medium or media can comprise commands for carrying out a method of the invention. The methods of the invention can be utilized to model improved characteristics of a cell line, for example, improved product production, improved growth, improved culture characteristics, and the like” (Famili: Abstract), the art applied to claim 1 also teaches claims 15 and 29.
Regarding Claim 32, Famili further discloses wherein designing experiments includes screening the second strains based at least in part upon the predicted third-scale performance of the second strains (In addition to media optimization and development, such a computational modeling approach can be used to design cell culture processes, that is, process design, and to engineer cell lines, that is, cell line engineering, to improve a desired characteristic including, but not limited to, biomass production, viable cell density, product yields, and/or product titers to improve the overall productivity of the cell culture, Para. [0048]; Process design as used herein generally refers to the design and engineering of scale up from small to large scale processes, in particular as they are used in an industrial and commercial scale for culture of cells, Para. [0086]; To validate the accuracy of this objective function, cell culture data was simulated in the model for a parental, that is, a non-producing cell line, and an lgG producing NS0 clone. The simulation results were benchmarked by comparing byproduct secretion rates to experimental measurements (Table 5). The results show that the metabolic model was able to correctly capture NS0 cell line growth and metabolite uptake and secretion rates in a batch bioreactor for both the parental and lgG producing NS0 clone, Para. (0225); Finally, based on the initial decrease in byproduct formation, agreements with predictions and effectiveness of the computational approaches, second generation modifications to the generated cell lines will be determined .... These new designs will also be computed with the knowledge gained from algorithm performance in the predictability for the initial designs. Second generation modifications will be suggested for all of the generated cell lines, regardless of performance, and will feed directly into future iterations, Para. [0436]).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Famili with the teaching of Goletz for the purpose of using proxy conditions for culture media removal as a method of screening to determine small-scale conditions that can be applied to large-scale conditions. One would reasonably expect success as both Famili and Goletz are about cell culture scaling, modeling and prediction, and Goletz demonstrated his method can be used for simulating large scale cell cultures and for screening culturing conditions and cell clones for use in large scale cell cultures (Goletz: Abstract).
Claims 2-4, 16-18 and 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Famili and Goletz as applied to claims 1, 15 and 29 above, and further in view of Serber et al. (“Microbial strain improvement by a htp genomic engineering platform”, US20170159045A1, Published 06/08/2017. Cited on the 12/28/2022 IDS).
Regarding Claim 2, Famili in view of Goletz fails to explicitly disclose further comprising generating a first-scale statistical model of first-scale performance of the second strains, and using the first-scale statistical model to predict performance of the second strains at a third scale.
Serber teaches using statistical modeling of a candidate strain in a first scale to predict performance of a candidate strain in a second scale (FIG. 34 illustrates an example of a computer system 800 that may be used to execute program code stored in a non-transitory computer readable medium (e.g., memory) in accordance with embodiments of the disclosure, Para. [0525]; In some embodiments, the present disclosure teaches iteratively improving the design of candidate microbial strains by (a) accessing a predictive model populated with a training set , Para. [0041]; Candidate strains are screened using bench scale fermentation reactors (e.g., reactors disclosed in Table 5 of the present disclosure) for relevant strain performance characteristics such as productivity or yield, Para. [0396}; During the analysis phase, the modified strain cultures are evaluated to determine their performance, i.e., their expression of desired phenotypic properties, including the ability to be produced at industrial scale, Para. [0469}; Candidate strains were generated. This example includes a serial build constraint associated with the introduction of new genetic changes to a parent strain, Para. [0491]; In supervised machine learning such as that of the linear regression example above, the machine (e.g., a computing device) learns, for example, by identifying patterns, categories, statistical relationships, or other attributes, exhibited by training data. The result of the learning is then used to predict whether new data will exhibit the same patterns, categories, statistical relationships or other attributes, Para. [0483]; Each newly created strain comprising a single SNP was tested for lysine yield in small scale cultures designed to assess product titer performance. Small scale cultures were conducted using media from industrial scale cultures, Para. [0604]; As predicted by the small scale high throughput cultures, larger tank cultures for strains comprising the combined zwf promoter swap and SNP 121 exhibited significant increases in yield and productivity over the base reference strain, Para. [0610]).
Regarding Claim 3, Famili in view of Goletz provides “The final clones will be grown in identical conditions as the baseline CHO-S cell line so the change in genetic content is the only variable changing in the process and a straightforward evaluation of byproduct elimination can be made” ([434]), which teaches the third scale is the same as the second scale.
Regarding Claim 4, Famili in view of Goletz discloses the method of claim 2. Famili in view of Goletz further discloses wherein designing experiments includes screening the second strains based at least in part upon the predicted third-scale performance of the second strains (In addition to media optimization and development, such a computational modeling approach can be used to design cell culture processes, that is, process design, and to engineer cell lines, that is, cell line engineering, to improve a desired characteristic including, but not limited to, biomass production, viable cell density, product yields, and/or product titers to improve the overall productivity of the cell culture, Para. [0048]; Process design as used herein generally refers to the design and engineering of scale up from small to large scale processes, in particular as they are used in an industrial and commercial scale for culture of cells, Para. (0086]; To validate the accuracy of this objective function, cell culture data was simulated in the model for a parental, that is, a non-producing cell line, and an lgG producing NS0 clone. The simulation results were benchmarked by comparing byproduct secretion rates to experimental measurements (Table 5). The results show that the metabolic model was able to correctly capture NS0 cell line growth and metabolite uptake and secretion rates in a batch bioreactor for both the parental and lgG producing NS0 clone, Para. (0225); Finally, based on the initial decrease in byproduct formation, agreements with predictions and effectiveness of the computational approaches, second generation modifications to the generated cell lines will be determined .... These new designs will also be computed with the knowledge gained from algorithm performance in the predictability for the initial designs. Second generation modifications will be suggested for all of the generated cell lines, regardless of performance, and will feed directly into future iterations, Para. [0436]).
Regarding Claim 16, Famili in view of Goletz discloses the system of claim 15. Famili in view of Goletz fails to explicitly disclose wherein the one or more memories store further instructions that, when executed, cause the system to generate a first-scale statistical model of first-scale performance of the second strains, and use the first-scale statistical model to predict performance of the second strains at a third scale. Serber teaches memories storing instructions for executing methods to perform statistical modeling of a candidate strain in a first scale to predict performance of a candidate strain in a second scale (In some embodiments, the present disclosure teaches iteratively improving the design of candidate microbial strains by (a) accessing a predictive model populated with a training set , Para. [0041]; Candidate strains are screened using bench scale fermentation reactors (e.g., reactors disclosed in Table 5 of the present disclosure) for relevant strain performance characteristics such as productivity or yield, Para. [0396]; During the analysis phase, the modified strain cultures are evaluated to determine their performance, i.e., their expression of desired phenotypic properties, including the ability to be produced at industrial scale, Para. [0469]; Candidate strains were generated. This example includes a serial build constraint associated with the introduction of new genetic changes to a parent strain, Para. [0491]; In supervised machine learning such as that of the linear regression example above, the machine (e.g., a computing device) learns, for example, by identifying patterns, categories, statistical relationships, or other attributes, exhibited by training data. The result of the learning is then used to predict whether new data will exhibit the same patterns, categories, statistical relationships or other attributes, Para. [0483]; Each newly created strain comprising a single SNP was tested for lysine yield in small scale cultures designed to assess product titer performance. Small scale cultures were conducted using media from industrial scale cultures, Para. (0604]; As predicted by the small scale high throughput cultures, larger tank cultures for strains comprising the combined zwf promoter swap and SNP 121 exhibited significant increases in yield and productivity over the base reference strain, Para. [0610]).
Regarding Claim 17, Famili in view of Goletz discloses the system of claim 16. Famili in view of Goletz further discloses wherein the third scale is the same as the second scale (The final clones will be grown in identical conditions as the baseline CHO-S cell line so the change in genetic content is the only variable changing in the process and a straightforward evaluation of byproduct elimination can be made, Para. [0434]).
Regarding Claim 18, Famili in view of Goletz discloses the system of claim 16. Famili in view of Goletz further discloses wherein designing experiments includes screening the second strains based at least in part upon the predicted third-scale performance of the second strains (In addition to media optimization and development, such a computational modeling approach can be used to design cell culture processes, that is, process design, and to engineer cell lines, that is, cell line engineering, to improve a desired characteristic including, but not limited to, biomass production, viable cell density, product yields, and/or product titers to improve the overall productivity of the cell culture, Para. [0048]; Process design as used herein generally refers to the design and engineering of scale up from small to large scale processes, in particular as they are used in an industrial and commercial scale for culture of cells, Para. [0086]; To validate the accuracy of this objective function, cell culture data was simulated in the model for a parental, that is, a non-producing cell line, and an lgG producing NSO clone. The simulation results were benchmarked by comparing byproduct secretion rates to experimental measurements (Table 5). The results show that the metabolic model was able to correctly capture NSO cell line growth and metabolite uptake and secretion rates in a batch bioreactor for both the parental and lgG producing NSO clone, Para. [0225]; Finally, based on the initial decrease in byproduct formation, agreements with predictions and effectiveness of the computational approaches, second generation modifications to the generated cell lines will be determined .... These new designs will also be computed with the knowledge gained from algorithm performance in the predictability for the initial designs. Second generation modifications will be suggested for all of the generated cell lines, regardless of performance, and will feed directly into future iterations, Para. [0436]).
Regarding Claim 30, Famili in view of Goletz discloses the computer-readable media of claim 29. Famili in view of Goletz fails to explicitly disclose wherein the computer-readable media store further instructions that, when executed, cause at least one of the one or more computing devices to generate a first-scale statistical model of first-scale performance of the second strains, and use the first-scale statistical model to predict performance of the second strains at a third scale.
Serber teaches computer-readable media that store instructions for executing methods to perform statistical modeling of a candidate strain in a first scale to predict performance of a candidate strain in a second scale (In some embodiments. the present disclosure teaches iteratively improving the design of candidate microbial strains by (a) accessing a predictive model populated with a training set , Para. [0041]; Candidate strains are screened using bench scale fermentation reactors (e.g., reactors disclosed in Table 5 of the present disclosure) for relevant strain performance characteristics such as productivity or yield, Para. [0396]; During the analysis phase, the modified strain cultures are evaluated to determine their performance, i.e., their expression of desired phenotypic properties, including the ability to be produced at industrial scale, Para. [0469]; Candidate strains were generated. This example includes a serial build constraint associated with the introduction of new genetic changes to a parent strain, Para. [0491]; In supervised machine learning such as that of the linear regression example above, the machine (e.g., a computing device) learns, for example, by identifying patterns, categories, statistical relationships, or other attributes, exhibited by training data. The result of the learning is then used to predict whether new data will exhibit the same patterns, categories, statistical relationships or other attributes, Para. [0483]; Each newly created strain comprising a single SNP was tested for lysine yield in small scale cultures designed to assess product titer performance. Small scale cultures were conducted using media from industrial scale cultures, Para. [0604]; As predicted by the small scale high throughput cultures, larger tank cultures for strains comprising the combined zwf promoter swap and SNP 121 exhibited significant increases in yield and productivity over the base reference strain, Para. [0610]).
Regarding Claim 31, Famili in view of Goletz provides “The final clones will be grown in identical conditions as the baseline CHO-S cell line so the change in genetic content is the only variable changing in the process and a straightforward evaluation of byproduct elimination can be made” ([0434]), which teaches the third scale is the same as the second scale.
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Famili and Goletz with the teaching of Serber for the purpose of using modeling to predict how a candidate strain would perform in a large-scale culture system using performance data from small scale data meant to simulate large scale conditions ([0041], [0491]). . One would reasonably expect success as Famili, Goletz and Serber are all about cell culture scaling modeling and prediction, and Serber demonstrated his method is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols in a high-throughput way (Serber: Abstract).
Conclusion
No claims are allowed.
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