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 .
Status of the Claims
Claims 1-19 are pending and under consideration in this action.
Priority
The instant application is a CON of PCT/JP2021/005417, filed 2/15/2021, which claims priority to Japanese Application Number 2020-026428, filed 2/19/2020, as reflected in the filing receipt mailed on 8/26/2022. Acknowledgment is made of applicant' s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-19 is 2/19/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/29/2022, 10/10/2023, 12/21/2023, 9/20/2024, 11/8/2024, and 2/10/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s have been considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code: Para. [0078], “http://bigg.ucsd.edu/”. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim recites the phrase “wherein the optimized process condition acquisition step includes”, which should be followed by a colon for clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 13 and 19 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 13 and 19 recite the phrase “a creation step of creating a learned model by machine learning with the process condition and the culture prediction result, which are input in the input step, as learning data” and “wherein the learned model is obtained by machine learning based on a plurality of process conditions and culture prediction results obtained from the plurality of process conditions”, respectively.
MPEP 2161.01(I) recites:
“Original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. … When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing.”
For these claim elements, the disclosure “does not sufficiently describe how the function is performed or the result achieved”. The instant specification (Para. [0073]) recites “in the optimized process condition acquisition step, it is possible to find out the optimal process condition by machine learning (deep learning).… with the process conditions and the culture prediction results, which are input in the input step, as learning data, a regression model (learned model) is created by the machine learning (creation step). An inverse problem is solved by using the regression model created as a result of learning to calculate the optimal process condition (calculation step) …”. The instant specification (Para. [0079]) further recites an example where “deep learning is implemented by using the culture medium compositions of glucose and 20 kinds of amino acids as input conditions” and “new culture medium compositions of 10 pairs are calculated by using a regression model obtained as a result of learning”. However, the specification silent on the type of regression model used (e.g., linear, polynomial, logistic, etc.). Accordingly, the disclosure is not commensurate with the written description scope of the claim.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-16 are directed towards a method, claim 17 is directed towards a manufacture, and claim 18 is directed toward a machine, which falls into one of the categories of statutory subject matter (Step 1: YES).
Claim 19 is directed towards a software code for a computer program. Regarding claim 19, the recitation of “a learned model” does not provide any structural components and therefore equates to “software per se”. Claims that equate to “software per se” are not a statutory category of invention (see MPEP § 2106.03(I)). However, claim 19 could be amended to be statutory subject matter by adding in structural components such as by replacing “a learned model” with the phrase “a non-transitory computer-readable medium”. Nonetheless, this amendment would still result in a rejection of the claim under 35 U.S.C. 101 for recitation of a judicial exception without significantly more. In the interest of compact prosecution, claim 19 has also been analyzed below under 35 U.S.C. 101 using the Alice/Mayo two-part test below.
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mental process (i.e., an evaluation of the culture prediction results to determine an optimal condition) in “an optimized process condition acquisition step of finding out an optimal process condition from the culture prediction result obtained in the culture result prediction step”.
Claim 5 recites a mathematical concept (i.e., matrix generation) in “wherein the plurality of process conditions generated in the process condition generation step are acquired as a matrix”.
Claim 6 recites a mental process (i.e., selecting a portion of the data with a corresponding numerical value) in “wherein in the process condition generation step, a part of items of the process condition is selected and a numerical value for the selected item is determined”.
Claim 7 recites a mental process (i.e., comparison to a predetermined numerical value, experimental result, or expansion culture medium mixing strategy result) and a mathematical concept (i.e., random number generation) in “wherein the process condition generation step is performed by a method including at least any one of a method of determining a numerical value for an item of the process condition based on a predetermined numerical value, a method of performing the determination by random number generation in a predetermined range, a method of performing the determination by a numerical value obtained by an experiment, or a method of performing the determination based on an expansion culture medium mixing strategy”.
Claim 8 recites a mathematical concept (i.e., setting the range using a mechanistic equation) in “wherein the predetermined range is set by using a mathematically modeled equation of a mechanism by which an organism takes in a culture medium component”.
Claim 9 recites a mathematical concept (i.e., modeling the equation using Michaelis-Menten kinetics or Fick's law) in “wherein the mathematically modeled equation is Michaelis-Menten kinetics or Fick's law”.
Claim 10 recites a mathematical concept (i.e., random number generation) in “wherein in the method by the random number generation, the numerical value for the item of the process condition is determined by using a numerical value generated by a continuous uniform random number, a continuous normal random number, a discrete random number, or a binary random number”.
Claim 11 recites a mental process (i.e., an evaluation of the results to determine if the mechanism is included or if the results reproduce a bioprocess) in “wherein the culture result prediction step includes a mechanism of the cell to be cultured and a cell culture simulation method that reproduces a bioprocess”.
Claim 12 recites a mathematical concept (i.e., performing metabolic flux analysis; see Specification Para. [0063]) in “wherein the cell culture simulation method includes a modeling approach including metabolic flux analysis using a genome-scale metabolic model or flux balance analysis”.
Claim 13 recites a mathematical concept (i.e., creating a model using input data; it is noted that the machine learning model is a regression model, see Specification Para [0073]) in “a creation step of creating a learned model by machine learning with the process condition and the culture prediction result, which are input in the input step, as learning data”; and a mathematical concept (i.e., using the created model) in “a calculation step of calculating an optimal process by solving an inverse problem using the learned model”.
Claim 18 recites a mental process (i.e., an evaluation of the culture prediction results to determine an optimal condition) in “an optimized process condition acquisition unit that finds out an optimal process condition from the culture prediction result obtained by the culture result prediction unit”.
Claim 19 recites a mathematical concept (i.e., a machine learning model with input process conditions and culture results; it is noted that the machine learning model is a regression model, see Specification Para [0073]) in “wherein the learned model is obtained by machine learning based on a plurality of process conditions and culture prediction results obtained from the plurality of process conditions”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 1 recites “a process condition generation step of generating a plurality of process conditions for culturing a cell” and “a culture result prediction step of acquiring a culture prediction result of the cell for each of the plurality of process conditions generated in the process condition generation step”.
Claim 17 recites “a non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to perform the cell culture process search method”.
Claim 18 recites “a process condition generation unit that generates a plurality of process conditions for culturing a cell” and “a culture result prediction unit that acquires a culture prediction result of the cell for each of the plurality of process conditions generated by the process condition generation unit”.
Regarding the above cited limitations in claims 1 and 18 of (i) a process condition generation step/unit of generating a plurality of process conditions for culturing a cell (claims 1 and 18); and (ii) a culture result prediction step/unit of acquiring a culture prediction result of the cell for each of the plurality of process conditions generated in the process condition generation step/unit (claims 1 and 18). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of determining the optimal process condition (see MPEP § 2106.04(d)).
Regarding the above cited limitations in claim 17 of (iii) a non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to perform the cell culture process search method. This limitation requires only a generic computer component, which does not improve computer technology. Therefore, this limitation equates to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claims 2-4 further limit the types of process conditions or culture conditions, claim 13 recites an extra-solution step of inputting data, and claims 14-16 further limit the type of cells. As such, claims 1-19 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claims 1 and 18 of (i) a process condition generation step/unit of generating a plurality of process conditions for culturing a cell; and (ii) a culture result prediction step/unit of acquiring a culture prediction result of the cell for each of the plurality of process conditions generated in the process condition generation step/unit. These limitations do not include any specific steps for generating the process conditions or for acquiring culture prediction result. Under the BRI, these limitations are merely receiving data for subsequent step of determining the optimal process condition. Therefore, these limitations equate to receiving/transmitting data over a network, which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in in claim 17 of (iii) a non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to perform the cell culture process search method. This limitation equates to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-19 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 13-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Downey et al. (WIPO Application WO 2019/100040 A1; published 5/23/2019; cited in the IDS dated 10/10/2023).
Regarding claim 1, Downey et al. teaches a predictive model for propagating cell cultures based on initial, measured concentrations, and historical data (Title, Abstract). Downey et al. further teaches that the method includes determining a concentration of a at least one quality attribute in a cell culture and measuring at least one attribute influencing parameter within the cell culture (Claim 1). The quality attribute may comprise lactate, protein, cell growth rate, glycan composition, a charge variant, an aggregate, a clipping, disulfide oxidation, or a disulfide shuffling variant (i.e., a process condition generation step of generating a plurality of process conditions for culturing a cell) (Para. [00011]). Downey et al. further teaches that the method also includes sending the quality attribute concentration and the at least one attribute influencing parameter measurement to a controller, the controller including a predictive model that determines a future concentration of the quality attribute in the cell culture (i.e., a culture result prediction step of acquiring a culture prediction result of the cell for each of the plurality of process conditions generated in the process condition generation step) (Claim 1). Downey et al. further teaches that the method also includes selectively changing at least one condition within the cell culture based upon the determined future concentration of the quality attribute in the cell culture for maintaining the quality attribute concentration within preset limits (Claim 1). For example, the predictive model can be in communication with an optimizer. The optimizer can be configured to simulate results within the bioreactor if one or more conditions are varied. The conditions can include changing nutrient media feed rate and thereby changing glucose concentration, glutamate concentration, asparagine concentration, and the like. In addition to nutrient feed rates, the optimizer can also change various other conditions including pH and gas rate additions. The optimizer can run multiple simulations and numerous iterations in order to determine if corrective action is needed within the cell culture, and, if so, not only the best conditions to change in the bioreactor but the magnitude of the change. The predictive model ultimately determines variations in manipulated variables in order to minimize future deviations of the lactate concentration from a specified referenced trajectory. As future data is fed to the controller, the optimizer can continue to run simulations over the entire incubation period in order to further change or tweak manipulated variables thereby changing one or more conditions within the cell culture (i.e., an optimized process condition acquisition step of finding out an optimal process condition from the culture prediction result obtained in the culture result prediction step) (Para. [00054]).
Regarding claim 2, Downey et al. teaches that the quality attributes that can be controlled include protein titer, cell growth rate, and glycan composition. Glycan composition can include galactosylation, high mannose species, sialylation and fucosylation. In another embodiment, the quality attribute being controlled may comprise a charge variant. For instance, the charge variant may relate to C-terminal lysine cleavage, deamidation, adduct formation, succinide formation, oxidation, C-terminal praline amidation, isomerization, and/or sialylation. Still other quality attributes that can be controlled include aggregate concentration, clipping, disulfide oxidation, and a disulfide shuffling variant (i.e., wherein the process condition includes at least either of a plurality of culture medium compositions or a plurality of culture conditions) (Para. [0032]).
Regarding claim 3, Downey et al. teaches that the quality attribute may comprise lactate concentration (Para. [00011]). Ports are also in communication with the monitoring and control system, including measurement of pH and dissolved oxygen (i.e., wherein the culture condition is a setting condition including at least any one of oxygen addition, or replenishment of a culture medium and a nutrient) (Para. [00047]).
Regarding claim 4, Downey et al. teaches that the process and system can be scaled to various different bioreactor sizes. For instance, the predictive models can be used in clinical as well as commercial manufacturing (i.e., wherein the culture condition includes a design and operation of scale-up from a small-scale process to a large-scale process) (Para. [0034]).
Regarding claim 13, Downey et al. teaches that the optimizer is configured to simulate results within the bioreactor if one or more conditions are varied. The conditions can include changing nutrient media feed rate and thereby changing glucose concentration, glutamate concentration, asparagine concentration, and the like (i.e., an input step of inputting the process condition generated in the process condition generation step and the culture prediction result acquired in the culture result prediction step) (Para. [00054]). Downey et al. further teaches that classification models were developed to predict the final lactate state from process data present through a specified end day (days 3, 4, and 5). For each end day considered, the following classification models were developed: linear discriminant analysis (LDA), classification trees, linear discriminant analysis applied to partial least squares scores (PLS-LDA), support vector machines (SVM), and logistic regression. Each individual model was computed from the batch-unfolded process data present in the training data set using functions from the MATLAB statistics and machine learning toolbox (i.e., a creation step of creating a learned model by machine learning with the process condition and the culture prediction result, which are input in the input step, as learning data) (Para. [00082]). Downey et al. further teaches that models consistently yielding good classification accuracy across all end days included: PLS-LDA, LOA, classification trees and ensembles of these models. The classification models were able to accurately classify favorable and unfavorable lactate runs with validation accuracy ranging between 83% (Day 3) and 88% (Day 4 & 5). Though the day 4 and 5 models achieved equivalent validation classification accuracy in total, the day 4 ensemble model produced more consistent validation performance across clones. Attributes commonly appearing across models include metabolites (glutamate, glucose and glutamine) and attributes related to pH modulation (CO2 sparge rate) (i.e., a calculation step of calculating an optimal process by solving an inverse problem using the learned model) (Para. [00083]).
Regarding claim 14, Downey et al. teaches that the process and system is directed towards propagating mammalian cell cultures (i.e., wherein the cell is a eukaryotic cell) (Para. [0009]).
Regarding claim 15, Downey et al. teaches that the methods allow for the production of eukaryotic cells, e.g., mammalian cells or lower eukaryotic cells such as for example yeast cells or filamentous fungi cells (i.e., wherein the eukaryotic cell is a cell strain derived from an animal, a plant, or an insect, a primary culture product, or a fungus) (Para. [00097]).
Regarding claim 16, Downey et al. teaches that that the methods allow for production of prokaryotic cells such as Gram-positive or Gram-negative cells, such as Bacillus subtilis, Salmonella spp., and Escherichia coli (i.e., wherein the prokaryotic cell is a bacterium including Escherichia coli, Bacillus subtilis, cyanobacteria, or actinomycetes and an archaeon including methanogen, extreme halophile, or hyperthermophile) (Para. [00097] and [000109]-[000110]).
Regarding claim 18, Downey et al. teaches an example embodiment, wherein the analyzer periodically or continuously monitors lactate concentration (i.e., the process condition generation unit), which is communicated to the controller. Within the controller, the predictive model (i.e., the culture result prediction unit) can also be in communication with the optimizer (i.e., the optimized process condition acquisition unit) (Para. [00051] and [00054] and Fig. 2). Downey et al. further teaches the limitations of a process condition generation unit that generates a plurality of process conditions for culturing a cell; a culture result prediction unit that acquires a culture prediction result of the cell for each of the plurality of process conditions generated by the process condition generation unit; and an optimized process condition acquisition unit that finds out an optimal process condition from the culture prediction result obtained by the culture result prediction unit as described for claim 1 above.
Regarding claim 19, Downey et al. teaches the generation of a plurality of process conditions and the acquisition of culture prediction results for the plurality of process conditions as described for claim 1 above. Downey et al. further teaches that the plurality of process conditions and the culture prediction results are input for the model as described for claim 13 above. Downey et al. further teaches that classification models were developed to predict the final lactate state from process data present through a specified end day (days 3, 4, and 5). For each end day considered, the following classification models were developed: linear discriminant analysis (LOA), classification trees, linear discriminant analysis applied to partial least squares scores (PLS-LDA), support vector machines (SVM) and logistic regression. Each individual model was computed from the batch-unfolded process data present in the training data set using functions from the MATLAB statistics and machine learning toolbox (i.e., wherein the learned model is obtained by machine learning based on a plurality of process conditions and culture prediction results) (Para. [00082]).
Therefore, Downey et al. teaches the limitations disclosed in claims 1-4, 13-16, and 18-19.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
1. Claims 5-6 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Downey et al. as applied to claims 1-4, 13-16, and 18-19 above, and further in view of Zampieri et al. (A poly-omics machine-learning method to predict metabolite production in CHO cells, in Proceedings of the 2nd International Electronic Conference on Metabolomics, 20–27 November 2017, MDPI: Basel, Switzerland; published 11/20/2017).
Downey et al., as applied to claims 1-4, 13-16, and 18-19 above, does not teach wherein the plurality of process conditions generated in the process condition generation step are acquired as a matrix (claim 5); wherein in the process condition generation step, a part of items of the process condition is selected and a numerical value for the selected item is determined (claim 6); wherein the culture result prediction step includes a mechanism of the cell to be cultured and a cell culture simulation method that reproduces a bioprocess (claim 11); and wherein the cell culture simulation method includes a modeling approach including metabolic flux analysis using a genome-scale metabolic model or flux balance analysis (claim 12).
Regarding claim 5, Zampieri et al. teaches a model that combines machine learning with metabolic modeling to estimate the lactate production in CHO cell cultures (Abstract). Zampieri et al. further teaches that as a first data source, a large-scale gene expression dataset from two different CHO cell lines was used. The dataset contains 295 microarray profiles with expression values for 3,592 genes from 121 CHO cell cultures of varying conditions in terms of including cell density, growth rate, viability, lactate and ammonium accumulation and cell productivity (Pg. 3, Lines 90-95). Though not explicitly taught by Zampieri et al., it would be obvious to one of ordinary skill in the art to store this dataset in a matrix to be used in subsequent modeling in MATLAB (Pg. 4, Lines 122-123).
Regarding claim 6, Zampieri et al. teaches that as a first data source, a large-scale gene expression dataset from two different CHO cell lines was used. The dataset contains 295 microarray profiles with expression values for 3,592 genes from 121 CHO cell cultures of varying conditions in terms of including cell density, growth rate, viability, lactate and ammonium accumulation and cell productivity. Of the total profiles, they extracted the 127 profiles with available quantification of lactate accumulation (i.e., a part of the items of the process conditions is selected and a numerical value for the selected item is determined) (Pg. 3, Lines 90-95).
Regarding claim 11, Zampieri et al. teaches that to create condition and cell-line specific poly-omics models the genome-scale model of CHO cell metabolism was combined with the gene expression data from CHO cell cultures in varying conditions. In this step, data accessible via the BIGG repository was employed to match gene identifiers. A model for each condition was created by computing gene set effective expressions for each reaction (i.e., wherein the culture result prediction step includes a mechanism of the cell to be cultured) (Pg. 3-4, Lines 104-109). Zampieri et al. further teaches that after a model for each condition was created, flux distributions were computed using flux balance analysis by maximizing the biomass for producing cell lines included in the CHO model (i.e., a cell culture simulation method that reproduces a bioprocess) (Pg. 4, Lines 119-123).
Regarding claim 12, Zampieri et al. teaches that after a model for each condition was created, flux distributions were computed using flux balance analysis by maximizing the biomass for producing cell lines included in the CHO model (i.e., wherein the cell culture simulation method includes a modeling approach including metabolic flux analysis using a genome-scale metabolic model or flux balance analysis) (Pg. 4, Lines 118-123).
Therefore, regarding claims 5-6 and 11-12, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive model for optimizing cell culture conditions of Downey et al. with the flux balance analysis of Zampieri et al. because combining gene expression and metabolic fluxes improves the accuracy of predicting metabolite/protein production conditions compared to using each type of data separately (Zampieri et al., Pg. 6-7, Lines 190-202). One of ordinary skill in the art would be able to combine the teachings of Downey et al. with Zampieri et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for determining optimal cell culture conditions. Therefore, regarding claims 5-6 and 11-12, the instant invention is prima facie obvious (MPEP § 2142).
2. Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Downey et al. as applied to claims 1-4, 13-16, and 18-19 above, and further in view of Mehta et al. (Quantitative inference of cellular parameters from microfluidic cell culture systems. Biotechnol Bioeng. 103(5): 966-974 (2009); published 3/27/2009).
Downey et al., as applied to claims 1-4, 13-16, and 18-19 above, does not teach wherein the process condition generation step is performed by a method including at least any one of a method of determining a numerical value for an item of the process condition based on a predetermined numerical value, a method of performing the determination by random number generation in a predetermined range, a method of performing the determination by a numerical value obtained by an experiment, or a method of performing the determination based on an expansion culture medium mixing strategy (claim 7); wherein the predetermined range is set by using a mathematically modeled equation of a mechanism by which an organism takes in a culture medium component (claim 8); and wherein the mathematically modeled equation is Michaelis-Menten kinetics or Fick's law (claim 9).
Regarding claim 7, Mehta et al. teaches a mathematical model describing the spatial distribution of nutrient and growth factor concentrations in inferring cellular oxygen uptake rates from experimental measurements in microfluidic cell culture systems. They use experimental measurements of oxygen concentrations in a poly(dimethylsiloxane) (PDMS) microreactor culturing human hepatocellular liver carcinoma cells to infer quantitative information on cellular oxygen uptake rates (i.e., wherein the process condition generation step is performed by a method including at least any one of determining a numerical value for an item of the process condition based on a predetermined numerical value and a method of performing the determination by a numerical value obtained by an experiment) (Abstract).
Regarding claims 8 and 9, Mehta et al. teaches that the cellular uptake of oxygen is dependent on the cell density and can be modeled using a logistic term in the Michaelis–Menten equation (i.e., wherein the predetermined range is set by using a mathematically modeled equation of a mechanism by which an organism takes in a culture medium component and wherein the mathematically modeled equation is Michaelis-Menten kinetics) (Abstract).
Therefore, regarding claims 7-9, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive model for optimizing cell culture conditions of Downey et al. with the kinetic models of Mehta et al. because the model of Mehta et al. can be readily used for the design or optimization of microfluidic cell culture reactors, as well as readily adapted to measure uptake rates of other soluble factors (e.g., nutrients, cell secreted signaling molecules) (Mehta et al., Pg. 973, Col. 1, Para. 1). One of ordinary skill in the art would be able to combine the teachings of Downey et al. with Mehta et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for modeling cell culture parameters. Therefore, regarding claims 7-9, the instant invention is prima facie obvious (MPEP § 2142).
3. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Downey et al. in view of Mehta et al. as applied to claims 1 and 7-9 above, and further in view of Schellenberger et al. (Use of Randomized Sampling for Analysis of Metabolic Networks. J Biol Chem. 284(9): 5457-5461 (2009); published 2/27/2009).
Downey et al. in view of Mehta et al., as applied to claims 1 and 7-9 above, does not teach wherein in the method by the random number generation, the numerical value for the item of the process condition is determined by using a numerical value generated by a continuous uniform random number, a continuous normal random number, a discrete random number, or a binary random number.
Regarding claim 10, Schellenberger et al. teaches the use of randomized sampling in the analysis of metabolic flux networks (Title, Abstract). Schellenberger et al. further teaches the use of uniform random samples (i.e., wherein in the method by the random number generation, the numerical value for the item of the process condition is determined by using a numerical value generated by a continuous uniform random number) (Pg. 5459, Col. 2, Para. 1).
Therefore, regarding claim 10, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictive model for optimizing cell culture conditions of Downey et al. in view of Mehta et al. with the random sampling of Schellenberger et al. because the random sampling of Schellenberger et al. was used to generate random media conditions for subsequent flux balance analysis (Schellenberger et al., Pg. 5459, Col. 1, Para. 3). One of ordinary skill in the art would be able to combine the teachings of Downey et al. in view of Mehta et al. with Schellenberger et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for processing input data for subsequent modeling. Therefore, regarding claim 10, the instant invention is prima facie obvious (MPEP § 2142).
4. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Downey et al. as applied to claims 1-4, 13-16, and 18-19 above.
Regarding claim 17, in In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplish the same result is not sufficient to distinguish over the prior art (see also MPEP § 2144.04(III)). In the instant case, the claimed invention merely makes the process of Downey et al. as computer-implemented or automatic and indeed accomplishes the same result. It is thus not sufficient to distinguish over Downey et al. Therefore, the claimed invention, i.e. “a non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to perform the cell culture process search method” would have been obvious to a person of ordinary skill in the art at the time the invention was made over the process disclosed by Downey et al. There would have been a reasonable expectation of success because the court held regarding software that “writing code for such software is within the skill of the art, not requiring undue experimentation, once its functions have been disclosed.” Fonar Corp., 107 F.3d at 1549, 41 USPQ2d at 1805.
Conclusion
No claims allowed.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687