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 .
Claims 1-20 are pending in this application.
Election/Restrictions
Applicant’s election without traverse of Group I (Claims 1-8) and of the species (multiple alternative regression prediction model: support vector regression) in the reply filed on 11/28/2025 is acknowledged. Claims 9-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/28/2025.
Claims 1-8 were examined on their merits.
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.
Claim 3 is 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 3 recites, “wherein the culture indicators comprise…glucose, lactic acid or ammonia”. It is unclear how “culture indicators” correlate with the recited chemical compounds, that is, whether the compounds are required to be present or not in the cell culture experiments.
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 1-8 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to Judicial Exception(s) without significantly more. The claim(s) recite(s) Abstract Ideas of Mathematical concepts and Mental processes.
These judicial exceptions are not integrated into a practical application because they amount to a general linkage of the use of the JEs to a particular technological environment/field of use, that is, optimizing a basal culture medium. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Step 1) The claims are drawn to a process
Step 2A, P1) The claims recite the Judicial Exception of Abstract Ideas. Claim 1 recites: “selecting and optimizing”, “to determine”, “for predicting”, “acquiring”, “enumerating”, “randomly selecting”, “predicting”;
Claim 2 recites: “searching within”, “acquiring culture indicator data”, “creating a data set”, “selecting”;
Claim 5 recites, “creating a training data sample set” and “optimizing the components”;
Claim 7 recites, “acquiring”, “enumerating and randomly selecting”, “acquiring a point value”, “sorting the value sequences”, “constructing a component value matrix”, “obtaining candidate basal culture medium formulations”.
Claim 4 recites the Judicial Exception of a mathematical concept. Claim 4 recites, “the step of creating a training sample data set with the addition amount of each component or its normalized value in the training formulation as an input matrix and the culture indicator data as an output matrix, comprises
-forming a set of experimental data by input data (x1, x2, ..., Xn) and output data (y1, y2, ..., Ym); wherein x; is the ith component of the basal culture medium formulation, used as a feature during model training, verification and testing; y1 represents a cell survival rate, y2 represents a cell density, y3 represents a protein expression level, ..., and ym represents the mth output indicator;
the input matrix of the regression model is X matrix, wherein xij represents the jth component of the ith formulation; and the output matrix is Y matrix, wherein yij represents the jth output value of the ith formulation:
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Step 2A, P2) These judicial exceptions are not integrated into a practical application because they amount to a general linkage of the use of the JEs to a particular technological environment/field of use, that is, optimizing a basal culture medium.
Step 2B) The claims do not recite additional elements that amount to significantly more than the JEs. Claims 1 and 2 recite “conducting cell culture experiments” which are recited with a high degree of generality, are mere data gathering, and are well-known and routine in the cel culture art. See for example, Biniarz et al. whom recites testing various culture media containing different components and under different conditions to optimize lipopeptide production (Pg. 1, Abstract and Pg. 4, Table 1, Pg. 5, Tables 2-3, Pg. 7, Table 4 and Pg. 9, Table 5).
Claim 3 merely lists culture indicators which can be monitored, Claim 6 recites multiple alternative regression prediction models (which are themselves abstract ideas/mathematical processes) and Claim 8 merely delineates the number of possible candidate basal culture medium formulations.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Applicant cannot rely upon the certified copy of the foreign priority application to overcome this rejection because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216.
Claims 1, 2, 3, 5 and 6 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Liang et al. (CN113450882A), machine translation, cited in the IDS.
The applied reference has common inventors with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. § 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Liang et al. teaches selecting a regression model and performing regression model training (optimizing) using a basal medium sample formulation to obtain a basal medium formula culture effect prediction model (Pg. 6, Paragraph [0029]) though an experimentally verified basal culture database (Pg. 6, Paragraph [0028] and acquiring an addition range of each component in the basal culture medium (Pg. 6, Paragraph [0027]), and enumerating and randomly selecting addition amount of each component,
to generate a plurality of candidate basal culture medium formulations (Pgs. 3-4, Paragraphs [0012]-[0016];
performing culture affect regression prediction aiming at an optimization target by adopting the culture effect prediction model of the basic culture medium formula (Abstract) and screening one or more formulations from the candidate basal culture medium formulations as recommended basal culture medium formulations based on prediction results (Pg. 6, Paragraph [0030];
and conducting cell culture experiments with the obtained recommended basal culture medium formulations, to verify the culture indicators of the recommended basal culture medium formulations and determine an optimal basal culture medium formulation based on the verified culture indicators (Pg. 5, Paragraphs [0022]-[0023] and Pg. 17, Paragraph [0089], and reading on Claim 1.
With regard to Claims 2 and 6, the reference teaches establishing a training formulation database for each culture media components’ lowest to maximum addition value (Pg. 8, Paragraph [0044] and Pg. 10, Paragraph [0055]);
conducting cell culture experiments for each medium culture formula to collect culture effect data thereby creating a training formulation sample data set (Pg. 10, Paragraph [0055]);
training a machine learning model for optimization using the sample formula training database including multiple alternative regression prediction models and selecting support vector machine regression (Pg. 10, Paragraph [0055] and Pg. 11, Paragraphs [0057]-[0059]).
With regard to Claim 3, the reference teaches monitoring cell state culture parameters during culturing including cell density, protein expression, glucose, lactic acid and ammonia (Pg. 5, Paragraph [0023]).
With regard to Claim 5, the reference teaches the optimization of the components of the training formulation through feature selection of the regression model (Pgs. 16-17, Paragraphs [0088]-[0089]).
Claims 1-8 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Liang et al. (CN113450868A), machine translation, cited in the IDS.
The applied reference has common inventors with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. § 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed;
or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Liang et al. (‘868) teaches selecting and optimizing regression models through an experimentally verified basal medium formula database to determine a regression prediction model to predict the basal medium for the cultivation index (culture indicators) (Pgs. 12-13, Paragraphs [0079]-[0080] and Pg. 25, Claim 1);
acquiring an addition range for each component of the basal culture medium, enumerating and randomly selecting the addition amount of each component to generate a plurality of alternative basal medium formulas (Pg. 13, Paragraph [0081] and Pg. 25, Claim 2);
predicting culture indicators for the alternative basal medium formulas using regression prediction models to select one of the alternative basal medium formulas according to prediction results (Pg. 13, Paragraph [0085] and Pg. 25, Claim 3);
conducting cell culture experiments with the obtained predicted optimal formulas to verify culture indicators (cell density) and determine the optimal basal culture medium (Pg. 23, Paragraph [0157]-[0162] and Pg. 25, Claim 4), reading on Claim 1.
With regard to Claim 2, the reference teaches searching within the addition range of each component of the basal culture medium, obtaining cultivation index data by performing cell culture experiments, organizing the training sample data set with the added amount of each component or its normalized value as the input matrix in the training index data and the cultivation index/indicator data as the output matrix and training and testing multiple alternative regression prediction models with the training sample data under the same conditions and selecting the best as the regression prediction model of the cultivation index/indicators (Pg. 26, Claim 2).
With regard to Claim 3, the reference teaches the culture indicators include but are not limited to: cell viability, cell density, protein expression, glucose, Lactic acid, ammonia (Pg. 26, Claim 3).
With regard to Claim 4, the reference teaches each set of experimental data consists of input data (x1, x2, ..., xn) and output data (y1, y2, ..., ym); where xi is the i-th component of the basic medium formula, during model training, it is used as a feature during verification and testing; y1 represents cell viability, y2 represents cell density, y3 represents protein expression, ym represents the m-th output index; the input matrix of the regression model is shown in the X matrix, where xij Represents the j-th ingredient of the i-th formula; the output matrix is shown as the Y matrix, where yij represents the j-th output value of the i-th formula:
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(Pg. 26, Claim 4 and Original Chinese Patent, Pg. 40, Paragraphs [0107]-[0108]).
With regard to Claim 5, the reference teaches the optional regression prediction model includes but not limited to: support vector regression model, elastic network model , an Xgboost model, a Gradient Boosting Regression model, a Logostic Regression model, a regression model based on a multilayer neural network, a
regression model based on a convolutional neural network, and/or a regression model based on a recurrent neural network (Pg. 27, Claim 6).
With regard to Claim 7, the reference teaches for each component of the basal medium, take the same number of point values within its value range to
form a value series of the components; Preferably, the same number of point values constitutes an arithmetic difference or a geometric sequence, which has better uniformity and can better cover its value range; Randomly sorting the value series of the components obtained to obtain the rearranged component value series; After the rearrangement obtained,
the value columns of all components are used as rows or columns to form a component value matrix, and the columns or rows of the component value matrix are used as each component of the formula to get the value to obtain the alternative basal medium formula (Pg. 27, Claim 7).
With regard to Claim 8, the reference teaches the number of alternative basal medium formulations is 1,000 to 1,000,000 (Pg. 27, Claim 8).
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to PAUL C MARTIN whose telephone number is (571)272-3348. The Examiner can normally be reached Monday-Friday 12pm-8pm EST.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Sharmila G Landau can be reached at (571) 272-0614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL C MARTIN/ Examiner, Art Unit 1653 12/16/2025