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 .
Information Disclosure Statement
The IDS filed 3/21/2024 and 5/27/2026 have been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of Japan application JAPAN 2021-107842 filed 06/29/2021 is acknowledged.
Claim Status
Claims 1-21 are pending and are examined on the merits.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-12 are to a “method” for creating a cell mathematical model, with a series functional steps. So a process.
Claim 13 is to a non-transitory, computer-readable tangible recording medium which records thereon a cell mathematical model determination program, so a machine or manufacturer.
Claim 14 is to a cell mathematical model creation apparatus, so a machine or manufacturer.
Claims 15-19 are to another “method” for determining a cell mathematical model (the cell mathematical model being created by the method for creating a cell mathematical model according to claim 1), with a series functional steps. So another process.
Claim 20 is to a non-transitory, computer-readable tangible recording medium which records thereon a cell mathematical model determination program, so a machine or manufacturer.
Claim 21 is to a cell mathematical model determination apparatus, so a machine or manufacturer.
Step 2A Prong One: Identification of Abstract Ideas
The claim(s) recite(s):
Extracting feature values of cell activity from the culture data (claims 1, 14 and 21);
“Extracting feature values of cell activity from the culture data” reads on data manipulation, a process that can be achieved in the human mind. Therefore, this step equates to an abstract idea of mental processes.
Creating a cell mathematical model from the feature values (claims 1, 14 and 21);
This step recites a mathematical process based on the feature values. therefore, this step equates to an abstract idea of mathematical concepts.
Determining that estimated culture data calculated using the cell mathematical model reflects the culture data (claims 15 and 21).
This step recites a judgement/opinion activity (data calculated using the cell mathematical model reflects the culture data) based on data observation. therefore, this step equates to an abstract idea of mental processes.
Dependent claims further describe data, cellular metabolite, temporal change, which are parts of the judicial exceptions.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of Creating a cell mathematical model from the feature values, and Determining that estimated culture data calculated using the cell mathematical model reflects the culture data, which are abstract ideas. The claims do not recite any additional elements that integrate the abstract idea/judicial exception 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 claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
Receiving culture data of cells (claims 1, 14 and 21);
Outputting the cell mathematical model (claims 1, 14 and 21).
A non-transitory, computer-readable tangible recording medium (claim 20).
A cell mathematical model determination apparatus (claim 21).
A processor (claim 21).
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception because it is routine and conventional to perform the acts of acquiring data and outputting data. Other elements of the method include a processor, a CRM and an apparatus which are recitations of generic computer structure that serves 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 § 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 1-21 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.
From claim 1 to claim 21, claims recite “a cell mathematical model” 27 times altogether, it is not clear the relationship among all these “a cell mathematical model”. The 27 “a cell mathematical model” are all independent, because they don’t refer back. Then a question come up: what are the relationship among these 27 “a cell mathematical model” because normally a claim set don’t need so many independent “cell mathematical model”. It is suspected that at least some of the “a cell mathematical model” should read as “the cell mathematical model” in order to refer back.
Applicant can amend the claims to verify the relationships among the cell mathematical models. For the same “cell mathematical model”, the later instance can refer the initial instance as “the cell mathematical model”.
Claim 12 recites “wherein the prokaryotic cells are bacteria including Escherichia coli, Bacillus subtilis, cyanobacteria, or actinomycetes, and archaebacteria including methanogens, hyperhalophiles, or hyperthermophiles.” It is not clear about the relationships among recited bacteria due to the confusing “or … and …or …” connections.
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, 10-11, 13-15 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sriyudthsak et al. ("Mathematical modeling and dynamic simulation of metabolic reaction systems using metabolome time series data." Frontiers in molecular biosciences 3 (2016): 15), in view of Miyake et al. ("Digital cell." U.S. Patent No. 7,747,390B2. 29 Jun. 2010).
Claim 1 is interpreted as a method for creating a mathematical model. Regarding claim 1, Sriyudthsak provides (page 2, col 1, last para) “the workflow begins with sampling biological data consisting of simultaneously acquired metabolic profiles. The profiles are processed and utilized for constructing a mathematical model, which can be exploited to analyze metabolic systems as well as to design an optimal system of interest”, which teaches receiving culture data of cells; extracting feature values (which is obvious over profiles), and creating mathematical models.
However, Sriyudthsak does not teach everything of extracting feature values. Sriyudthsak does not teach outputting mathematical model either.
Miyake provides (col 25, 2nd para) “the term ‘digital cell’ refers to a collection of at least one experimental data on a cell of experimental interest. These experimental data correlate the experimental conditions and the experimental results of an example conducted against an actual cell”, which suggest extracting feature values of cell activity from the culture data.
Miyake provides (col 75, 8th para) “the output section 503 outputs a cellular state
selected by the CPU 502 as display data. The output data is output through the I/O device 506”, which teaches output the mathematical models.
Regarding claim 10, , Sriyudthsak does not teach eukaryotic cells or prokaryotic cells. Miyake provides (col 25, 4th para) “Cells used herein may be derived from any organism (e.g., any unicellular organism (e.g., bacteria and yeast) or any multicellular organisms (e.g., animals (e.g., vertebrates and invertebrates), plants (e.g., monocotyledons and dicotyl dons, etc.))”, which teaches the cells are eukaryotic cells or prokaryotic cells.
Regarding claim 11, Sriyudthsak does not teach eukaryotic cells. Miyake provides (col 25, 4th para) “Cells used herein may be derived from any organism (e.g., any unicellular organism (e.g., bacteria and yeast) or any multicellular organisms (e.g., animals (e.g., vertebrates and invertebrates), plants (e.g., monocotyledons and dicotyl dons, etc.))”, which teaches the eukaryotic cells are animal cells.
Regarding claim 13, Sriyudthsak does not teach a computer recording medium. Miyake provides (col 22, 14th para): “FIG.35 depicts an example of a configuration of computer system 3501 which provides a service reproducing an experimental result obtained using an actual cell using the digital cell”, which teaches computer-readable-medium implementation for the mathematical model.
Regarding claim 14, Sriyudthsak does not teach an apparatus. Miyake provides (col 22, 14th para): “FIG.35 depicts an example of a configuration of computer system 3501 which provides a service reproducing an experimental result obtained using an actual cell using the digital cell”, which teaches computer-readable-medium implementation for the mathematical model. Other than that, the art applied to claim 1 also teaches claim 14.
Regarding claim 15, Sriyudthsak provides (page 5, col 2, 3rd para) “In general, parameter values are determined by minimizing an objective function measuring the difference between experimental data (time series data) and model predictions (predicted data)”, which teaches comparison/acceptance based on comparison of estimated data vs culture data.
Regarding claim 18, Sriyudthsak provides (page 5, col 2, 3rd para) “In general, parameter values are determined by minimizing an objective function measuring the difference between experimental data (time series data) and model predictions (predicted data)”, which teaches minimizing an objective function measuring the difference of observed/predicted data. Sum of absolute values of difference is a routine optimization choice.
Regarding claim 19, Sriyudthsak does not teach comparing difference between the calculated values vs the obtained values. Miyake provides (col 12, penultimate para, claim 134) “A method for providing a service which reproduces an experimental result of an actual cell using a digital cell by means of a computer system”, which teaches computer reproduces experiment result and validation in simulation.
Regarding claim 20, Sriyudthsak provides (page 5, col 2, 3rd para) “In general, parameter values are determined by minimizing an objective function measuring the difference between experimental data (time series data) and model predictions (predicted data)”, which teaches comparison/acceptance based on prediction error.
Regarding claim 21, Sriyudthsak provides (page 2, col 1, last para) “the workflow begins with sampling biological data consisting of simultaneously acquired metabolic profiles. The profiles are processed and utilized for constructing a mathematical model, which can be exploited to analyze metabolic systems as well as to design an optimal system of interest”, which teaches receiving culture data of cells; extracting feature values (which is obvious over profiles), and creating mathematical models.
However, Sriyudthsak does not teach everything of extracting feature values. Sriyudthsak does not teach outputting mathematical model either.
Miyake provides (col 25, 2nd para) “the term ‘digital cell’ refers to a collection of at least one experimental data on a cell of experimental interest. These experimental data correlate the experimental conditions and the experimental results of an example conducted against an actual cell”, which suggest extracting feature values of cell activity from the culture data.
Miyake provides (col 75, 8th para) “the output section 503 outputs a cellular state
selected by the CPU 502 as display data. The output data is output through the I/O device 506”, which teaches output the mathematical models.
Sriyudthsak provides (page 5, col 2, 3rd para) “In general, parameter values are determined by minimizing an objective function measuring the difference between experimental data (time series data) and model predictions (predicted data)”, which teaches comparison/acceptance based on comparison of estimated data vs culture data.
It would have been prima facie obvious to a person of ordinary skill to combine Sriyudthsak and Miyake because Sriyudthsak teaches receiving/processing biological time-series/metabolic data to construct a mathematical model, and calibrating parameters by minimizing a difference between experimental and predicted data. Miyake teaches a computer-based “digital cell” that correlates experimental cell conditions with outcomes and reproduces an outcome from a supplied condition. Each reference addresses computational construction, extraction model features from experimental data.
One would reasonably expect success because the combination applies Sriyudthsak’s data-fitted metabolic modeling workflow, implemented in Miyake’s computer-based condition/result model, would result in an optimized cell mathematical model whose predictions are validated by experimental culture data.
Claims 3 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sriyudthsak and Miyake, as applied to claims 1, 10-11, 13-15 and 18-21 above, and further in view of Orth et al. (“What is flux balance analysis?” Nature Biotechnology. (2010)).
Regarding claim 3, neither Sriyudthsak nor Miyake teaches separating the feature values into the input factors and the output factors. Orth provides (page 245, col 2, 2nd para) “Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system”, which teaches an input factor into the cells and an output factor from the cells; Sriyudthsak teaches time-series data (page 1, title and Abstract), which suggests dynamic temporal “any time” application of the cell mathematical model.
Regarding claim 16-17, neither Sriyudthsak nor Miyake teaches the input factor and the output factor for the cells Orth provides (page 245, col 2, 2nd para) “Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system”, which teaches an input factor into the cells and an output factor from the cells; Sriyudthsak teaches time-series data (page 1, title and Abstract), which suggests dynamic temporal any time application of the cell mathematical model.
It would have been prima facie obvious to a person of ordinary skill to combine the combined Sriyudthsak and Miyake teaching of receiving/processing biological time-series/metabolic data to construct a mathematical model, calibrating parameters by minimizing a difference between experimental and predicted data, with Orth’s teaching of FBA constraints that balance reaction inputs and outputs, because Orth’s method includes bounds and optimization.
One would reasonably expect success because the combination applies known FBA input/output constraints and known genetic-algorithm model evolution to Sriyudthsak’s data-fitted metabolic modeling workflow, implemented in Miyake’s computer-based condition/result model. The result will be an optimized cell mathematical model whose performance would have been predictable.
Conclusion
No claims are allowed.
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
