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 information disclosure statements (IDS) submitted on 01/26/2026 and 05/14/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: Claim one states a “machine learning analysis system” which is not present in the Specification filed on 12/30/2025 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a data collection system configured to collect performance data for a plurality of entities and market data comprising costs for development inputs in claim 1;
a development system configured to predict performance of the entities under different process conditions in claim 1;
a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions using one or more machine learning models trained on economic data, wherein the economic data includes market data, and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-13 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 one’s limitation of “a development system configured to predict performance of the entities under different process conditions” and “a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions using one or more machine learning models trained on economic data, wherein the economic data includes market data, and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
[Para. 0639] of the Applicant’s Specification partly states: “synthetic biology development system configured to predict performance of the synthetic biologic products under different process conditions...wherein the synthetic biology development system is further configured to prioritize development of synthetic biology products based on the predicted performance and the economic viability predictions.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a development system and/or clearly linking a structure to the claimed function of a development system .
[Para. 0643] of the Applicant’s Specification partly states: “synthetic biology development system generates economic viability predictions for a plurality of parallel development paths for multiple synthetic biology products, wherein the synthetic biology is configured to dynamically allocate development resources between the parallel development paths based on comparing the economic viability predictions.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a development system and/or clearly linking a structure to the claimed function of a development system .
[Para. 1612] of the Applicant’s Specification partly states: “The selection of the variant for inclusion in the adjusted biologic synthesis process may be based on a machine learning analysis of the biologic synthesis process, wherein details and/or measurements of the biologic synthesis process may be processed by a machine learning model that is trained to identify and address bottlenecks arising in biologic synthesis processes.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a machine learning analysis system and/or clearly linking a structure to the claimed function of a machine learning analysis system.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-13 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 claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, 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.
Claim one’s limitation of “a development system configured to predict performance of the entities under different process conditions” and “a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions using one or more machine learning models trained on economic data, wherein the economic data includes market data, and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
[Para. 0639] of the Applicant’s Specification partly states: “synthetic biology development system configured to predict performance of the synthetic biologic products under different process conditions...wherein the synthetic biology development system is further configured to prioritize development of synthetic biology products based on the predicted performance and the economic viability predictions.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a development system and/or clearly linking a structure to the claimed function of a development system .
[Para. 0643] of the Applicant’s Specification partly states: “synthetic biology development system generates economic viability predictions for a plurality of parallel development paths for multiple synthetic biology products, wherein the synthetic biology is configured to dynamically allocate development resources between the parallel development paths based on comparing the economic viability predictions.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a development system and/or clearly linking a structure to the claimed function of a development system .
[Para. 1612] of the Applicant’s Specification partly states: “The selection of the variant for inclusion in the adjusted biologic synthesis process may be based on a machine learning analysis of the biologic synthesis process, wherein details and/or measurements of the biologic synthesis process may be processed by a machine learning model that is trained to identify and address bottlenecks arising in biologic synthesis processes.” This recitation of Applicant’s Specification contains insufficient disclosure of structure for performing the claimed function of a machine learning analysis system and/or clearly linking a structure to the claimed function of a machine learning analysis system.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. While the independent claims 1, 14 and 18 are directed to statutory subject matter under Step 1 (i.e., they are directed to a machine, process and an article of manufacture), the independent claims recite the following judicial exceptions:
a development system configured to predict performance of the entities under different process conditions in claim 1;
a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions in claim 1;
wherein the economic data includes market data, and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions in claim 1;
predicting performance of the entities under different process conditions in claims 14 and 18;
generating viability predictions by analyzing the predicted performance and process conditions in claims 14 and 18;
wherein the economic data includes market data in claims 14 and 18;
prioritizing development of entities based on the predicted performance and the viability predictions in claims 14 and 18;
When viewing these claim limitations under the Broadest Reasonable Interpretation, these claim limitations can be performed in the human mind through the use of observations, evaluations, judgements and opinion and thus fall under the mental process grouping under Step 2A, Prong One.
These judicial exceptions are not integrated into a practical application under Step 2A, Prong Two because the additional claim elements of:
one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions in claim 1 are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic memory to do generic computer functions) such that it does not amount to a particular machine.
a data collection system configured to collect performance data for a plurality of entities and market data comprising costs for development inputs in claim 1 amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., collecting data of entities and market data).
using one or more machine learning models trained on economic data in claim 1 recite only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model and/or configuration used and the training and/or finetuning steps used by the model on the economic data.
collecting performance data for a plurality of entities and market data comprising costs for development inputs in claims 14 and 18 amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., collecting data of entities and market data).
using one or more machine learning models trained on economic data in claims 14 and 18 recite only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model and/or configuration used and the training and/or finetuning steps used by the model on the economic data.
One or more non-transitory computer storage media storing instructions that when executed by one or more computers in claim 18 are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic memory to do generic computer functions) such that it does not amount to a particular machine.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as stated above the additional claim elements of:
one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions in claim 1 are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic memory to do generic computer functions) such that it does not amount to a particular machine.
a data collection system configured to collect performance data for a plurality of entities and market data comprising costs for development inputs in claim 1 are well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
using one or more machine learning models trained on economic data in claim 1 only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
collecting performance data for a plurality of entities and market data comprising costs for development inputs in claims 14 and 18 are well-understood, routine, conventional activity that court decisions, such as Symantec and buySAFE cited in MPEP 2106.05(d)(II) have indicated that the mere receiving and/or sending of data over a network using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
using one or more machine learning models trained on economic data in claims 14 and 18 only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
One or more non-transitory computer storage media storing instructions that when executed by one or more computers in claim 18 are recited at a high-level of generality using generic computer components (i.e., using a generic computer and generic memory to do generic computer functions) such that it does not amount to a particular machine.
Dependent claims 2-13, 15-17, and 19-20 are directed to statutory subject matter under Step 1, but when viewed under the Broadest Reasonable Interpretation, do not contain additional claim limitations that transform the judicial exception into a practical application under Step 2A, Prong Two because the claim elements of:
one or more machine learning models comprise one or more of a convolutional
neural network, a long-short term memory (LSTM), and a transformer neural network in claim 6 recite only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the training and/or finetuning steps used by the model.
And do not include additional elements that amount to significantly more than the judicial exception under Step 2B because as recited above, the claim elements of:
one or more machine learning models comprise one or more of a convolutional neural network, a long-short term memory (LSTM), and a transformer neural network in claim 6 only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
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.
Claims 1-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Long, Bin, et al. "Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity." Nature Communications 13.1 (2022)(“Long”) in view of O’Riordan, Keelan, et al. "Novel Bioreactor System for Economic Scale-Out." (April, 29th 2024)(“Keelan”).
Regarding claim 1, Long teaches a machine learning system comprising:
a data collection system configured to collect performance data for a plurality of entities and market data comprising costs for development inputs(Long, pgs., 6-8, see also figs., 5 and 6, “Inspired by the high productivity from the indoor pond system, we further tested biomass productivity of the pond SAC in real outdoor conditions. The outdoor tests were carried out in late September 2021 in College Station, Texas, with both ‘partially sunny’ and ‘mostly sunny’ weather. These conditions represent a typical fall growth condition. The outdoor cultivation achieved an average biomass productivity of 43.3 g/
m
2
/
d[a data collection system configured to collect performance data for a plurality of entities]... [r]ecent efforts to quantify the economic potential of algal biomass production by the National Renewable Energy Laboratory (NREL) examined different existing, well-documented PBR and pond designs across a number of different configurations. Both studies focused on estimating the break-even minimum biomass selling price (MBSP), given an internal rate of return on capital of 10%... the
implementation of ABS in SAC would also markedly reduce operating costs[ and market data comprising costs for development inputs].”);
a development system configured to predict performance of the entities under different process conditions(Long, pgs., 2-3, see also figs. 1 and 2, “Light intensity and cell concentration, the two major factors determining LDPs [light distribution pattern], were set as features and their corresponding LDPs were set as labels in training. We chose the support vector regression (SVR) algorithm to train due to its versatility...resulting in an LDP prediction model... [w]e named this second machine learning model a growth rate prediction model (GRM). The overall workflow for GRM training is shown in Fig. 2b. Vectors extracted from LDPs and their corresponding growth rates (based on the same time points) were set as features and labels in the training[a development system configured to predict performance of the entities under different process conditions]. ”);
and a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions using one or more machine learning models trained on economic data, wherein the economic data includes market data(Long, pgs., 6-8, see also fig. 6, “We further validated the potential of SAC with a 30-litre raceway pond system. We first adapted the machine learning models (LDPM and GRM) for a pond system to guide the cultivation design[and a machine learning analysis system configured to: generate viability predictions by analyzing the predicted performance and process conditions]... [t]he machine learning-informed SAC holds significant economic potential after being scaled up. Recent efforts to quantify the economic potential of algal biomass production by the National Renewable Energy Laboratory (NREL) examined different existing, well-documented PBR and pond designs across a number of different configurations... [b]ased on the NREL study, the yearly average of biomass productivity is estimated to be the productivities achieved in the Spring (MAR, APR, MAY) and Fall (SEP, OCT, NOV)... [a]t these conditions, the NREL model projects a MBSP of approximately $281 per ton based on the outdoor trial yield[using one or more machine learning models trained on economic data, wherein the economic data includes market data]....”),
and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions(Long, pgs., 6-8, see also fig. 6, “Following that approach, we estimated the yearly average of biomass productivity for the open pond system to be 43.3 g/
m
2
/d in the outdoor study and 48.1 g/
m
2
/d (83.3% of summer productivity) in the indoor mimicking trial[and wherein the development system is further configured to prioritize development of entities based on the predicted performance and the viability predictions].”).
Long does not teach: one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions.
However, Keelan teaches:
one or more computers; and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions(Keelan, pg., 8, “To validate these assumptions, we conducted a Techno-Economic Analysis using SuperPro Designer software.”)1
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 teachings of Long with the teachings of Keelan the motivation to do so would be to provide techno-economic analysis for bioreactors that cultivate the collected data of Long to determine viability of a given bio-product based on costs associated with bioreactors(Keelan, pgs., 3-4 “The bioproduction industry encompasses the production of any biological-based drug or chemical... [l]ow-value products are generally cheap and easy to make but require larger production amounts to be profitable. High-value products, conversely, are very expensive and hard to make...[b]ioreactors are the backbone of the bioproduction industry. At their most basic level, bioreactors are machines designed to grow cells (eukaryotic and prokaryotic) under very specific conditions...[s]caling of biomanufacturing and bioprocessing is required to meet cost parity.”).
Regarding claim 2, Long in view of Keelan teaches the system of claim 1, wherein prioritizing development comprises: generating risk-adjusted economic predictions for each entity; generating rankings of entities based on probability of commercial success; and adjusting development resource allocation based on the rankings(Long, pg., 4-6, see also figs. 3 and 4 and table 1, “Despite higher biomass productivity using optimal initial cell concentrations in SAC, the growth rate of UTEX 2973 was less than previously reported... [d]espite the potential of SAC, its feasibility depends heavily on cost-effective harvesting, a major challenge in algal biofuel[generating risk-adjusted economic predictions for each entity]... we overexpressed a limonene synthase in UTEX 2973 to produce limonene, a strong hydrophobic terpene that can be excreted from cyanobacterial cells. The strain was named L524[generating rankings of entities based on probability of commercial success]... [t]o investigate if limonene-induced aggregation could enable efficient UTEX 2973 cell sedimentation, we monitored the Aggregation-Based Sedimentation (ABS) process of L524 cells... [l]imonene production by L524 from SAC surpassed previously reported yields as shown in Table 1[and adjusting development resource allocation based on the rankings].”).
Regarding claim 3, Long in view of Keelan teaches the system of claim 1, wherein the market data further comprises one or more of feedstock costs, energy costs, labor costs, capital costs, equipment costs, or product market prices(Long, pg., 8, “[T]he limonene produced by L524 has a current market value of about $5/kg[product market prices]... [a]t this price, the SAC system proposed here would generate approximately $10.08 of additional revenue in limonene sales per ton of biomass produced...the implementation of ABS in SAC would also markedly reduce operating costs. ABS...could save up to 93% on energy costs compared to traditional harvesting methods[energy costs]....”).2
Regarding claim 4, Long in view of Keelan teaches the system of claim 1, wherein the system is further configured to: identify thresholds for viability; monitor performance data with respect to the thresholds; and automatically adjust development priorities when the performance data indicates a particular threshold will not be met(Long, pg., 6, see also fig. 5, “We evaluated L524 limonene and biomass productivities/yields in SAC compared to batch and fed-batch cultivations. In batch cultivation, L524 produced 11.2 mg/L limonene and 3.7 g/L biomass in 7 days (Fig. 5b, c). The limonene and biomass accumulations drastically slowed after day 2, indicating growth limitations caused by nutrient depletion (Fig. 5b, c). The limonene and biomass yields increased to 25.8 mg/L and 6.9 g/L, respectively, in 7 days with fed-batch cultivation, which removed the nutrient limitation (Fig. 5b, c). Despite the significant increases, limonene and biomass productivities still gradually decreased over time, suggesting that mutual shading became a limiting factor at high cell concentration (Fig. 5b–d)[ identify thresholds for viability; monitor performance data with respect to the thresholds]...by overcoming mutual shading, the SAC sustained near-linear limonene and biomass accumulations of ~5 mg/L/day of limonene and 2.2 g/L/day of biomass (Fig. 5b, c). The sustained high productivity resulted in
50.0 mg/L of limonene and 23.4 g/L of biomass over 11 days (Fig. 5b, c)[ and automatically adjust development priorities when the performance data indicates a particular threshold will not be met].”).
Regarding claim 5, Long in view of Keelan teaches the system of claim 1, wherein the development system generates the viability predictions for a plurality of parallel development paths for multiple entities, wherein the development system is configured to dynamically allocate development resources between the parallel development paths based on comparing the viability predictions(Long, pgs., 3-4, see also fig. 2, “Empowered by growth prediction, we propose a type of algal cultivation system where cells are removed periodically or continuously to maintain the cultivation with near optimal light availability and growth rates[wherein the development system generates the viability predictions for a plurality of parallel development paths for multiple entities]. The continuous or semicontinuous cultivation systems could minimize the impact of mutual shading and improve growth potential for cyanobacteria. As a demonstration, we simplified the SAC system with a harvesting interval of 24 h and used machine learning-based growth simulations to predict the best initial inoculum concentration. We evaluated biomass productivity predictions from different initial cell concentrations under low light...high light...and changing light... [i]n order to further improve biomass productivity, we optimized light conditions with double light sources...on opposite sides of PBRs[wherein the development system is configured to dynamically allocate development resources between the parallel development paths based on comparing the viability predictions].”).
Regarding claim 6, Long in view of Keelan teaches the system of claim 1, wherein the one or more machine learning models comprise one or more of a convolutional neural network, a long-short term memory (LSTM), and a transformer neural network(Keelan, pg., 5, “[W]e use an LSTM (Long Short-Term Memory) with a Bayesian fully connected layer (making it robust to outliers)[ a long-short term memory (LSTM)]....”).3,4
Regarding claim 7, Long in view of Keelan teaches the system of claim 1, wherein the performance data comprises one or more of yield data, titer data, productivity data, stability data, or growth rate data(Long, pgs., 3-4, see also fig. 2, “Empowered by growth prediction, we propose a type of algal cultivation system where cells are removed periodically or continuously to maintain the cultivation with near optimal light availability and growth rates[growth rate data].”).5
Regarding claim 8, Long in view of Keelan teaches the system of claim 1, wherein the process conditions comprise one or more of temperature, pH, nutrient concentrations, dissolved oxygen levels, mixing speed, gas flow rates, or nutrient feeding rates(Long, pg., 2, “The strong correlation between light pattern and growth rates suggests that light availability is the primary factor determining cyanobacterial growth rates when nutrients are sufficient and temperature is controlled[nutrient concentrations; temperature].”).6
Regarding claim 9, Long in view of Keelan teaches the system of claim 1, wherein the economic data further comprises production data indicating relationships between production factors and economic outcomes(Long, pg., 8, “[T]he implementation of ABS in SAC would also markedly reduce operating costs. ABS...could save up to 93% on energy costs compared to traditional harvesting methods...while maintaining high efficiency and recovery rates. As the dewatering process accounts for $24.4 per ton of biomass in the current model...the simplified harvest by ABS would further significantly reduce the MBSP[production data indicating relationships between production factors and economic outcomes]....”).
Regarding claim 10, Long in view of Keelan teaches the system of claim 1, wherein the system is further configured to simulate scale-up costs for different production scenarios(Keelan, pgs., 8-9, “Based on these estimates, we moved forward to test our
scale-out concept. We hypothesized that instead of a larger bioreactor to mass produce a biological product, multiple smaller-sized bioreactors could be coupled together to form
a cascade system. This configuration would reduce the capital cost involved with building an industrial-scale bioreactor[simulate scale-up costs for different production scenarios].”).7
Regarding claim 11, Long in view of Keelan teaches the system of claim 1, wherein the system is further configured to predict market-dependent revenue potential(Long, pgs., 8-9, “[D]ue to the high glycogen content of UTEX 2973 cells, the cyanobacterial biomass could directly feed into biorefineries for ethanol fermentation without pretreatment[predict market-dependent revenue potential]....”).
Regarding claim 13, Long in view of Keelan teaches the system of claim 1, wherein the data collection system continuously collects the performance data and the market data, and wherein the system continuously updates the viability predictions during development of the entities(Long, pgs., 3-4, see also fig. 2, “Empowered by growth prediction, we propose a type of algal cultivation system where cells are removed periodically or continuously to maintain the cultivation with near optimal light availability and growth rates. The continuous or semicontinuous cultivation systems could minimize the impact of mutual shading and improve growth potential for cyanobacteria. As a demonstration, we simplified the SAC system with a harvesting interval of 24 h and used machine learning-based growth simulations to predict the best initial inoculum concentration. We evaluated biomass productivity predictions from different initial cell concentrations under low light...high light...and changing light... [i]n order to further improve biomass productivity, we optimized light conditions with double light sources...on opposite sides of PBRs).
Regarding claim 14, Long teaches a method the method comprising:
collecting performance data for a plurality of entities and market data comprising costs for development inputs(Long, pgs., 6-8, see also figs., 5 and 6, “Inspired by the high productivity from the indoor pond system, we further tested biomass productivity of the pond SAC in real outdoor conditions. The outdoor tests were carried out in late September 2021 in College Station, Texas, with both ‘partially sunny’ and ‘mostly sunny’ weather. These conditions represent a typical fall growth condition. The outdoor cultivation achieved an average biomass productivity of 43.3 g/
m
2
/
d[collect performance data for a plurality of entities]... [r]ecent efforts to quantify the economic potential of algal biomass production by the National Renewable Energy Laboratory (NREL) examined different existing, well-documented PBR and pond designs across a number of different configurations. Both studies focused on estimating the break-even minimum biomass selling price (MBSP), given an internal rate of return on capital of 10%... the implementation of ABS in SAC would also markedly reduce operating costs[ and market data comprising costs for development inputs].”);
predicting performance of the entities under different process conditions(Long, pgs., 2-3, see also figs. 1 and 2, “Light intensity and cell concentration, the two major factors determining LDPs [light distribution pattern], were set as features and their corresponding LDPs were set as labels in training. We chose the support vector regression (SVR) algorithm to train due to its versatility...resulting in an LDP prediction model... [w]e named this second machine learning model a growth rate prediction model (GRM). The overall workflow for GRM training is shown in Fig. 2b. Vectors extracted from LDPs and their corresponding growth rates (based on the same time points) were set as features and labels in the training[predict performance of the entities under different process conditions]. ”);
and generating viability predictions by analyzing the predicted performance and process conditions using one or more machine learning models trained on economic data, wherein the economic data includes market data(Long, pgs., 6-8, see also fig. 6, “We further validated the potential of SAC with a 30-litre raceway pond system. We first adapted the machine learning models (LDPM and GRM) for a pond system to guide the cultivation design[generate viability predictions by analyzing the predicted performance and process conditions]... [t]he machine learning-informed SAC holds significant economic potential after being scaled up. Recent efforts to quantify the economic potential of algal biomass production by the National Renewable Energy Laboratory (NREL) examined different existing, well-documented PBR and pond designs across a number of different configurations... [b]ased on the NREL study, the yearly average of biomass productivity is estimated to be the productivities achieved in the Spring (MAR, APR, MAY) and Fall (SEP, OCT, NOV)... [a]t these conditions, the NREL model projects a MBSP of approximately $281 per ton based on the outdoor trial yield[using one or more machine learning models trained on economic data, wherein the economic data includes market data]....”);
prioritizing development of entities based on the predicted performance and the viability predictions(Long, pgs., 6-8, see also fig. 6, “Following that approach, we estimated the yearly average of biomass productivity for the open pond system to be 43.3 g/
m
2
/d in the outdoor study and 48.1 g/
m
2
/d (83.3% of summer productivity) in the indoor mimicking trial[prioritize development of entities based on the predicted performance and the viability predictions].”).
Long does not teach: performed by one or more computers.
However, Keelan teaches:
performed by one or more computers (Keelan, pg., 8, “To validate these assumptions, we conducted a Techno-Economic Analysis using SuperPro Designer software.”)8
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 teachings of Long with the teachings of Keelan the motivation to do so would be to provide techno-economic analysis for bioreactors that cultivate the collected data of Long to determine viability of a given bio-product based on costs associated with bioreactors(Keelan, pgs., 3-4 “The bioproduction industry encompasses the production of any biological-based drug or chemical... [l]ow-value products are generally cheap and easy to make but require larger production amounts to be profitable. High-value products, conversely, are very expensive and hard to make...[b]ioreactors are the backbone of the bioproduction industry. At their most basic level, bioreactors are machines designed to grow cells (eukaryotic and prokaryotic) under very specific conditions...[s]caling of biomanufacturing and bioprocessing is required to meet cost parity.”).
Referring to dependent claims 15-17, they are rejected on the same basis as
dependent claims 2-4 since they are analogous claims.
Regarding claim 18, Keelan teaches one or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations(Keelan, pg., 8, “To validate these assumptions, we conducted a Techno-Economic Analysis using SuperPro Designer software.”)9 and for all other claim limitations they are rejected on the same basis as independent claim 14 since they are analogous claims.
Referring to dependent claims 19-20, they are rejected on the same basis as
dependent claims 2-3 since they are analogous claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. US 20230094690 A1(details incorporating economic objectives with respect to AI and medical decision making)
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Because SuperPro Designer is software that requires a computer for execution it is inherent
that a computer contains a processor and a memory within.
2 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
3 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
4 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 teachings of Long with the above teachings of Keelan for the same rationale stated at Claim 1.
5 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
6 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
7 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 teachings of Long with the above teachings of Keelan for the same rationale stated at Claim 1.
8 Because SuperPro Designer is software that requires a computer for execution it is inherent
that a computer contains a processor and a memory within.
9 Because SuperPro Designer is software that requires a computer for execution it is inherent
that a computer contains a processor and a memory within.
Prosecution Insights