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 6/3/2022 and 11/06/2022 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Status of Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Drawings
The Drawings filed on 6/03/2022 and 6/08/22 were considered.
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 7 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.
The term “knowledge base” in claim 7 is a relative term which renders the claim indefinite. The term “knowledge base” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-8 are directed to a method for photosynthesis optimization. Claims 9-14 are directed to a computer program, on a non-transitory storage medium, for photosynthesis optimization. Claims 15-20 are directed to a system for photosynthesis optimization.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
selecting a catalyst to perform an artificial photosynthesis reaction at the location (mental process)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (mental process, mathematical process)
Dependent claim 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging (mathematical process)
Dependent claim 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight (mental process)
Dependent claim 4 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the at least one gas includes carbon dioxide (mathematical process)
Under the broadest reasonable interpretation, the level of carbon dioxide can be determined by machine learning based on at least one of a weather forecast, previously recorded ambient levels of carbon dioxide, water, and sunlight, and satellite imaging
Dependent claim 5 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
performing a cost-benefit analysis of compensating for the at least one limiting factor (mental process, mathematical process)
Dependent claim 7 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the catalyst is a photocatalyst, bio-electrochemical catalyst, or a photochemical catalyst. (mental process)
selecting the catalyst is based on a humidity, irradiance, and carbon dioxide sensitivity profile and knowledge base (mental process)
Dependent claim 8 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
Generating a three-dimensional lookup table for the catalyst based on the humidity, irradiance, and carbon dioxide sensitivity profile (mathematical process)
Independent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
selecting a catalyst to perform an artificial photosynthesis reaction at the location (mental process)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst (mental process, mathematical process)
Dependent claim 10 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging (mathematical process)
Dependent claim 11 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight (mental process)
Dependent claim 12 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the at least one gas includes carbon dioxide
Under the broadest reasonable interpretation, the level of carbon dioxide can be determined by machine learning based on at least one of a weather forecast, previously recorded ambient levels of carbon dioxide, water, and sunlight, and satellite imaging
Dependent claim 13 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
performing a cost-benefit analysis of compensating for the at least one limiting factor (mental process, mathematical process)
Independent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
selecting a catalyst to perform an artificial photosynthesis reaction at the location (mental process)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst (mental process, mathematical process)
Dependent claim 16 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging (mathematical process)
Dependent claim 17 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight (mental process)
Dependent claim 18 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
wherein the at least one gas includes carbon dioxide
Under the broadest reasonable interpretation, the level of carbon dioxide can be determined by machine learning based on at least one of a weather forecast, previously recorded ambient levels of carbon dioxide, water, and sunlight, and satellite imaging
Dependent claim 19 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
performing a cost-benefit analysis of compensating for the at least one limiting factor (mental process, mathematical process)
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. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. 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.
Therefore, claims 1-20 recite an abstract idea as the dependent claims will inherit the abstract ideas from the independent claims.
[Step 2A Prong One: YES]
Eligibility 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); MPEP 2106.05(a-h)). 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 judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
The additional element in independent claim 1 includes:
determining ambient levels of at least one gas, water, and sunlight at a location
compensating for the at least one limiting factor
The additional element in dependent claim 4 includes:
wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location.
The additional element in dependent claim 5 includes:
implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis
The additional element in dependent claim 6 includes:
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller
The additional element in independent claim 9 includes:
determining ambient levels of at least one gas, water, and sunlight at a location
compensating for the at least one limiting factor.
The additional element in dependent claim 12 includes:
wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location
The additional element in dependent claim 13 includes:
implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis
The additional element in dependent claim 14 includes:
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller
The additional element in independent claim 15 includes:
determining ambient levels of at least one gas, water, and sunlight at a location
compensating for the at least one limiting factor
The additional element in dependent claim 18 includes:
wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location.
The additional element in dependent claim 19 includes:
implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis.
The additional element in dependent claim 20 includes:
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller.
The additional elements of, compensating for the at least one limiting factor (claims 1, 9, 15) and
implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis (claim 5, 13, 19) are insignificant extra-solution activity that is well known see MPEP 2106.05(g). It also merely invokes a computer as a tool and does not improve the technology of a generic computer (see MPEP 2106.05(a)). The additional element of wherein the ambient levels of carbon dioxide is determined using at least one of satellite imaging and carbon sequestration at the location (claims 4, 12, 18) and determining ambient levels of at least one gas, water, and sunlight at a location claims (1, 9, 15) are an insignificant extra-solution activity that is part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)). The additional element of wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller (claims 6, 14, 20)
merely invokes a computer as a tool and does not improve the technology of a generic computer (see MPEP 2106.05(a) and is an insignificant extra-solution activity that is well known see MPEP 2106.05(g).
Claims 2-3, 7-8, 10-11, 16-17 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Thus, the additionally recited elements merely invoke a computer as a tool, are insignificant extra-solution activity, and/or amount to data gathering activity, and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea
into a practical application, the claims are probed for a specific inventive concept. The judicial exception
alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying
whether the additional elements beyond the abstract idea amount to such an inventive concept
requires considering the additional elements individually and in combination to determine if they
amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly
more than the judicial exception(s) because of the reasons noted below.
Claims 2-3, 7-8, 10-11, 16-17 do not recite any elements in addition to the judicial exception. The additional elements in claims 1,4-6, 9, 12-15, 18-20 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of, compensating for the at least one limiting factor (claims 1, 9, 15) and implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis (claim 5, 13, 19) are conventional. Evidence for conventionality is shown by Li et al. (T. Li, E. Heuvelink, T. A. Dueck, J. Janse, G. Gort, L. F. M. Marcelis, Enhancement of crop photosynthesis by diffuse light: quantifying the contributing factors, Annals of Botany, Volume 114, Issue 1, July 2014, Pages 145–156) and Xin et al. (Xin, P., Li, B., Zhang, H. et al. Optimization and control of the light environment for greenhouse crop production. Sci Rep 9, 8650 (2019)). Li et al. and Xin et al. both teach improving photosynthesis by adjusting factors such as carbon dioxide concentration or light.
The additional element of wherein the ambient levels of carbon dioxide is determined using at least one of satellite imaging and carbon sequestration at the location (claims 4, 12, 18) and determining ambient levels of at least one gas, water, and sunlight at a location (claims 1, 9, 15) are conventional. Evidence for conventionality is shown by Streets et al. (Streets, D. G.; Canty, T.; Carmichael, G. R.; de Foy, B.; Dickerson, R. R.; Duncan, B. N.; Edwards, D. P.; Haynes, J. A.; Henze, D. K.; Houyoux, M. R.; Jacob, D. J.; Krotkov, N. A.; Lamsal, L. N.; Liu, Y.; Lu, Z.; Martin, R. V.; Pfister, G. G.; Pinder, R. W.; Salawitch, R. J.; Wecht, K. J. Emissions Estimation from Satellite Retrievals: A Review of Current Capability. Atmospheric Environment 2013, 77, 1011–1042.) Streets et al. is a review of different methods of detecting different gas concentrations (including carbon dioxide) using satellites.
The additional element of wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller (claims 6, 14, 20) merely invokes a computer as a tool and does not improve the technology of a generic computer (see MPEP 2106.05(a)). It is also conventional. Evidence for conventionality is shown by Gomes et al. (Gomes, J.B.A.; Rodrigues, J.J.P.C.; Rabêlo, R.A.L.; Kumar, N.; Kozlov, S. IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities. J. Sens. Actuator Netw. 2019, 8, 57). Gomes et al. is a review on IoT-enabled gas sensors.
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 1,9, 15 are rejected under 35 U.S.C. 103 as being unpatentable by Miller at al. (Jackson Miller et al., ResearchGate, Greenhouse, AI-Powered Smart Greenhouses for Climate Control and Crop Growth Optimization, 10/19/2021) in view of Zhang et al. (Zhang, B.; Sun, L. Artificial Photosynthesis: Opportunities and Challenges of Molecular Catalysts. Chemical Society Reviews 2019, 48 (7), 2216–2264.)
Regarding the limitations of independent claim 1,
A method for photosynthesis optimization, the method comprising: determining ambient levels of at least one gas, water, and sunlight at a location;
Miller at al. teaches temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates. AI-based irrigation management leads to more efficient water usage by continuously analyzing soil moisture levels and predicting crop water requirements. The results indicate a reduction in water consumption compared to conventional irrigation methods, contributing to sustainable agricultural practices (Results and discussion 2nd and 3rd paragraphs pg. 3)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels;
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
and compensating for the at least one limiting factor
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3).
Regarding the limitations of independent claim 9,
A computer program product for photosynthesis optimization, the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method, the method comprising
Miller at al. teaches temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates. AI-based irrigation management leads to more efficient water usage by continuously analyzing soil moisture levels and predicting crop water requirements. The results indicate a reduction in water consumption compared to conventional irrigation methods, contributing to sustainable agricultural practices (Results and discussion 2nd and 3rd paragraphs pg. 3)
determining ambient levels of at least one gas, water, and sunlight at a location
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
compensating for the at least one limiting factor.
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
Regarding the limitations of independent claim 15,
A computer system for photosynthesis optimization, the system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method, the method comprising
Miller at al. teaches temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates. AI-based irrigation management leads to more efficient water usage by continuously analyzing soil moisture levels and predicting crop water requirements. The results indicate a reduction in water consumption compared to conventional irrigation methods, contributing to sustainable agricultural practices (Results and discussion 2nd and 3rd paragraphs pg. 3)
determining ambient levels of at least one gas, water, and sunlight at a location;
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels;
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
compensating for the at least one limiting factor.
Miller at al. teaches the integration of AI-powered computer vision systems improves crop monitoring by detecting early signs of disease or nutrient deficiencies. Deep learning models trained on image datasets successfully identify plant stress indicators, allowing for timely interventions. This capability reduces the need for chemical pesticides and fertilizers, promoting environmentally friendly farming practices. One of the key advantages of AI-powered greenhouses is their ability to adapt to varying climate conditions. The machine learning models continuously learn from historical and real-time data, refining their predictions and improving system performance over time. This adaptability makes AI-powered greenhouses more resilient to climate change, ensuring stable food production even in unpredictable environmental conditions. (Results and discussion 3rd and 4th paragraphs pg. 3)
Miller at al. does not explicitly teach;
selecting a catalyst to perform an artificial photosynthesis reaction at the location (claim 1, claim 9, claim 15)
Regarding the limitations of independent claim 1,
selecting a catalyst to perform an artificial photosynthesis reaction at the location (claim 1)
Zhang et al. teaches an artificial photosynthesis catalyst of Fe complexes 1, 2, and 3 are well-known examples of Fe-based molecular catalysts for water oxidation, hydrogen evolution, and CO2 reduction. The metal centers of 1, 2, and 3 are all iron; however, these complexes can catalyze different reactions because the electronic structures of the iron cores are precisely adjusted by the particular coordination environments created by the ligands. (3.1 Advantages of molecular catalysts, pg. 2219, column 1) A person of ordinary skill in the art would know to select the catalyst based on the knowledge of it.
Regarding the limitations of independent claim 9,
selecting a catalyst to perform an artificial photosynthesis reaction at the location
Zhang et al. teaches an artificial photosynthesis catalyst of Fe complexes 1, 2, and 3 are well-known examples of Fe-based molecular catalysts for water oxidation, hydrogen evolution, and CO2 reduction. The metal centers of 1, 2, and 3 are all iron; however, these complexes can catalyze different reactions because the electronic structures of the iron cores are precisely adjusted by the particular coordination environments created by the ligands. (3.1 Advantages of molecular catalysts, pg. 2219, column 1) A person of ordinary skill in the art would know to select the catalyst based on the knowledge of it.
Regarding the limitations of independent claim 15,
selecting a catalyst to perform an artificial photosynthesis reaction at the location;
Zhang et al. teaches an artificial photosynthesis catalyst of Fe complexes 1, 2, and 3 are well-known examples of Fe-based molecular catalysts for water oxidation, hydrogen evolution, and CO2 reduction. The metal centers of 1, 2, and 3 are all iron; however, these complexes can catalyze different reactions because the electronic structures of the iron cores are precisely adjusted by the particular coordination environments created by the ligands. (3.1 Advantages of molecular catalysts, pg. 2219, column 1) A person of ordinary skill in the art would know to select the catalyst based on the knowledge of it.
A person of ordinary skill in the art would be motivated to combine the method of photosynthesis optimization of Miller et al. with the artificial catalyst of Zhang et al. because the optimization of photosynthesis is a known technique therefore it would be obvious to apply it to an artificial photosynthesis catalysts. As both seek to optimize photosynthesis. A person of ordinary skill in the art would understand that the techniques used to optimize photosynthesis could be used to optimize artificial photosynthesis
Claims 2,4,10, 12,16,18 are rejected under 35 U.S.C. 103 as being unpatentable by Miller at al. in view of Zhang et al. as applied to claims 1,9, 15 above, and further in view of Sanga-Ngoie et al. (Sanga-Ngoie, K et al. Estimating CO2 Sequestration by Forests in Oita Prefecture, Japan, by Combining LANDSAT ETM+ and ALOS Satellite Remote Sensing Data. Remote Sensing 2012, 4 (11), 3544–3570.)
As applied to independent claims 1 (detailed above), by Miller at al. in view of Zhang et al. teaches a method for artificial photosynthesis optimization.
Miller at al. in view of Zhang et al. does not explicitly teach:
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging (Claims 2, 10, 16)
wherein the at least one gas includes carbon dioxide, and wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location. (Claims 4, 12, 18)
Regarding the limitations of dependent claim 2,
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging.
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
Regarding the limitations of dependent claim 10,
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging.
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
Regarding the limitations of dependent claim 16,
wherein the determining ambient levels of the at least one gas, water, and sunlight at the location is performed by machine learning based on at least one of a weather forecast, previously recorded ambient levels of the at least one gas, water, and sunlight, and satellite imaging.
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
Regarding the limitations of dependent claim 4,
wherein the at least one gas includes carbon dioxide, and wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location.
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
Regarding the limitations of dependent claim 12,
wherein the at least one gas includes carbon dioxide wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
Regarding the limitations of dependent claim 18,
wherein the at least one gas includes carbon dioxide, and wherein the ambient levels of carbon dioxide are determined using at least one of satellite imaging and carbon sequestration at the location.
Sanga-Ngoie et al. teaches that traditional methods of estimating carbon/CO2 sequestration through site-based methods or using eddy covariance flux tower are known to be expensive, time consuming and limited in area coverage. Expanding the area or implementing continuous monitoring is even more costly and time consuming. Remotely sensed satellite observations have provided the scientists with an alternative method for studying the earth’s biosphere (atmosphere, vegetation, etc.). As for the vegetation, it has been demonstrated that the reflected RGB (red, green, blue), and mostly the NIR (near infrared), wavelengths contain considerable information about plants biomass, from which precise knowledge about the vegetation can be extracted. This gives us the possibility of biomass monitoring at lower costs and with less time loss. Today, with further research advancement, remote sensing is becoming a common analysis tool, not only for producing maps needed for categorizing the land cover type of the surface and for allocating or managing the earth’s resources, but also for analyzing the changes and their impacts for future land use/land cover (LU/LC) developments. Using remote sensing data for evaluating regional carbon/CO2 sequestration has been implemented in various ways, among which is combining the land cover information with the averaged carbon sequestration values of different land cover types. The strength of using land cover information based on remotely sensed images is that it covers areas of regional or even global scales, making it possible to extend analysis over sites that are difficult to access on the ground. Moreover, it allows the implementation of continuous monitoring of the area, making it possible to analyze the temporal changes of the land cover. (Introduction, par 3-4, pg. 3546)
It would be obvious to combine the method of artificial photosynthesis optimization taught by Miller at al. in view of Zhang et al. with the method to identify carbon dioxide levels from Sanga-Ngoie et al. because carbon dioxide levels are used to optimize artificial photosynthesis therefore a person of ordinary skill in the art would use the known technique of Sanga-Ngoie et al. to gather carbon dioxide levels for the method of artificial photosynthesis optimization.
Claims 3, 11, 17 are rejected under 35 U.S.C. 103 as being unpatentable by Miller at al. in view of Zhang et al. as applied to claims 1,9, 15 above, and further in view of Fan et al. (arxiv, AGNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction, Fan et al. 1/21/2022)
As applied to independent claims 1 (detailed above), by Miller at al. in view of Zhang et al. teaches a method for artificial photosynthesis optimization.
Miller at al. in view of Zhang et al. does not explicitly teach:
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight. (Claims 3, 11, 17)
Regarding the limitations of dependent claim 3,
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight.
Fan et al. teaches a GNN-RNN framework to innovatively incorporate both geospatial and temporal knowledge into crop yield prediction, through graph-based deep learning methods. A person of ordinary skill in the art would chose an area with maximal crop yields which is equivalent to maximizing photosynthesis.
Regarding the limitations of dependent claim 11,
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight.
Fan et al. teaches a GNN-RNN framework to innovatively incorporate both geospatial and temporal knowledge into crop yield prediction, through graph-based deep learning methods. A person of ordinary skill in the art would choose an area with maximal crop yields which is equivalent to maximizing photosynthesis.
Regarding the limitations of dependent claim 17,
wherein at least one of the location and the catalyst is chosen based on the ambient levels of the at least one gas, water, and sunlight.
Fan et al. teaches a GNN-RNN framework to innovatively incorporate both geospatial and temporal knowledge into crop yield prediction, through graph-based deep learning methods. A person of ordinary skill in the art would choose an area with maximal crop yields which is equivalent to maximizing photosynthesis.
It would be obvious to combine the method of artificial photosynthesis optimization taught by Miller at al. in view of Zhang et al. with the method to chose a location to maximize crop yields from Fan et al. because optimizing crop yields is equivalent to optimizing photosynthesis. Therefore, a person of ordinary skill in the art would use the method of Fan et al. to chose an ideal location for optimal artificial photosynthesis. As a person of ordinary skill in the art would use the same methods and techniques to maximize both artificial and natural photosynthesis.
Claims 5, 6, 13, 14, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable by Miller at al. in view of Zhang et al. as applied to claims 1,9, 15 above, and further in view of DeVincentis et al. (DeVincentis, A. J.; Solis, S. S.; Bruno, E. M.; Leavitt, A.; Gomes, A.; Rice, S.; Zaccaria, D. Using Cost-Benefit Analysis to Understand Adoption of Winter Cover Cropping in California’s Specialty Crop Systems. Journal of Environmental Management 2020, 261, 110205.)
As applied to independent claims 1 (detailed above), by Miller at al. in view of Zhang et al. teaches a method for artificial photosynthesis optimization.
Regarding the limitations of dependent claim 6,
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller.
Miller at al. teaches Temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates.
Regarding the limitations of dependent claim 14,
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller.
Miller at al. teaches Temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates.
Regarding the limitations of dependent claim 20,
wherein the compensatory option is performed by adjusting at least one of an IoT controlled solar irradiance mirror, an artificial light source, a gas inlet, and a humidity controller.
Miller at al. teaches Temperature and humidity regulation in AI-powered greenhouses is significantly more precise than in manually controlled setups. Machine learning algorithms predict temperature fluctuations and adjust heating and cooling systems accordingly, minimizing energy consumption. This predictive capability ensures that plants experience stable conditions, reducing stress and enhancing growth rates.
Miller at al. in view of Zhang et al. does not explicitly teach:
further comprising: performing a cost-benefit analysis of compensating for the at least one limiting factor; and implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis. (Claims 5, 13, 19)
Regarding the limitations of dependent claim 5,
further comprising: performing a cost-benefit analysis of compensating for the at least one limiting factor; and implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis.
DeVincentis et al. teaches a method that conducts a cost-benefit analysis of winter cover cropping for two specialty crops, processing tomatoes and almonds, which are widespread in California’s Central Valley, and provides insight into possible explanations for low adoption (introduction, pg. 2, par. 6, col. 1)
Regarding the limitations of dependent claim 13,
further comprising: performing a cost-benefit analysis of compensating for the at least one limiting factor; and implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis.
DeVincentis et al. teaches a method that conducts a cost-benefit analysis of winter cover cropping for two specialty crops, processing tomatoes and almonds, which are widespread in California’s Central Valley, and provides insight into possible explanations for low adoption (introduction, pg. 2, par. 6, col. 1)
Regarding the limitations of dependent claim 19,
further comprising: performing a cost-benefit analysis of compensating for the at least one limiting factor; and implementing a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis.
DeVincentis et al. teaches a method that conducts a cost-benefit analysis of winter cover cropping for two specialty crops, processing tomatoes and almonds, which are widespread in California’s Central Valley, and provides insight into possible explanations for low adoption (introduction, pg. 2, par. 6, col. 1)
It would be obvious to combine the method of artificial photosynthesis optimization taught by Miller at al. in view of Zhang et al. with the method to choose a location to maximize crop yields from DeVincentis et al. because optimizing crop yields is equivalent to optimizing photosynthesis. Therefore, a person of ordinary skill in the art would use the method of Fan et al. to perform cost benefit analysis when optimizing artificial photosynthesis. As a person of ordinary skill in the art would use the same methods and techniques to maximize both artificial and natural photosynthesis.
Conclusion
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/C.H.B./Examiner, Art Unit 1687 /Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687