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 in a previous office action.
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 in a previous office action.
Claim Objections
Claim 5 is objected to because of the following informalities: “analyis”. Examiner assumes it is a misspelling of “analysis.” Appropriate correction is required.
Response to Arguments 112(b)
Applicant sufficiently amended claim 7 and removed the indefinite term “knowledge base.” The 112(b) rejection is withdrawn for Claim 7.
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 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to ineligible computer readable medium.
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 9-14 are directed to a computer readable medium. This is statutory ineligible as under the BRI it can be a transitory computer readable medium. Applicant must amend to “non-transitory computer readable medium” in order to overcome the 101 rejections.
[Step 1: No]
Response to Arguments 101
Applicant sufficiently amended independent claims 1, 15 to overcome the rejection. The additional element of “responsive to determining the at least one limiting factor, diverting, by one or more processors, a first type of gas from the location to a second location, through a series of interconnected pipes, to compensate for the at least one limiting factor” integrates the abstract idea into a practical application by creating a system that will optimize artificial photosynthesis under step 2A. All dependent claims are also deemed to integrate an abstract idea in a practical application for the same reason. Applicant’s arguments regarding step 2B are moot as the claims are deemed allowable under 2A. The rejection of claims 1-8 and 15-20 under 101 is withdrawn. Claims 9-14 are still rejected as they are directed to are directed to a computer readable medium. This is statutory ineligible as under the BRI it can be a transitory computer readable medium. Applicant must amend to “non-transitory computer readable medium” in order to overcome the 101 rejections.
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, 3, 11, 17, 7 are rejected under 35 U.S.C. 103 as being unpatentable by Diego F. Lozano-Garcia et al. (Potential Land Microalgae Cultivation in Mexico: From Food Production to Biofuels. Algal Research 2019, 39, 101459.) in view of Huesemanna et al. (Huesemann et al. The Laboratory Environmental Algae Pond Simulator (LEAPS) Photobioreactor: Validation Using Outdoor Pond Cultures of Chlorella Sorokiniana and Nannochloropsis Salina. Algal Research 2017, 26, 39–46.) in view of Miller at al. (Jackson Miller et al., ResearchGate, Greenhouse, AI-Powered Smart Greenhouses for Climate Control and Crop Growth Optimization, 10/19/2021) The instant application claims are italicized.
With respect to the limitations of Claim 1, 9, 15, 3, 11, 17, Diego F. Lozano-Garcia et al. teaches
geographic information system model was built to identify the areas in Mexico with high potential for mi croalgae production. The input data was obtained from diverse national and international sources. The model was constructed using multiple criteria for facility siting, including the following land characteristics and resources: land use, topographic slope, temperature, evaporation, solar radiation, vegetation, water and CO2 sources, wastewater treatment plants, rivers and lakes, cities and roads, natural protected areas, historical sites, Ramsar sites, airport locations and geological faults. Various models were constructed by assigning weights according to suitability to the data layers. All 3 elements of CO2, water, and sunlight are used to optimize photosynthesis, although in the case gas is pumped in but once pumped in that changes the ambient level. Addition, its an obvious variant to look at CO2 levels at a location to choose it as CO2 is known to produce plant growth. (abstract, a computer implemented method comprising: determining, by one or more processors, ambient levels of gas, water, and sunlight at a location; (Claim 1), A computer program product comprising: one or more computer-readable storage media- and program instructions stored on the one or more computer-readable storage media to perform operations comprising: determining ambient levels of gas, water, and sunlight at a location (Claim 9), A computer system comprising: a processor set one or more computer-readable storage media and program instructions stored on the one or more of the computer-readable storage media to cause the processor set to perform operations comprising: determining ambient levels of gas, water, and sunlight at a location (Claim 15), The computer implemented method of claim 1, wherein the location and the catalyst are each chosen based on the ambient levels of the gas, water, and sunlight (Claim 3, 11, 17) Diego F. Lozano-Garcia also teaches carbon dioxide (CO2) is an integral component of the microalgae cultivation process and can be provided via pipeline from a point source location or trucked to the production site. The data set used to identify point sources of CO2 was extracted from CFE. A total of 1785 stationary sources of carbon dioxide are reported in Mexico, all of them were considered for the analysis and were classified according to type of industry as cement, glass or lime industries. A person of ordinary skill in the art would understand that CO2 is a critical limiting factor in photosynthesis optimization and one of ordinary skill would recognize CO2 as a limiting factor in algae growth and compensate by changing the ambient levels of CO2. (pg. 3, col. 2 paragraph 4, and determining, by one or more processors, at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 1) responsive to determining the at least one limiting factor, diverting, by one or more processors, a first type of gas from the location to a second location, through a series of interconnected pipes, to compensate for the at least one limiting factor (Claim 1), determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels; and responsive to determining the at least one limiting factor, diverting a first type of gas from the location to a second location, through a series of interconnected pipes, to compensate for the at least one limiting factor (Claim 9), determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels and responsive to determining the at least one limiting factor, diverting a first type of gas from the location to a second location, through a series of interconnected pipes, to compensate compensating for the at least one limiting factor (Claim 15))
Diego F. Lozano-Garcia et al. does not explicitly teach
selecting, by one or more processors, a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 1),
selecting a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 9),
selecting a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 15)
determining, by one or more processors, at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 1),
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 9),
determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 15))
wherein: the catalyst is selected from the group consisting of: a photocatalyst, bio-electrochemical catalyst, and a photochemical catalyst, and wherein the selecting the catalyst is based on a humidity, irradiance, and carbon dioxide sensitivity profile (Claim 7)
With respect to the limitations of Claim 1, 7, Huesemanna et al. teaches the Laboratory Environmental Algae Pond Simulator (LEAPS), was designed and constructed to simulate outdoor pond cultivation for a wide range of geographical locations and seasons. The LEAPS consists of six well-mixed glass column photobioreactors submerged in a temperature controlled waterbath (−2 °C to> 60 °C) and illuminated from above by a programmable multicolor LED lighting system (0 to 2500 μmol/m2-s). Measured incident light intensities and water temperatures deviated from the respective light and temperature setpoints on average only 2.3% and 0.9%, demonstrating accurate simulation of light and temperature conditions measured in outdoor ponds. When combined with Lozano-Garcia et al. provides the information on water, sunlight, and CO2 while LEAPS provides selecting the right Algae. Under the broadest reasonable interpretation Algae is a catalyst as it is a substance that speeds up a chemical reaction. When combining these two works you select the right catalyst(algae) and the right location. Additionally, anything used for photosynthesis would be applied to artificial photosynthesis. (abstract, selecting, by one or more processors, a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 1), selecting a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 9), selecting a catalyst to perform an artificial photosynthesis reaction at the location based on the ambient levels of gas, water, and sunlight at the location (Claim 15), wherein: the catalyst is selected from the group consisting of: a photocatalyst, bio-electrochemical catalyst, and a photochemical catalyst, and wherein the selecting the catalyst is based on a humidity, irradiance, and carbon dioxide sensitivity profile (Claim 7, algae is a bio-electric catalyst)
With respect to the limitations of Claim 1, 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. The principal of using AI to investigate limiting factors and applying to them can easily be applied to both Huesemanna et al. and Diego F. Lozano-Garcia et al. (Results and discussion 3rd and 4th paragraphs pg. 3, determining, by one or more processors, at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 1), determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 9), determining at least one limiting factor for the artificial photosynthesis reaction based on the catalyst and the ambient levels (Claim 15))
A person of ordinary skill in the art would be motivated to combine the method of site selection based on available resources of sunlight, water, gas, among other needed inputs taught by Diego F. Lozano-Garcia et al. Huesemanna et al. provides the method of selecting a bio-catalyst based on optimal conditions and photosynthesis optimization combined with intervention using machine learning of Miller et al. As all works 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. There is a reasonable expectation of success because each piece works independently and the individual steps are not fundamentally changed therefore it is expected to work when combined.
Claims 2,4,10, 12,16,18 are rejected under 35 U.S.C. 103 as being unpatentable Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. claims, 1, 9, 15, 3, 11, 17, 7 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.) in view of Voyantet al (Voyant, Cet al. Machine Learning Methods for Solar Radiation Forecasting: A Review. Renewable Energy 2017, 105, 569–582) in view of Ahmad et al. (Ahmad, S.; Kalra, A.; Stephen, H. Estimating Soil Moisture Using Remote Sensing Data: A Machine Learning Approach. Advances in Water Resources 2010, 33 (1), 69–80)
As applied to claims, 1, 9, 15, 3, 11, 17, 7 detailed above), Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. teaches a method for artificial photosynthesis optimization.
Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. does not explicitly teach:
wherein determining the ambient levels of the gas, water, and sunlight at the location is performed by machine learning based on a weather forecast, ii previously recorded ambient levels of the gas, water, and sunlight, and iii satellite imaging. (Claim 2, Claim 10, Claim 16)
wherein: the gas includes carbon dioxide and the ambient levels of carbon dioxide are determined using a selection from the group consisting of satellite imaging and carbon sequestration at the location. (Claims 4, Claim 12, Claim 18)
With respect to the limitations of Claim 2, 10, 16, 4, 12, 18, 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. This measures the changes and ambient levels of CO2 (Introduction, par 3-4, pg. 3546, wherein determining the ambient levels of the gas, water, and sunlight at the location is performed by machine learning based on a weather forecast, ii previously recorded ambient levels of the gas, water, and sunlight, and iii satellite imaging. (Claim 2, Claim 10, Claim 16) Sanga-Ngoie et al. teaches the monitoring of CO2, wherein: the gas includes carbon dioxide and the ambient levels of carbon dioxide are determined using a selection from the group consisting of satellite imaging and carbon sequestration at the location. (Claims 4, Claim 12, Claim 18)
With respect to the limitations of Claim 2, 10, 16, Voyantet al. teaches forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. (abstract, wherein determining the ambient levels of the gas, water, and sunlight at the location is performed by machine learning based on a weather forecast, ii previously recorded ambient levels of the gas, water, and sunlight, and iii satellite imaging. (Claim 2, Claim 10, Claim 16) Voyantet et al. teaches the monitoring of sunlight.
With respect to the limitations of Claim 2, 10, 16, Ahmad et al. teaches soil moisture is an integral quantity in hydrology that represents the average conditions in a finite vol ume ofsoil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in min imizing the empirical classification error and maximizing the geometric margin by solving inverse prob lem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimat ing soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models– one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agree ment with the estimates from VICmodel. TheSMestimatecorrelation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. (abstract, wherein determining the ambient levels of the gas, water, and sunlight at the location is performed by machine learning based on a weather forecast, ii previously recorded ambient levels of the gas, water, and sunlight, and iii satellite imaging. (Claim 2, Claim 10, Claim 16) Ahmad et al. teaches the monitoring of water.
A person of ordinary skill in the art would be motivated to combine the method Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller et al. with Ahmad et al. teaches the monitoring of water which is vital for both photosynthesis and artificial photosynthesis. Voyantet et al. teaches the monitoring of sunlight which is vital for both photosynthesis and artificial photosynthesis. Sanga-Ngoie et al. teaches the monitoring of CO2 which is vital for both photosynthesis and artificial photosynthesis. Therefore, a person of ordinary skill in the art would use them in order to choose an ideal location. There is a reasonable expectation of success because each piece works independently and the individual steps are not fundamentally changed therefore it is expected to work when combined.
Claims 5, 6, 13, 14, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. claims, 1, 9, 15, 3, 11, 17, 7 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 claims, 1, 9, 15, 3, 11, 17, 7 detailed above), Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. teaches a method for artificial photosynthesis optimization.
Regarding the limitations of dependent claim 6,
wherein the compensatory option is performed by adjusting a selection from the group consisting 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 a selection from the group consisting 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 a selection from the group consisting 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.
Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. does not explicitly teach:
further comprising: performing a cost-benefit analysis of compensating for the at least one limiting factor; and implementing, by one or more processors, a compensatory option from a plurality of compensatory options based on the cost-benefit-analysis. (Claim 5)
wherein the operations further comprise: 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 (Claim 13)
wherein the operations further comprise: 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 (Claim 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, by one or more processors, 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,
wherein the operations further comprise: 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,
wherein the operations further comprise: 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)
A person of ordinary skill in the art would be motivated to combine the method Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller 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 DeVincentis 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. Therefore, a person of ordinary skill in the art would use them in order to optimize photosynthesis. There is a reasonable expectation of success because each piece works independently and the individual steps are not fundamentally changed therefore it is expected to work when combined.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. claims, 1, 9, 15, 3, 11, 17, 7 above, and further in view of Xie et al. (Xie, L.; Jiang, G.; Qian, F. Experimental Research on Aftertreatment SCR Sizing Strategy for a Nonroad Mid–Range Diesel Engine. Energies 2020, 13 (17), 4462.)
As applied to claims, 1, 9, 15, 3, 11, 17, 7 (detailed above), Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. teaches a method for artificial photosynthesis optimization.
Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller at al. does not explicitly teach:
the computer implemented method of claim 7, further comprising: generating, by one or more processors, a three-dimensional lookup table for the catalyst that graphs the sensitivity profile of the catalyst, wherein the three-dimensional lookup table accounts for dimensions, quantity, and estimated wear-and-tear of the catalyst (Claim 8)
With respect to the limitations of Claim 8, Xie et al. teaches there are a comprehensive research experimental tests guiding how to determine the appropriate catalyst size for a non-road mid-range diesel engine with a given catalyst diameter (Experimental Methodology, paragraph 1) The results show that by applying the SCR substrate aftertreatment with a cell density of 600 cpsi, NOx conversion capability is stronger than that with cell density 400 cpsi for the same SCR size. (abstract) catalyst material selection for higher NOx conversion efficiency. Cu-zeolite was claimed to perform well on NOx emissions and could become a good candidate for the next generation SCR technology, especially for applications that require high thermal durability. It is an obvious variation of to include this data in a 3d lookup table when using the same information (introduction, pg. 3, the computer implemented method of claim 7, further comprising: generating, by one or more processors, a three-dimensional lookup table for the catalyst that graphs the sensitivity profile of the catalyst, wherein the three-dimensional lookup table accounts for dimensions, quantity, and estimated wear-and-tear of the catalyst (Claim 8))
A person of ordinary skill in the art would be motivated to combine the method Diego F. Lozano-Garcia et al. in view of Huesemanna et al. in view of Miller et al. with the method to chose an optimal catalyst taught by Xie et al. A person of ordinary skill in the art would look at how others have previously optimized catalysts. Therefore, a person of ordinary skill in the art would use the method of Xie et al. to optimize the choice of catalyst. As a person of ordinary skill in the art would use the same methods and techniques to catalyst efficiency are applicable to other fields. Therefore, a person of ordinary skill in the art would use it in order to optimize photosynthesis. There is a reasonable expectation of success because each piece works independently and the individual steps are not fundamentally changed therefore it is expected to work when combined.
Response to Arguments 103
Arguments for claims 1-20 are moot because applicant amended the independent claims by adding the following limitation “responsive to determining the at least one limiting factor, diverting, by one or more processors, a first type of gas from the location to a second location, through a series of interconnected pipes, to compensate for the at least one limiting factor” previous claims in order to overcome the previous rejection. A new ground of rejection was necessitated by amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.H.B./Examiner, Art Unit 1687 /Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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