DETAILED ACTION
This Final Office Action is in response to the argument and amendment filed December 09, 2025.
Claims 1, 5, 25 and 26 are amended.
Claims 2-4, 6-8, 10-11, and 13-24 are originals.
Claims 9 and 12 are cancelled.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 10-11, and 13-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1: Claims 1-8, 10-11, and 13-26 are directed to a method, system. Thus, each of the claims falls within one of the four statutory categories (step 1). However, the claims also fall within the judicial exception of an abstract idea (step 2). While claims 1, 25 and 26 are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1;
Computer-implemented monitoring method for a renewables plant, the plant being configured for production of a chemical or fuel product at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the method comprising:
a) receiving input data indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor,
b) solving a multi-objective optimization problem, wherein the solution maximizes a product yield while simultaneously optimizing a sustainability score, the sustainability score being calculated from the received input data and wherein the solution defines one or more optimal set points for a group of manipulable underlying process variable;
c) tracking and identifying deviations from optimal setpoint in one or more of the manipulated underlying variables; and
d) automatically adjusting said one or more underlying variables based on the identified deviation, thereby maximizing the product yield while simultaneously optimizing the sustainability score.
The abstract idea of claim 25;
Computer-implemented method of controlling production of a chemical or fuel product by renewables plant at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the method comprising:
a) at a predetermined measuring interval, or continuously, receiving input data obtained from the means for registering input data and indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor;
b) at a predetermined calculating interval, or continuously, calculating a sustainability score from the received input data;
c) determining a deviation in the sustainability score;
d) determining an underlying variable as a cause of the deviation; and
e) automatically changing the underlying variable to obtain a target sustainability score; and
f) automatically adjusting at least one manipulated operating variable of the renewables plant based on the target sustainability score thereby maximizing the product yield while simultaneously optimizing the sustainability score.
The abstract idea of claim 26;
Computer-implemented system for controlling production of a chemical or fuel product by a renewables plant at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the system being configured for:
a) at a predetermined measuring interval, or continuously, receiving input data obtained from the means for registering input data and indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor;
b) at a predetermined calculating interval, or continuously, calculating a sustainability score from the received input data;
c) determining a deviation in the sustainability score;
d) determining an underlying variable as a cause of the deviation; and
e) automatically changing the underlying variable to obtain a target sustainability score, wherein the system is configured to automatically adjust at least one manipulated operating variable of the renewables plant based on the target sustainability score, thereby maximizing the product yield while simultaneously optimizing the sustainability score.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, registering, receiving, calculating and monitoring sensors in the renewable plant. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, and alternatively, the abstract idea steps italicized above relate to elements of calculation of sustainability score including the data input, and variable aspects, which constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. This is further supported by page 9-13 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: A computer, a renewable feedstock or source, input data, sustainability score.
Claim 25 and 26 recites the following additional elements: Computer-implemented, renewables plant, a renewable feedstock or source, input data, material input, a production energy consumption, a deviation, sustainability score, product yield.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [page 19] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claim 2-8, 10-11, and 13-24 contain additional steps that further narrow the abstract idea above.
Claim 2 recites the following additional elements: Computer, input data, sensors. Applicant has only described generic computing elements in their specification, as seen in {[Fig 1, page 10]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 3 recites the following additional elements: sustainability score, Greenhouse Gas (GHG) emission score, carbon footprint score, Life cycle assessment (LCA) score. Applicant has only described generic computing elements in their specification, as seen in {[Page 11]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 4 recites the following additional elements: Input data. Applicant has only described generic computing elements in their specification, as seen in {[Page 6]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 5 recites the following additional elements: hydrogen plant steam reformer. Applicant has only described generic computing elements in their specification, as seen in {[Page 21]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 18 recites the following additional elements: Human machine interface. Applicant has only described generic computing elements in their specification, as seen in {[Page 26]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
The machine interface is not a technical improvement and merely implementing the abstract idea using generic technology. As such additional elements are not significantly more or transformative into a practical application. MPEP 2106.05(F). Therefore, the claims are covered under certain methods of mental process groupings of abstract ideas.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non - statutory subject matter.
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.
Claim(s) 1-4, 6-8, 10- 11, and 13-26 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al [US2020/0096992], hereafter Cella, in view of Walker et al [US2010/0274612] hereafter Walker, in further view of Pancholi et al [US2022/0284519] hereafter Pancholi.
As per claim 1;
Cella discloses;
Computer-implemented monitoring method for a renewables plant, the plant being configured for production of a chemical or fuel product at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the method comprising: a) receiving input data indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor,
{[1034] Certain operations described herein include interpreting, receiving, and/or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and/or determining any value parameter, input, data, and/or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and/or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value.}
Cella discloses;
b) solving a multi-objective optimization problem, wherein the solution maximizes a product yield
{[0328] In embodiments, certain machine learning algorithms may be used (such as genetic algorithms defined for solving both constrained and unconstrained optimization problems that may be based on natural selection, the process that drives biological evolution). By way of this example, genetic algorithms may be deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear.
Walker discloses the dynamic scoring;
while simultaneously optimizing a sustainability score, the sustainability score being calculated from the received input data and wherein the solution defines one or more optimal set points for a group of manipulable underlying process variable;
{[0035] FIG. 6 illustrates a dynamic scoring system 600 for sustainability factors. The system 600 includes a dynamic scoring component 610 that processes one or more sustainability factors 620 and scoring metrics 630. Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses the manipulation of the process
{[0054] Automated production lines can monitor some level of energy usage for startup profiles, maintaining recipe optimization, or for regulatory compliance. Manufacturers could, by applying various monitoring components, have the ability to make scheduling, forecasting and optimizing choices against energy demands through the use of standard production simulation tools. They could manipulate schedules to move orders that consume large amounts of energy to off peak pricing (load leveling). Also, in areas where energy has been de-regulated, manufactures will be able to make wiser choices based on manufacturing schedules.}
Walker discloses;
c) tracking and identifying deviations from optimal setpoint in one or more of the manipulated underlying variables; and
{[0029] Sustainability factors can be created using known industry standards, or, individuals can develop their own factors in order to track and measure those characteristics that are of particular importance to them. A table describing sample sustainability factors is described below with respect to FIG. 8. However, as a sustainability factor could be self-created to account for factors unique in importance to an individual, company, retailer, region, and so forth, thus, it is to be appreciated that the table is not an all-inclusive list. In another aspect, a system for controlling an industrial process is provided. The system includes means for scoring (scoring component 140) one or more sustainability factors and means for weighting the sustainability factors (see metrics 630 in FIG. 6 below). The system also includes means for adjusting (optimization component 150) a production process in view of at least one of a recycling requirement, a carbon footprint, a procurement process, a shipping process, or a regulatory requirement.}
Walker discloses the manipulation of the process
{[0054] Automated production lines can monitor some level of energy usage for startup profiles, maintaining recipe optimization, or for regulatory compliance. Manufacturers could, by applying various monitoring components, have the ability to make scheduling, forecasting and optimizing choices against energy demands through the use of standard production simulation tools. They could manipulate schedules to move orders that consume large amounts of energy to off peak pricing (load leveling). Also, in areas where energy has been de-regulated, manufactures will be able to make wiser choices based on manufacturing schedules.}
Walker discloses;
d) automatically adjusting said one or more underlying variables based on the identified deviation, thereby maximizing the product yield while simultaneously optimizing the sustainability score.
{[0022] An optimizer automatically adjusts a production process in view of the sustainability factors, the weight, and at least one of a recycling requirement, a carbon footprint, a procurement process, a shipping process, or a regulatory requirement.
[0035] FIG. 6 illustrates a dynamic scoring system 600 for sustainability factors. The system 600 includes a dynamic scoring component 610 that processes one or more sustainability factors 620 and scoring metrics 630. Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses maximizing the product yield
{[0034] By associating a sustainability factor indicating the type of energy used for production, in addition to other relevant sustainability factors, manufacturers could optimize production to take maximum advantage of government rebates and other incentives while minimizing the risk of adverse judgments. Similarly, regulatory bodies could optimize tax administration and administration of other regulations to drive the desired behavior to keep their economies and environments responsible and sustainable.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s simultaneous optimizing a sustainability score, the sustainability score being calculated from the received input data and wherein the solution defines one or more optimal set points for a group of manipulable underlying process variable; tracking and identifying deviations; and automatically adjusting one or more underlying variables based on the identified deviation, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include a dynamic scoring component that processes one or more sustainability factors and scoring metrics, using known industry standards, or, individuals to track and measure characteristics that are of particular importance, upon processing of the factors and metrics, one or more sustainability scores are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics can enable the calculation of a dynamic sustainability score for each product/method. See Walker [0022, 0029 and 0035].
Pancholi discloses the received input data to be calculated;
{[0195] Similarly, although the simulations performed by planning tool 600 are described primarily as simulating or predicting the economic feasibility or financial performance of various plant designs, it is contemplated that the simulations performed by planning tool 600 can simulate or predict any of a variety of performance metrics in addition to or in place of financial metrics, and can be applied to any type or combination of equipment in addition to or in place of equipment of an energy plant. Examples of performance metrics that could be simulated or predicted by planning tool 600 include cost metrics (e.g., true cost, rate of return, payback period, capital expenditure, operating cost, maintenance cost, equipment degradation cost, cost of carbon offset credits, etc.), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.), healthy building metrics such as an indoor air quality (IAQ) index (which may be a combination of various air quality metrics such as PM 2.5, volatile organic compounds, carbon dioxide, etc.), a probability of infection of building occupants (e.g., expected hospitalizations, expected absentee rate, reproduction number), occupant productivity, or any other types of metrics that quantify the performance of the simulated system and/or the impact or effects of the simulated system on building occupants.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s received input data; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include some performance metrics that could be simulated or predicted by a planning tool such as cost metrics (e.g., true cost), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.). See Pancholi [0195].
As per claim 2;
Cella discloses;
Computer-implemented monitoring method according to claim 1 wherein the means for registering input data include a plurality of sensors, and said input data is based on measurements by one or more of the plurality of sensors.
{[0354] In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR/VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations.
[0352] In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to a heat map. Coordinates may include real world location coordinates (such as geo-location or location on a map of an environment), as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for representation of analog sensor signals, digital signals, input source information, and various combinations, in a map-based visualization, such that colors may represent varying levels of input along the relevant dimensions.}
As per claim 3;
Pancholi discloses;
Computer-implemented monitoring method according to claim 1, wherein the sustainability score is or comprises a Carbon Intensity score, a Green House Gas (GHG) emission score, or another carbon footprint score, or a Life Cycle Assessment (LCA) score.
{[0210], Although carbon emissions is provided as one example of a sustainability metric that can be accounted for in the cost function J, it is contemplated that the carbon emissions term can be replaced with any other sustainability control objective or sustainability metric (e.g., water usage, global warming potential, non-carbon pollution, etc.), or other sustainability control objectives or sustainability metrics can be added as additional terms in the cost function J in addition to the carbon emissions term. In sustainability cost functions that account for other sustainability metrics, the β variables can include coefficients that translate the values of the decision variables into units of the corresponding sustainability control objective (e.g., amount of water consumption per unit of resource consumption or resource production, amount of global warming potential per unit of resource consumption or resource production, amount of non-carbon pollution per unit of resource consumption or resource production).}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s sustainability score that comprises a Carbon Intensity score, a Green House Gas (GHG) emission score, or another carbon footprint score, or a Life Cycle Assessment (LCA) score; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include providing carbon emissions as one example of a sustainability metric that can be accounted for in the cost function J, it is contemplated that the carbon emissions term can be replaced with any other sustainability control objective or sustainability metric (e.g., water usage, global warming potential, non-carbon pollution, etc.), to enable a better calculation of the sustainability and metrics score. See Pancholi [0210].
As per claim 4;
Walker discloses;
Computer-implemented monitoring method according to claim 1 wherein at least some of the input data are related to liquid, gaseous, or solid streams, and comprise a mass flow, a volume flow, a temperature, a pressure, a chemical composition, and/or electrical consumption.
{[0057] Production tools for material forecasting can be used for energy forecasting when energy is added to the BOM 900. Manufacturing can forecast demands on infrastructure such as compressed air, steam, electricity, natural gas, and water, for example. Rates with utility brokers in unregulated areas could be negotiated more accurately. Production emission rates can be calculated and applied to the BOM 900. Again, allowing standard production forecasting tools to forecast emission against Cap and Trade regulations, for example. Energy information on the BOM 900 can aid in prioritizing production schedules to load level demand. Adjusting schedules based on peak demand times can reduce the overall cost of energy consumed.
[0058] …. A manufacturing execution system (MES) is a control system for managing and monitoring work-in-process on a factory floor. An MES tracks manufacturing information in real time, receiving up-to-the-minute data from robots, machine monitors and employees. As noted previously, another type of model 1010 that can be employed includes an S88 type model.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s input data are related to liquid, gaseous, or solid streams, and comprise a mass flow, a volume flow, a temperature, a pressure, a chemical composition, and/or electrical consumption, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include forecast demand on infrastructure such as compressed air, steam, electricity, natural gas, and water, using a manufacturing execution system (MES) to control system for managing and monitoring work-in-process and tracking manufacturing information in real time, receiving up-to-the-minute data from robots, machine monitors and employees. See Walker [0057-0058].
As per claim 6;
Walker discloses;
Computer-implemented monitoring method according to claim 1, wherein the input data are received at regular intervals and/or continuously and/or in real time.
{[0058] A manufacturing execution system (MES) is a control system for managing and monitoring work-in-process on a factory floor. An MES tracks manufacturing information in real time, receiving up-to-the-minute data from robots, machine monitors and employees. As noted previously, another type of model 1010 that can be employed includes an S88 type model. Still yet other models for associating with sustainability factors include programming models which can include ladder programs, SFC programs, functions block programs, and other control programs, for example.
[0047] In contrast to prior systems that could only view energy from the overall sense of plant-wide consumptions, the source data that is associated with the BOM (or other model described below) can now be analyzed in real-time or via offline modeling to optimize and mitigate energy usage. For example, portions of a process may be rearranged to minimize overall energy usage (e.g., perform step C before step A in order to conserve energy from the reverse order of A and C). It is noted that various models other than BOM models can have associated sustainability factors.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s input data received at regular intervals and/or continuously and/or in real time, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include an MES that tracks manufacturing information in real time, receiving up-to-the-minute data from robots, machine monitors and employees. To enable the control system to manage and monitor the work-in-process. See Walker [0058].
As per claim 7;
Pancholi discloses;
Computer-implemented monitoring method according to claim 1, wherein a display device interactively displays input data, the display device being configured for graphically or textually receiving an input signal from the monitoring system via a dedicated communication infrastructure, creating an interactive display for a user.
{[0186] Referring now to FIG. 9-12, several examples of user interface 624 are shown, according to various embodiments. As discussed above, user interface 624 may be generated by planning tool 600 and displayed to a user via client device 622. Client device 622 may be a personal computer, laptop, smartphone, tablet, workstation, or any other type of suitable computing device. In some embodiments, planning tool 600 is implemented as a cloud-based SaaS (i.e., software as a service) application and can be accessed via an electronic network (e.g., via web services 614). Planning tool 600 may be used by personnel such as application engineers, commissioning engineers, and technicians to facilitate the design of a central plant. The example user interfaces of FIG. 9-12 are depicted to be illustrative and should not be regarded as strictly limiting. It should be appreciated that the various elements both included and not included in the different user interfaces can be rearranged, excluded, included, and otherwise transformed between the various examples without deviating from the scope of this disclosure.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s interactive display input data; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include a user interface that may be generated by a planning tool and displayed to a user via client device. It may be a personal computer, laptop, smartphone, tablet, workstation, or any other type of suitable computing device to facilitate the design and interaction with the user. See Pancholi [0186].
As per claim 8;
Pancholi discloses;
Computer-implemented monitoring method according to claim 7 wherein utilization factors, are displayed on the display device.
{[0174] Simulation result data is displayed to a user of Planning Tool for further economic analysis in step 712. The derived Energy Cost can serve a basis of comparison between varied replicated client site models being compared in order to derive the best fit model. In some embodiments, a user indicates one or more simulation results to generate financial performance data. Generation of the economic analysis can be performed by financial performance analyzer 636. In other embodiments, financial performance data can be retrieved from results database 628 from previously analyzed plant designs or simulations.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s utilization factors displayed on the display device; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include displaying a Simulation result data to a user of Planning Tool to facilitate the design, input and interaction with the user. See Pancholi [0174].
As per claim 10;
Cella discloses;
Computer-implemented monitoring method according to claim 1, further comprising transmitting executable information to the renewables plant via a communication network.
{[0874] A physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input/output functions to one or more network nodes in the net, accelerating relay functions, or the like.}
As per claim 11;
Pancholi discloses;
Computer-implemented monitoring method according to claim 1 further comprising calculating improved values for one or more underlying variables and reporting the calculated improved values to an operator of the plant.
{[0260] If the user is still not happy with the Batch 2 simulation results 1903, the user can trigger another iteration of process 1700, this time providing both the Batch 1 and the Batch 2 points 1802-1804 and the Batch 1 and Batch 2 simulation results 1902-1904 as input to process 1700 as the current dataset in step 1710. Process 1700 can then be run to generate the three new points 1806 which are returned to the user in step 1714 as the suggested new points {tilde over (x)}k and shown as the Batch 3 points in GUI 1800. If the user wishes to run the simulations for points 1806, planning tool 600 run a simulation for each of new points 1806 to generate corresponding sets of simulation results 1906 shown as the Batch 3 simulation results in GUI 1900. The Batch 3 simulation results 1906 can be added to GUI 1900.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s calculating improved values for one or more underlying variables and reporting the calculated improved values to an operator of the plant; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include reporting the simulation result data to a user of Planning Tool to facilitate the design, input and interaction with the user. See Pancholi [0260].
As per claim 13;
Walker discloses;
Computer-implemented monitoring method according to claim 1 further comprising adjusting one or more underlying variables in response to the observed deviations.
{[0029] The system includes means for scoring (scoring component 140) one or more sustainability factors and means for weighting the sustainability factors (see metrics 630 in FIG. 6 below). The system also includes means for adjusting (optimization component 150) a production process in view of at least one of a recycling requirement, a carbon footprint, a procurement process, a shipping process, or a regulatory requirement.
[0031] At 230, after determining the recycling requirements, a production process is automatically adjusted in view of the requirements. For example, if the current process is to bottle a product in an aluminum container, and the recycling requirement is for plastic containers, batch processes can be automatically re-routed to a packaging line that employs plastic containers rather than aluminum. As can be appreciated, a plurality of such routing decisions can be performed depending on the number of recycling options supported. At 240, products are shipped to the destination locations with the appropriate packaging that has been tailored for the recycling requirements of the location.}
As per claim 14;
Walker discloses;
Computer-implemented monitoring method according to claim 1, wherein the improved values for one or more of the underlying variables are calculated to improve the sustainability score of the production process and/or a utilization factor
{[0035] Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.
[0036] This score 640 can be an individual value, a combinatorial value, a multi-factorial value, or a weighted value, for example. For example, a given individual may care about a single value, such as the overall carbon impact, or emissions, for a given product.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s improved values for one or more of the underlying variables are calculated to improve the sustainability score of the production process and/or a utilization factor, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include processing of the factors and metrics, one or more sustainability scores generated and automatically adjusted to enable the calculation of a dynamic sustainability score for each product/method. This enables the sustainability score to calculate the optimal result from desired metrics to result in the ‘most sustainable’ product or process for the end user.
See Walker [0035 an 0036].
As per claim 15;
Cella discloses;
Computer-implemented monitoring method according to claim 1 wherein the production process comprises a catalytic reaction step, and the method comprises receiving input data for temperature and pressure in the catalytic reaction step.
{[0982] Downstream actions may include: triggering an alert of a failure, imminent failure, or maintenance event; shutting down equipment/component; initiating maintenance/lubrication/alignment; deploying a field technician; recommending a vibration absorption/dampening device; modifying a process to utilize backup equipment/component; modifying a process to preserve products/reactants, etc.; generating/modifying a maintenance schedule; coupling the vibration fingerprint with duty cycle of the equipment, RPM, flow rate, pressure, temperature or other vibration-driving characteristic to obtain equipment/component status and generate a report, and the like. For example, vibration noise for a catalytic reactor in a chemical processing plant may be matched to a condition when the catalytic reactor required maintenance.}
As per claim 16;
Cella discloses;
Computer-implemented monitoring method according to claim 15 wherein the replacement of catalysts is optimized, being scheduled as a result of calculations based on input data.
{[0960] In embodiments, the expert system may change smart band settings in the event that a new set of offset data is available from a third-party library. For example, a pharmaceutical processing plant may have optimized a catalytic reactor to operate in a highly efficient way and deposited the smart band settings in a data structure. The data structure may be continuously scanned for new smart bands that better aid in monitoring catalytic reactions and thus, result in optimizing the operation of the reactor.}
As per claim 17;
Cella discloses;
Data-processing system for performing the computer- implemented monitoring method according to claim 1, said data-processing system comprising a server, the server being located distant from the renewables plant and being connected to the internet.
{[0427] In embodiments, streaming hubs such as the streaming hubs 5420, 5480 may effectively move the electronics required for streaming to an external hub via cable. It will be appreciated in light of the disclosure that the streaming hubs may be located virtually next to the streaming sensors or up to a distance supported by the electronic driving capability of the hub. In instances where an internet cache protocol (“ICP”) is used, the distance supported by the electronic driving capability of the hub would be anywhere from 100 to 1000 feet (30.5 to 305 meters) based on desired frequency response, cable capacitance, and the like.}
As per claim 18;
Pancholi discloses;
Computer-implemented monitoring system for a renewables plant providing a display device for calculating and interactively displaying input data and sustainability scores, the display device being configured for graphically or textually receiving an input signal, using a human- machine interface via a dedicated communication infrastructure, said monitoring system comprising:- the means for registering input data; - the data-processing system according to- the data-processing system according to wherein said system is coupled to a server for communicating with a plant via a communication network, using a web-based platform for receiving and/or sending input data over the network.
{[0086] Client device 368 can include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 100, its subsystems, and/or devices. Client device 368 can be a computer workstation, a client terminal, a remote or local interface, or any other type of user interface device. Client device 368 can be a stationary terminal or a mobile device. For example, client device 368 can be a desktop computer, a computer server with a user interface, a laptop computer, a tablet, a smartphone, a PDA, or any other type of mobile or non-mobile device. Client device 368 may communicate with BMS controller 366 and/or AHU controller 330 via communications link 372.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s the display device being configured for graphically or textually receiving an input signal, using a human- machine interface via a dedicated communication infrastructure; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with HVAC system 100, its subsystems, and/or devices. See Pancholi [0086].
As per claim 19;
Cella discloses;
Computer-implemented monitoring system for a renewables plant according to claim 18, wherein said means for registering input data are a plurality of sensors located at the renewables plant, configured for transmitting sensor data to the server via the internet.
{[0030] In embodiments, the local data collection system includes a graphical user interface (“GUI”) system configured to manage the data collection bands. In embodiments, the GUI system includes an expert system diagnostic tool. In embodiments, the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment. In embodiments, the platform is configured to provide self-organization of data pools based on at least one of the utilization metrics and yield metrics. In embodiments, the platform includes a self-organized swarm of industrial data collectors. In embodiments, the local data collection system includes a wearable haptic user interface for an industrial sensor data collector with at least one of vibration, heat, electrical, and sound outputs.}
As per claim 20;
Cella discloses;
Computer-implemented monitoring system for renewables plant according to claim 19, wherein said plurality of sensors monitor the same or different underlying variables or parameters of the production process.
{[0034] In embodiments, the tri-axial sensor is located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors simultaneously. In embodiments, the method includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location is a position associated with the shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with the shaft of the machine. The tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine.}
As per claim 21;
Cella discloses;
Computer-implemented monitoring system for a renewables plant according to claim 18, wherein said plurality of sensors are located along a reactor to collect data from different positions in the same location of the equipment.
{[0034] In embodiments, the tri-axial sensor is located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors simultaneously. In embodiments, the method includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location is a position associated with the shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with the shaft of the machine. The tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine.}
As per claim 22;
Cella discloses;
Plant for production of a chemical or fuel product, at least partly from a renewable feedstock or source, the plant comprising a data processing system according to claim 17 the plant comprising the means for registering input data and being arranged such that: a) input data indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor product are received
{[1047] An example system includes an industrial system including an oil refinery. An example oil refinery includes one or more compressors for transferring fluids throughout the plant, and/or for pressurizing fluid streams (e.g., for reflux in a distillation column). Additionally, or alternatively, the example oil refinery includes vacuum distillation, for example, to fractionate hydrocarbons. The example oil refinery additionally includes various pipelines in the system for transferring fluids, bringing in feedstock, final product delivery, and the like. An example system includes a number of sensors configured to determine each aspect of a distillation column—for example temperatures of various fluid streams, temperatures, and compositions of individual contact trays in the column, measurements of the feed and reflux, as well as of the effluent or separated products. The design of a distillation column is complex, and optimal design can depend upon the sizing of boilers, compressors, the contact conditions within the column, as well as the composition of feedstock, all of which can vary significantly.
[1103] In this example, as feedback is received during operation of the thermic heating system, the expert system may instruct the system to modify one or more operational parameters, such as to change the input feedstock, to increase the flow of the feedstock, and the like.}
Cella does not disclose sustainability score. However, Pancholi does disclose the following limitation:
Pancholi discloses;
a sustainability score from the received input data is calculated.
{[0195] Similarly, although the simulations performed by planning tool 600 are described primarily as simulating or predicting the economic feasibility or financial performance of various plant designs, it is contemplated that the simulations performed by planning tool 600 can simulate or predict any of a variety of performance metrics in addition to or in place of financial metrics, and can be applied to any type or combination of equipment in addition to or in place of equipment of an energy plant. Examples of performance metrics that could be simulated or predicted by planning tool 600 include cost metrics (e.g., true cost, rate of return, payback period, capital expenditure, operating cost, maintenance cost, equipment degradation cost, cost of carbon offset credits, etc.), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.), healthy building metrics such as an indoor air quality (IAQ) index (which may be a combination of various air quality metrics such as PM 2.5, volatile organic compounds, carbon dioxide, etc.), a probability of infection of building occupants (e.g., expected hospitalizations, expected absentee rate, reproduction number), occupant productivity, or any other types of metrics that quantify the performance of the simulated system and/or the impact or effects of the simulated system on building occupants.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s sustainability score from the received input data is calculated; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include some performance metrics that could be simulated or predicted by a planning tool such as cost metrics (e.g., true cost), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.). See Pancholi [0195].
As per claim 23;
Cella discloses;
Plant according to claim 22, wherein the means for registering input data are one or more sensors
{[0352] In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to a heat map. Coordinates may include real world location coordinates (such as geo-location or location on a map of an environment), as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for representation of analog sensor signals, digital signals, input source information, and various combinations, in a map-based visualization, such that colors may represent varying levels of input along the relevant dimensions.
[0354] In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR/VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations.}
As per claim 24;
Cella discloses;
Plant according to claim 22 wherein said plant is arranged for production of a chemical or fuel product via hydro processing, hydrogen production, ammonia production or production of methanol, ethanol, naphtha, synthesis gas, jet fuel, diesel, from renewable feedstocks or sources, including e-chemicals and e-fuels.
{[1109] The industry-specific feedback 11118 includes the power utilization efficiency of a machine about which the input sensors provide information, wherein the machine is one of a turbine, a transformer, a generator, a compressor, one that stores energy, and one that includes power train components (e.g., the rate of extraction of a material by a machine about which the input sensors provide information, the rate of production of a gas by a machine about which the input sensors provide information, the rate of production of a hydrocarbon product by a machine about which the input sensors provide information), and the rate of production of a chemical product by a machine about which the input sensors provide information. The machine learning data analysis circuit 11110 may be further structured to learn received output data patterns 11112 based on the outcome.
[2068] In embodiments, solar collector panels or the like may be configured with a hydrogen production system, such as a system described herein, to provide electricity for powering the production of hydrogen, including from water. A hydrogen production system may be built with integrated solar collector panels and the ability to connect to further solar systems, so that placement of the hydrogen production system in an ambient environment that is exposed to sunlight may facilitate its self-powered operation or partially-self-powered operation via solar power.}
As per claim 25;
Cella discloses;
Computer-implemented method of controlling production of a chemical or fuel product by renewables plant at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the method comprising:
at a predetermined measuring interval, or continuously, receiving input data obtained from the means for registering input data and indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor;
{[1034] Certain operations described herein include interpreting, receiving, and/or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and/or determining any value parameter, input, data, and/or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and/or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value.}
Pancholi discloses;
b) at a predetermined calculating interval, or continuously, calculating a sustainability score from the received input data;
{[0195] Similarly, although the simulations performed by planning tool 600 are described primarily as simulating or predicting the economic feasibility or financial performance of various plant designs, it is contemplated that the simulations performed by planning tool 600 can simulate or predict any of a variety of performance metrics in addition to or in place of financial metrics, and can be applied to any type or combination of equipment in addition to or in place of equipment of an energy plant. Examples of performance metrics that could be simulated or predicted by planning tool 600 include cost metrics (e.g., true cost, rate of return, payback period, capital expenditure, operating cost, maintenance cost, equipment degradation cost, cost of carbon offset credits, etc.), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.), healthy building metrics such as an indoor air quality (IAQ) index (which may be a combination of various air quality metrics such as PM 2.5, volatile organic compounds, carbon dioxide, etc.), a probability of infection of building occupants (e.g., expected hospitalizations, expected absentee rate, reproduction number), occupant productivity, or any other types of metrics that quantify the performance of the simulated system and/or the impact or effects of the simulated system on building occupants.}
Pancholi discloses;
c) determining a deviation in the sustainability score;
{[0256] GUI 1900 shows a variety of performance metrics that result from performing the simulations for each of points 1802-1806. The simulation results for points 1802 are represented by simulation results 1902, the simulation results for points 1804 are represented by simulation results 1904, and the simulation results for points 1806 are represented by simulation results 1906. Each of simulation results 1902-1906 includes a value for a first performance metric (e.g., “Financial Cost”) and a value for a second performance metric (e.g., “Carbon Emissions”), which may be determined by running the simulation under the conditions defined by the corresponding points 1802-1806. Simulation results 1902-1906 correspond to the simulation results y described with reference to process 1700. Although only two performance metrics are shown in GUI 1900 for ease of illustration, it is contemplated that each of simulation results 1902-1906 can include values for any number of performance metrics or control objectives that characterize the corresponding simulation.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s predetermined calculation of interval, calculation of a sustainability score from the received input data; determination of a deviation in the sustainability score; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include simulating and predicting any of a variety of performance metrics in addition to or in place of financial metrics, to enable a sustainability score. See Pancholi [0195; 0256].
Walker discloses;
d) determining an underlying variable as a cause of the deviation; and
{[0035] The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses;
e) automatically changing the underlying variable to obtain a target sustainability score; and
{[0035] FIG. 6 illustrates a dynamic scoring system 600 for sustainability factors. The system 600 includes a dynamic scoring component 610 that processes one or more sustainability factors 620 and scoring metrics 630. Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses;
f) automatically adjusting at least one manipulated operating variable of the renewables plant based on the target sustainability score thereby maximizing the product yield while simultaneously optimizing the sustainability score.
{[0022] An optimizer automatically adjusts a production process in view of the sustainability factors, the weight, and at least one of a recycling requirement, a carbon footprint, a procurement process, a shipping process, or a regulatory requirement.
[0035] FIG. 6 illustrates a dynamic scoring system 600 for sustainability factors. The system 600 includes a dynamic scoring component 610 that processes one or more sustainability factors 620 and scoring metrics 630. Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses maximizing the product yield
{[0034] By associating a sustainability factor indicating the type of energy used for production, in addition to other relevant sustainability factors, manufacturers could optimize production to take maximum advantage of government rebates and other incentives while minimizing the risk of adverse judgments. Similarly, regulatory bodies could optimize tax administration and administration of other regulations to drive the desired behavior to keep their economies and environments responsible and sustainable.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s determination of underlying variable,
automatically changing the underlying variable to obtain a target sustainability score;
automatically adjusting at least one manipulated operating variable of the renewables plant based on the target sustainability score thereby maximizing the product yield while simultaneously optimizing the sustainability score, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include a dynamic scoring component that processes one or more sustainability factors and scoring metrics, using known industry standards, or, individuals to track and measure characteristics that are of particular importance, upon processing of the factors and metrics, one or more sustainability scores are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics can enable the calculation of a dynamic sustainability score for each product/method. See Walker [0022, 0029 and 0035].
As per claim 26;
Cella discloses;
Computer-implemented system for controlling production of a chemical or fuel product by a renewables plant at least partly from a renewable feedstock or source, the plant comprising means for registering input data, the system being configured for:
at a predetermined measuring interval, or continuously, receiving input data obtained from the means for registering input data and indicative of a measure of at least a material input to the production process, a production energy consumption, and a utilization factor;
{[1034] Certain operations described herein include interpreting, receiving, and/or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and/or determining any value parameter, input, data, and/or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and/or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value.}
Pancholi discloses;
b) at a predetermined calculating interval, or continuously, calculating a sustainability score from the received input data;
{[0195] Similarly, although the simulations performed by planning tool 600 are described primarily as simulating or predicting the economic feasibility or financial performance of various plant designs, it is contemplated that the simulations performed by planning tool 600 can simulate or predict any of a variety of performance metrics in addition to or in place of financial metrics, and can be applied to any type or combination of equipment in addition to or in place of equipment of an energy plant. Examples of performance metrics that could be simulated or predicted by planning tool 600 include cost metrics (e.g., true cost, rate of return, payback period, capital expenditure, operating cost, maintenance cost, equipment degradation cost, cost of carbon offset credits, etc.), sustainability metrics (e.g., carbon emissions, water usage, global warming potential, non-carbon pollution, etc.), healthy building metrics such as an indoor air quality (IAQ) index (which may be a combination of various air quality metrics such as PM 2.5, volatile organic compounds, carbon dioxide, etc.), a probability of infection of building occupants (e.g., expected hospitalizations, expected absentee rate, reproduction number), occupant productivity, or any other types of metrics that quantify the performance of the simulated system and/or the impact or effects of the simulated system on building occupants.}
Pancholi discloses;
c) determining a deviation in the sustainability score;
{[0256] GUI 1900 shows a variety of performance metrics that result from performing the simulations for each of points 1802-1806. The simulation results for points 1802 are represented by simulation results 1902, the simulation results for points 1804 are represented by simulation results 1904, and the simulation results for points 1806 are represented by simulation results 1906. Each of simulation results 1902-1906 includes a value for a first performance metric (e.g., “Financial Cost”) and a value for a second performance metric (e.g., “Carbon Emissions”), which may be determined by running the simulation under the conditions defined by the corresponding points 1802-1806. Simulation results 1902-1906 correspond to the simulation results y described with reference to process 1700. Although only two performance metrics are shown in GUI 1900 for ease of illustration, it is contemplated that each of simulation results 1902-1906 can include values for any number of performance metrics or control objectives that characterize the corresponding simulation.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Pancholi et al’s predetermined calculation of interval, calculation of a sustainability score from the received input data; determination of a deviation in the sustainability score; since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include simulating and predicting any of a variety of performance metrics in addition to or in place of financial metrics, to enable a sustainability score. See Pancholi [0195; 0256].
Walker discloses;
d) determining an underlying variable as a cause of the deviation; and
{[0035] The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses;
e) automatically changing the underlying variable to obtain a target sustainability score, wherein the system is configured to automatically adjust at least one manipulated operating variable of the renewables plant based on the target sustainability score, thereby maximizing the product yield while simultaneously optimizing the sustainability score.
{[0022] An optimizer automatically adjusts a production process in view of the sustainability factors, the weight, and at least one of a recycling requirement, a carbon footprint, a procurement process, a shipping process, or a regulatory requirement.
[0035] FIG. 6 illustrates a dynamic scoring system 600 for sustainability factors. The system 600 includes a dynamic scoring component 610 that processes one or more sustainability factors 620 and scoring metrics 630. Upon processing of the factors 620 and metrics 630, one or more sustainability scores 640 are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics 640 can enable the calculation of a dynamic sustainability score for each product/method such as a unit or batch perspective, for example. The sustainability score 640 can be the result of an algorithm to calculate the optimal result from desired metrics 630 to result in the ‘most sustainable’ product or process for the end user.}
Walker discloses maximizing the product yield
{[0034] By associating a sustainability factor indicating the type of energy used for production, in addition to other relevant sustainability factors, manufacturers could optimize production to take maximum advantage of government rebates and other incentives while minimizing the risk of adverse judgments. Similarly, regulatory bodies could optimize tax administration and administration of other regulations to drive the desired behavior to keep their economies and environments responsible and sustainable.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Walker et al’s determination of underlying variable,
automatically changing the underlying variable to obtain a target sustainability score;
automatically adjusting at least one manipulated operating variable of the renewables plant based on the target sustainability score thereby maximizing the product yield while simultaneously optimizing the sustainability score, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include a dynamic scoring component that processes one or more sustainability factors and scoring metrics, using known industry standards, or, individuals to track and measure characteristics that are of particular importance, upon processing of the factors and metrics, one or more sustainability scores are generated that can be employed to automatically adjust productions shipping, manufacturing methods, product procurement, packaging, and/or labeling. The use of metrics can enable the calculation of a dynamic sustainability score for each product/method. See Walker [0022, 0029 and 0035].
Claim(s) 5 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al, in view of Walker et al, in view of Pancholi et al, in further view of Schutzle et al [US2021/0340077] hereafter Schuetzle.
As per claim 5;
Schuetzle et al discloses;
Computer-implemented monitoring method according to claim 4, wherein at least one of said gaseous streams is a stream comprising at least 75 vol% H2 or feed streams to the hydrogen plant steam reformer.
{[0067] The per pass conversion of carbon dioxide to carbon monoxide in the RWGS reactor vessel is between 15 and 75 mole % or between 30 and 70 mole %, and the RWGS Weight Hourly Space Velocity between 1,000 and 50,000 hr−1 and more preferably 5,000 to 30,000 hr−1. One or more C1-C4 hydrocarbons (e.g., methane), carbon monoxide and hydrogen are fed into an auto-thermal reformer (“ATR”) that includes a solid solution catalyst to provide an ATR product stream. The RWGS product gas (either purified or not) is blended with the ATR product stream (either purified or not) and fed into a system that produces fuels or chemicals. The fuels or chemicals produced in this process, or a portion thereof, have a percent reduction in lifecycle Greenhouse Gas Emissions compared to the average lifecycle Greenhouse Gas Emissions for products produced from petroleum of at least 10 percent, at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent or at least 90 percent.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Cella’s monitoring system for renewable plants to include Schuetzle et al’s at least one of said gaseous streams is a stream comprising at least 75 vol% H2 or feed streams to the hydrogen plant steam reformer, since Cella teaches a machine learning algorithms and genetic algorithms deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear . (See Cella [0328]). The combination would have been obvious to one ordinary skill in the art to modify Cella to include a conversion of carbon dioxide to carbon monoxide to calculate the lifecycle Greenhouse Gas Emissions. See Schuetzel [0067].
Response to Arguments
Regarding the Abstract objection, the Examiner withdraws the objection based on Applicant arguments.
In response to the arguments filled December 09, 2025, regarding the 101 rejections, the Examiner Respectfully disagrees.
Applicant argues that the amended claims “automatically adjusting said one or more underlying variables based on the identified deviation, thereby maximizing the product yield while simultaneously optimizing the sustainability score” does not recite a mental process, mathematical concept and abstract idea, and as amended provide an improvement to a technical field. Applicant further states that the amended claims are technical improvements “the claimed invention improves the operation, control stability, catalyst lifecycle, and energy efficiency of renewables production plants through: "Real-time acquisition of physical process data from industrial sensors (e.g., pressure, mass flow, composition, electrical load), " Data cleansing and reconciliation to enforce mass and energy balance constraints, and " Automated adjustment of manipulated process variables and setpoints to optimize plant utilization and sustainability performance.”
Examiner notes that the system and method is directed to a mental process and mathematical concept.
The Examiner respectfully disagrees. The industrial sensors, data cleansing, automated adjustments are generic tools. These are merely generic technology with no technical improvement rather an improvement to the abstract idea using generic technology. See specification page 4.
Examiner notes that the system is directed to a mental process. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) Mental processes [] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 19 3, 197 (1978).
The Examiner maintains these claims recite an abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 35 USC 101 rejection.
Regarding the prior art rejections, the Examiner respectfully disagrees.
Applicant argues that the prior art of record fails to teach or suggest the closed-loop operational control configured to optimize both the product yield as well as a sustainability score, as now explicitly recited in amended independent claims 1, 25, and 26.
The Examiner respectfully disagrees.
In response to the amended claims, Examiner includes the prior art of Walker et al. to teach the simultaneous optimization of sustainability score, automatic adjustment of one or more underlying variables based on the identified deviation. See Walker [0022, 0029, 0035-0036 and 0058].
In terms of arguments Walker discloses calculating sustainability score and using it to automatically adjust plant operating variables, using measure real-time physical parameters to optimize performance process. See Walker [0057 -0058]. In addition, Walker discloses equipment adjustment in a sustainability score deviation. See Walker [ 0022 and 0035]. Walker does teach the use of the combination of sustainability scoring and maximization of product yield for real-time plant control. See Walker [0033]. Walker discloses the source data associated with the model can be analyzed in real-time or via offline modeling to optimize and mitigate energy. See Walker [0047].
In terms of the arguments Cella, Walker, Pancholi and Schuetzle does teach specific limitations such as amended.
Based on the considered amendments cited, 35 USC 103 references have been utilized to
teach the claimed invention (claim 1, 25 and 26). Lacking any further argument, claims 1-26 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitation above.
Conclusion
THIS ACTION IS MADE FINAL. 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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/VICTOR CHIGOZIRIM ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629