DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending in this Application.
Priority
This application is a continuation-in-part of PCT Application No. PCT/US22/50932 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
Also, this application is a continuation of PCT Application No. PCT/US22/50924 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 8, 12, 15-16, and 18-19 are rejected under 35 U.S.C. 102(a)1) as being anticipated by Cella et al (US 20190033845 cited in the IDS).
As per claim 1, Cella teaches an AI-based platform for enabling intelligent orchestration and management of power and energy (see Fig. 6 and see [0216] and [0219] platform 100; and see [0264], [0312]-[0316], [0326] “…machine learning…”; also, see 0344] “”…AI models…optimization of outputs (such as for production of energy…” and see [0402],), comprising:
a set of autonomous orchestration systems for improving delivery of a heterogeneous set of energy types to a point of consumption based on (see [0216] “The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like” ;asp. See [0219]; also, see [0312 “the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines,…include turbines, generators…”; [0313]-[0315] “…In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement… The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow (or volume of water) at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine”; 0316 “In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources…”; [0326] “…. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs…such as recognition of certain patterns (…such as fuel efficiency, energy production, or the like)... For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in battery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like). For example, if a resonance effect between two machines is adversely affecting one of them, the model may account for this and automatically provide an output that results in changing the operation of one of the machines (such as to reduce the resonance, to increase fuel efficiency of one or both machines); also, see also, see Fig. 164 and see [01399-01401]) a neural network to improve or optimize power efficiency/resource utilization. This embodiment is a neural network/ai based platform that accepts several attribute/parameters to optimize the output or delivery of power of a system such as power system; also, see also, see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, thus, the delivery of energy is improved):
a location of the point of consumption (see [01399] “ The self-organization functionality, in embodiments involving a neural net or other machine learning system…location of power sources..”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, portions of a grid suggests a location on the grid), and
a set of consumption attributes, the consumption attributes including at least one of (this requires only one of the following attributes since it is in the alternative):
a peak power requirement at the point of consumption (see [01117] “…peak energy consumption…”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, high demands suggests peak power at certain point of consumption);
a continuity of power requirement at the point of consumption (see [01399] …power availability…”; see [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others; also, see [2162] “…may be deployed as components in a smart power grid that may operate cooperatively with other components of a smart grid to attempt to deliver reliable energy available throughout the grid…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid”; also, see [2182] “…Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability,… to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”);
and a type of energy that can be used at the point of consumption (see [01401] “…type of equipment…”; also, see 2181 “..Example factors may include the price of other energy sources, including energy sources that are available to the cooking and heating system as well as those that are not directly available. In this way, selecting an energy source may be driven by other considerations, such as which energy source is better for the environment, and the like. In embodiments, an automatic energy source selection may be based, at least in part on the anticipated availability of an energy source…”).
As per claim 2, Cella teaches the AI-based platform of claim 1, Cella
Further teaches wherein the set of autonomous orchestration systems orchestrates delivery of defined types of energy generation capacity to the point of consumption (see also, see [2182] “…Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability,… to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”; also, see [2185] “…for each type of energy source (solar, hydro-based, wind, exhaust gas, including sulfur dioxide use, and the like..”; also, see [01789] “… define at least one of an energy utilization policy, …”; also, see [2151], [2162]).
As per claim 3, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein the set of autonomous orchestration systems orchestrates delivery of defined types of energy storage capacity to the point of consumption (see [2122] “…the methods and systems disclosed herein may include, connect with or be integrated with hydrogen production, storage, and use systems. In embodiments, the hydrogen production, storage, and use systems may use renewable energy as a source of energy for various operations including hydrogen production, hydrogen storage…”; also, see [2125] “…Solar energy harvesting may also be used to charge a battery, charge various thermal systems, or other electrical energy storage facility that may directly provide the energy needed for hydrogen production immediately or with a time-shift and on-demand functions and other operational elements as described herein… the impact of an absence of sunlight and therefore diminished solar power production may be mitigated through the use of an intermediate battery or the like”; thus, batteries are coordinated to provided power when solar power is unavailable’ also, see [2187] “…methods and systems related to hydrogen production, storage, distribution and use…”; also, see [2150], [2151] “…In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like… In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like… Supply, from the production of hydrogen and/or from stored hydrogen, may be directed where when it is predicted to be needed or it is predicted to be needed in possibly relatively fewer quantities but may be consumed more quickly”; also, see [2157]; also, see [2162] “…in an example, a renewable energy-based hydrogen production system may utilize its renewable energy harvesting components to deliver electricity to a smart grid based on various factors, such as local demand for hydrogen and the like. When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid.”, thus, stored energy from the stored hydrogen is used to provide power.
As per claim 4, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein the type of energy that can be used is determined at least in part based on a set of operational compatibility parameters (the term “operational compatibility parameters”, has not been defined in the disclosure and will be interpreted in the broadest reasonable interpretation (BRI) as operational data (0005), cost 90085, capacity (0085), time of operational use, operational conditions current or predicted (0095), operational needs or operational metrics (0096, 0115, 0116, 0119, 0126,), operational configuration of the event,..market prices (0101); Cella teaches [2182] “Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability, costs, efficiency, and the like that may be processed by, for example comparing the information to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”, Thus, the type of energy that can be used is determined at least in part based on a set of operational compatibility parameters such as cost, reliability, availability, efficiency).
As per claim 5, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein the type of energy that can be used is determined at least in part based on a set of governance parameters (the term “governance parameters” has not been explicitly defined in the disclosure and is related to the use of renewable resources or carbon generation/emissions according to claims 6-7 below. Therefore, it will be interpreted in the BRI in light of the disclosure as the type of energy that can be used is related to the use of renewable resources, carbon generation or emission, pollution (0093), etc., government policies or rules, track carbon generation or emissions (0103-0104); Cella further teaches [2182] “..,The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollution abatement credits, or the like based on a forecast of future energy needs for an individual or enterprise ).
As per claim 6, Cella teaches the AI-based platform of claim 5, Cella further teaches wherein the set of governance parameters relates to use of renewable energy resources (see [2125] “…Solar energy harvesting may also be used to charge a battery, charge various thermal systems, or other electrical energy storage facility that may directly provide the energy needed for hydrogen production immediately or with a time-shift and on-demand functions and other operational elements as described herein…”; also, see [2130-2131] “…solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like. A wind energy source 5711 may be natural air currents, motor driven air currents, air currents resulting from movement of a vehicle, or waste air flow sources 5719 (such as waste heat from heating operations, such as cooking and the like). Any of these renewable energy sources may be converted into a form of energy that is suitable for an intended use by the hydrogen production, storage, distribution, and use system….”; also, see [2157], [2182] “…,The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollution abatement credits, or the like based on a forecast of future energy needs for an individual or enterprise…”, [2183]).
As per claim 8, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is further configured to adapt a transport of data over a network and/or communication system (see [0018] “multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing, and/or other network conditions”; also, see [0353] “…Thus, a self-organizing, network-condition-adaptive data collection system is provided….”; [0481], [0610], [1889], [2664]), wherein the adapting is based on at least one of (only one parameter/factor below is required),
a congestion condition ([02025] “individual congestion control loops may be employed on each path to adapt to the available bandwidth and congestion on the path…”),
a delay and/or latency condition ([1887] “..adapting to changes in average throughput, latency, etc.”),
a packet loss condition ([1008] “machine learning system… Certain feedback may include utilization measures…data loss…”; [1954], [1964], [1969-1971], [2015]),
an error rate condition ([1008] “machine learning system… Certain feedback may include utilization measures…error rate in transmission” ),
a cost of transport condition ([1520] “…. An example transmission condition 12254 includes a change in a cost of transmitting information 12298 (e.g., cost has increased or decreased, where cost may be a direct cost parameter such as a data transmission subscription cost…”; [1582]),
a quality-of-service (QoS) condition (see [0018], [1530], [1592],[1594], [1670], [2213] “a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service…”, [2340]),
a usage condition,
a market factor condition, or
a user configuration condition.
As per claim 12, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is further configured to perform at least one of (only one function/factor below is required to read in the claims),
extracting energy-related data ([2238], [2792]),
detecting and/or correcting errors in energy-related data ([1673], [1674], [1864], [1895]),
transforming, converting, normalizing, and/or cleansing energy-related data ([1396]),
parsing energy-related data ([0392] “…parse…”),
detecting patterns, content, and/or objects in energy-related data (see [0363] “…training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment…”, [0365], [0390], and [2191] “…training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment…”),
compressing energy-related data ([1518], [1529], [1533]),
streaming energy-related data (see [2238]),
filtering energy-related data ([0335], [0381], [1042]),
loading and/or storing energy-related data ([2191]),
routing and/or transporting energy-related data ([0346], [0465], [0481]), or
maintaining security of energy-related data (see [2187]).
As per claim 15, Cella teaches the AI-based platform of claim 1, Cella further teaches further comprising at least one AI-based model and/or algorithm, wherein the at least one AI-based model and/or algorithm is trained based on a training data set, and the training data set is based on at least one of (only one parameter/factor below is required),
at least one human tag and/or label (0218; 0964), at least one human interaction with a hardware and/or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process (0324), a semi-supervised learning training process, or a deep learning training process (see [0324]; see [0343] “ Methods and systems are disclosed herein for training AI models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, and where the AI model operates on sensor data from an industrial environment….”; also, see [0935] “…a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback…”; [0938] “unsupervised learning”).
As per claim 16, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is further configured to orchestrate delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of (only one parameter/factor below is required),
at least one fixed transmission line,
at least one instance of wireless energy transmission,
at least one delivery of fuel, or at least one delivery of stored energy (see [0326] “…. For example, a model of fuel consumption by an industrial machine…estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like)”; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, portions of a grid suggests a location on the grid and suggest fixed transmission lines).
As per claim 18, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is deployed in an off-grid environment, and the off-grid environment includes at least one of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system (see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, [2047] “…In embodiments, the intelligent cooking system may be fueled by a hydrogen generator, referred to herein in some cases as the electrolyzer, an independent fuel source that does not require traditional connections to the electrical power grid,”, e.g. off grid system; also, see [2125], [2151]).
As per claim 19, Cella teaches the AI-based platform of claim 1, Cella further teaches wherein the set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a set of rules and/or policies that govern a set of energy generation, storage, and/or consumption workloads (see [0216] “The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116…”; also, see [0331] “…The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation,…”, ), and
the rules and/or policies are associated with a configuration of a set of edge devices operating in local data communication with a set of energy generation facilities, energy storage facilities, energy delivery facilities or energy consumption systems (see [0016] “These methods and systems include methods, systems, components, devices, workflows, services, processes, and the like that are deployed in various configurations and locations, such as: (a) at the “edge” of the Internet of Things, such as in the local environment of a heavy industrial machine;… as well as methods and systems for deploying increased intelligence at the edge, in the network, and in the cloud or premises of the controller of an industrial environment.”; also, see [0331] “…The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation,…” local controller suggest an edge controller; also, see [0335] and [0345]; also, see [2069]).
Claim Rejections - 35 USC § 103
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.
Claim(s) 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Steven (US 20160190805 cited in the IDS).
As per claim 7, Cella teaches the AI-based platform of claim 5, Cella does not explicitly teach wherein the set of governance parameters relates to carbon generation or emissions.
However, Steven teaches a systems for improving delivery of energy (see Fig. 28 and [0092] “FIG. 28 shows an example energy storage asset optimization according to a principle described herein”) comprising optimizing/improving the coordination of different set of energy types based on a set of governance parameters relates to carbon generation or emissions (see [0027] “the mathematical model for the energy asset(s) is employed to determine a suggested operating schedule over a given time period T for the energy asset(s) (different than the BAU operating schedule) based on a mathematical optimization of an “objective cost function” representing the net energy-related cost to the energy customer for operating the asset(s)”; also, see [0028] “The objective cost function employed in the mathematical optimization to determine a suggested operating schedule for the energy asset(s) also may specify energy-related costs which are offset by the energy-related revenues. In particular, in some examples, the energy-related costs included in the objective cost function may include… emissions-related costs,…”; also, see [0102] “to provide energy asset management capabilities for reducing retail electricity costs by optimizing electricity usage, generation, and storage, while at the same time providing significant revenue opportunities in markets, including wholesale electricity markets, in regulation markets, in synchronized reserve markets and/or in emissions markets.”; also, see [0174] “…The emissions costs can be associated with greenhouse gas emissions during operation of the system. Non-limiting examples of such emissions are CO.sub.x emissions (e.g., carbon monoxide and carbon dioxide emissions)… the supply costs based on the emissions costs may be computed based on a trading price of an emissions credit based on an amount of emissions, such as but not limited to a trading price of a carbon credit based on CO.sub.x emission (also an economic benefit)…”; also, see [0175], [0355] “…wherein the operating schedule for the energy assets 126 is optimized for reducing energy costs, reducing emissions costs,…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include optimizing/improving the coordination of different set of energy types based on a set of governance parameters relates to carbon generation or emissions as taught by Steven in order to reduce costs and maximize the delivery of energy with respect to cost (see [0027-0028] and [0355]).
As per claim 20, Cella teaches the AI-based platform of claim 1, but it does not explicitly teach wherein the set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios, and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed.
However, Steven teaches a system for improving delivery of energy (see Fig. 28 and [0092] “FIG. 28 shows an example energy storage asset optimization according to a principle described herein”; also, see [0027-0028];) comprising a set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer (see [0010] “These energy assets include energy storage assets, energy consuming assets and energy generating assets. In different examples herein, an energy asset can include an energy storage asset, an energy consuming asset, and/or an energy generating asset”; also, see [0019] “…the mathematical model, which in turn provides as an output a simulated CBL energy profile representing a typical electricity consumption or generation as a function of time, over a given time period T, for the modeled energy asset(s)….”; also, see [0040] “In an example, the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of the model parameters. In this example, the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of a zone temperature of the at least one energy asset, a load schedule of the at least one energy asset, the projected environmental condition, and a control setpoint of the at least one controllable energy asset” ), the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios (see [0027] the simulation is based on different costs or revenue, “the mathematical model for the energy asset(s) and specifies energy-related revenues from one or more wholesale energy markets (e.g., based on forecasted wholesale energy prices over the time period T for the one or more wholesale markets of interest), from which possible revenue may be available to the energy customer. In some examples, the energy-related revenues specified in the objective cost function may take into consideration a simulated customer baseline (CBL) energy profile (discussed above) as a basis for determining such revenue…”; also, see [0030]; also, see [0153], [0164], [0233], [0355], [0381]), and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed (see [0019] and [0023] “Accordingly, for at least the foregoing reasons, a simulated and predictive CBL energy profile, based on a mathematical model of an energy customer's energy asset(s) according to the concepts disclosed herein (rather than an historical actual-use-based CBL as conventionally employed), provides a significant improvement for more accurately determining revenue earned from economic demand response wholesale electricity market…”; see [0027] “the mathematical model for the energy asset(s) is employed to determine a suggested operating schedule over a given time period T for the energy asset(s) (different than the BAU operating schedule) based on a mathematical optimization of an “objective cost function” representing the net energy-related cost to the energy customer for operating the asset(s)”; also, see [0028] “The objective cost function employed in the mathematical optimization to determine a suggested operating schedule for the energy asset(s) also may specify energy-related costs which are offset by the energy-related revenues. In particular, in some examples, the energy-related costs included in the objective cost function may include… emissions-related costs,…”; also, see [0030] and [0033], [0040], [0096]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include the set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios, and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed as taught by Steven in order to improve the energy delivery and consumption of energy assets including consuming energy assets and variety of power generation assets (see [0010] and [0096]-[0097], [0355]).
Claim(s) 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Schmitt et al (US 20210110262, cited in the IDS).
As per claim 9, Cella teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach further comprising an adaptive energy digital twin that represents at least one of, an energy stakeholder entity,
an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition (the term “digital twin” will be interpreted in the BRI as a simulation, virtual model, or replicated model of a process of a machine, system , facility, etc.).
However, Schmitt teaches a system comprising an adaptive energy digital twin that represents (see [0010] and [0051]) at least one of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition (see [0010] “…digital twin simulations can be efficiently used by running a digital twin simulation in parallel to the normal operation of the technical system. Both, the digital twin simulation and the technical system operating in the physical world, share the same environmental input quantities. It is possible to compare sensor measurements from the real, physical technical system to a corresponding output from the digital twin simulator…”; also, see [0051] “The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] CHP or cogeneration, [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy stakeholder entity or an energy distribution resource).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include an adaptive energy digital twin that represents at least one of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
As per claim 10, Cella teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach further comprising an adaptive energy digital twin that is configured to perform at least one of, providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data.
However, further Schmitt teaches a system comprising an adaptive energy digital twin (see [0010] and [0051]) that is configured to perform at least one of, providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data (see [0010] “…digital twin simulations can be efficiently used by running a digital twin simulation in parallel to the normal operation of the technical system. Both, the digital twin simulation and the technical system operating in the physical world, share the same environmental input quantities. It is possible to compare sensor measurements from the real, physical technical system to a corresponding output from the digital twin simulator…”; also, see [0051] “The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] CHP or cogeneration, [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy consumer, thus, the digital twin provides analytic indicator of energy consumption).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include an adaptive energy digital twin that is configured to perform at least one of, providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
As per claim 11, Cella teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach further comprising an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.
However, further Schmitt teaches a system comprising an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet (see [0010], [0051] The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] “ a combined heat and power (CHP or cogeneration) device with sensors providing time-series values for electrical power consumption and thermal power output of the cogeneration device respectively”; [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy consumer, thus, the digital twin provides analytic indicator of energy consumption).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle flee as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
Claim(s) 13-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Forbes Jr et al (US 20210090185, cited in the IDS).
As per claim 13, Cella teaches the AI-based platform of claim 1, while Cella further teaches wherein at least one of the consumption attributes is based on at least one data resource, a satellite data resource, a census, population, demographic, and/or psychographic data resource, a market data resourc