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 .
Allowable Subject Matter Discussion
Claim 16, but for the 35 USC 112(b), 2nd para. rejection, is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The stack generator of the cited prior art does not teach or suggest the inclusion of:
wherein the stack generator is configured to generate the optimized stack by creating a hierarchical deployment specification that includes hardware component selection, software configuration parameters, integration instructions specifying electrical connections and communication interfaces between components, and deployment procedures including installation sequences, calibration steps, and commissioning protocols, wherein the stack generator automatically formats the deployment specification into executable configuration files based on the best configuration selected by the AI predictor
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a data collection module configured to receive application-specific parameters for an energy storage unit (ESU) deployment; 0678: “Data collection modules 283 (e.g., software or hardware, or a mix of hardware and software) that are owned/operated/maintained by either the permissioned third party 297 and/or that are owned/operated/maintained by the third party provider 298 can be deployed at any level, and into any applicable host device.”)
an analysis engine configured to determine intended application, environment, and uses based on the received application-specific parameters; 02685 e.g. “The analysis engine 27-310 contains a machine learning model 27-312 and a predictive analytics model 27-314 for analyzing energy usage patterns and optimizing distribution. The analysis engine may utilize distributed computing architecture to process large datasets efficiently, leveraging cloud resources when necessary for complex computations”
a deployment configurator configured to: access the hardware and software configuration stack, configure a baseline application-specific deployment by selecting and configuring components from the hardware and software configuration stack based on the application-specific parameters, enumerate feasible application-specific deployment options by generating multiple stack configurations, and identify optimal configurations from the feasible application-specific deployment options; 03305 e.g. “The deployment configurator 3212 comprises a baseline config generator 3214, a stack config enumerator 3216, and an optimization identifier 3218. In various embodiments, the deployment configurator provides the core functionality for generating, evaluating, and optimizing energy storage unit configurations based on application requirements and system constraints”)
artificial intelligence (AI) predictor configured to select a best configuration from the optimal configurations; and a stack generator configured to generate an optimized stack for the ESU deployment based on the best configuration selected by the AI predictor.0104 e.g. “n artificial intelligence (AI) predictor configured to select a best configuration from the optimal configurations; and a stack generator configured to generate an optimized stack for the ESU deployment based on the best configuration selected by the AI predictor.”)
a stack generator configured to generate an optimized stack for the ESU deployment based on the best configuration selected by the AI predictor e.g. 003302 “the ESU configuration management system 3200 includes a configuration database 3202, a deployment configurator 3212, an AI predictor 3220, a stack generator 3228, a user interface 3226, a data collection module 3232, and an analysis engine 3230.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “module, analysis engine, deployment configurator, AI generator, and stack generator configured to” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 1-15 and 17-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) a mental process directed to determine intended application, environment, and uses based on the received application-specific parameters; configure a baseline application-specific deployment by selecting and configuring components from the hardware and software configuration stack based on the application-specific parameters, enumerate feasible application-specific deployment options by generating multiple stack configurations, and identify optimal configurations from the feasible application-specific deployment options; an artificial intelligence (AI) predictor configured to select a best configuration from the optimal configurations; and a stack generator configured to generate an optimized stack for the ESU deployment based on the best configuration selected by the AI predictor, claim 1; analysis engine employs machine learning algorithms to determine the intended application, environment, and uses, claim 2; to simulate performance of each generated configuration under various operational scenarios and use simulation results to refine the feasible application-specific deployment options., claim 5; analyze performance metrics, configuration details, and outcomes from previous ESU deployments to identify relevant patterns and trends for generating and evaluating new configurations., claim 6; deep learning algorithms to analyze complex patterns in system behavior and identify one or more precursors to potential reliability issues., claim 7; generate detailed specifications, control parameters, integration instructions, and safety protocols for the optimized stack., claim 8; continuously update the feasible application-specific deployment options based on real-time performance feedback from deployed ESUs and dynamically adjust configuration parameters in response to changing environmental conditions, load patterns, and system performance metrics., claim 10; manage reliability across a network of distributed ESUs by dynamically allocating energy storage and distribution tasks across multiple units., claim 11; dynamically adjust power distribution among connected loads, prioritize critical systems during periods of reduced capacity or increased demand, and consider long-term energy security and resilience when adjusting power distribution., claim 12; optimize energy distribution for both stationary applications and mobile applications., claim 14; analyze real-time energy pricing data and market conditions and participate in demand response programs or energy arbitrage opportunities based on the analysis., claim 17; simulation capabilities using digital twin technologies to create virtual representations of the deployment environment and test different scenarios and configurations before physical deployment., claim 19; adapt to potential future changes in energy policies, regulations, or market structures by incorporating a rules engine that can be updated to reflect new regulatory requirements or pricing structures and using scenario analysis to evaluate…, claim 20; multi-criteria optimization weighs trade-offs between energy density, cycle life, cost, and safety parameters while considering power density specifications, discharge rate capabilities, and operational temperature ranges., claim 21; adapt configurations in real-time based on operational data collected from active ESU deployments, including modifying power distribution strategies, charging algorithms, and load balancing parameters., claim 24; eliminates configurations that fail to meet minimum performance thresholds based on simulation results before presenting optimal configurations to the AI predictor, claim 25; dynamically select cathode nanostructure configurations including agglomerate size distributions and pore density gradients for sulfur confinement, and the AI predictor uses machine learning algorithms to correlate the selected nanostructure parameters with predicted polysulfide shuttle suppression performance and cycle life based on the determined intended application and expected load patterns., claim 28; predictive maintenance capabilities that analyze system performance data to optimize operational efficiency, reduce downtime of energy-related equipment, and schedule maintenance activities based on predicted component degradation patterns., claim 30.
This judicial exception is not integrated into a practical application because the following combination of limitations represent insignificant extra-solution activity:
receive application-specific parameters for an energy storage unit (ESU) deployment; claim 1
configured to display real-time performance metrics, alert operators to potential issues, and include interactive elements that allow stakeholders to explore different scenarios and adjust parameters of the optimized stack., claim 9 ; receive historical deployment data to initialize machine learning models that improve configuration selection accuracy over time through continuous learning from deployment outcomes., claim 23
This judicial exception is not integrated into a practical application because the following combination of limitations generally link the abstract idea to the field of energy management:
a configuration database storing a hardware and software configuration stack; a deployment configurator configured to, claim 1;; application-specific parameters include performance requirements, resource constraints, compatibility specifications, available energy sources, expected load patterns, and regulatory requirements., claim 2; wherein the hardware and software configuration stack includes components for interfaces, control software, guest operating systems, host operating systems, control hardware, and energy storage, claim 4; configured to integrate with external data sources including weather forecasts and grid stability reports to enhance predictive capabilities and make more informed decisions., claim 13; configured to integrate with and optimize energy harvesting technologies including piezoelectric energy harvesting from vibrations and thermoelectric generators that convert waste heat into electricity., claim 15; implements a hybrid deployment strategy that combines behind-the-meter approaches for optimizing energy consumption for individual buildings or facilities with front-of-meter approaches for managing power distribution at the grid level., claim 18; resource constraints include available space, budget limitations, and installation complexity factors that are evaluated during the optimization process., claim 22; configured for deployment in applications requiring Level 2 and Level 3 electric vehicle charging capabilities with dynamic power allocation between different charging levels, claim 26; ESU deployment includes lithium-sulfur battery systems with enhanced energy density and reduced thermal runaway risk compared to conventional battery technologies, wherein the lithium-sulfur battery systems comprise anodes with protective layers and cathodes with nanostructured three-dimensional carbonaceous scaffolds configured to micro-confine sulfur species., claim 27; configured to support both terrestrial applications including data centers and industrial facilities, and space-based applications with battery systems qualified for operation in vacuum, radiation, and temperature extreme environments., claim 29.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because each of the modules, configurators, AI predictor, and stack generator represent mere instructions to apply the abstract idea; the insignificant extra-solution activity limitations described above are well known, conventional, and routine, MPEP 2106.05(d)
Claim Rejections - 35 USC § 102
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 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) 1-3, 5-8, 10-1, 13, 19, and 23-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Henaff (PG/PUB 20220401883).
Claim 1.
Henaff teaches an energy management system, comprising:
a data collection module configured to receive application-specific parameters for an energy storage unit (ESU) deployment (e.g. see predictor as a data collection module, see para [0157]-[0158] - "In some embodiments, the predictor 3614 includes or accesses a data-driven algorithm, such as a machine learning module configured to predict weather, cloud cover and or solar irradiance, such as based on past data. In some embodiments, the predictor 3614 includes a model that includes a data set, such as a data set fed to a machine learning module 3616. As noted, the second controller 3600 is coupled to an electric energy storage unit 3604, such as a rechargeable battery or a capacitor. The term "electric energy storage unit" means any energy storage unit that accepts and provides electric power, regardless how the unit stores energy."; para [0170] - "In some embodiments, goals, constraints and other parameters used by the predictor 3614, the first controller 3500 and/or the second controller 3600 are automatically adjusted by a data-driven control algorithm, such as a machine learning, deep learning or other artificial intelligence module, represented by machine learning module 3616 (FIG. 36)."; para [0189] - "Their deployment off-the-grid or their integration in microgrids has therefore accelerated, incentivized by national policies."; para [0200] - "Consequently, the flexible PV- EDR technology presented herein has the potential to provide access to affordable potable water in remote locations by enabling a new generation of water-treatment systems specifically designed for insufficient-or no-grid access."
an analysis engine configured to determine intended application, environment, and uses based on the received application-specific parameters (e.g. see predictor as the analysis engine, see para [0157]-[0158] and [0170])
a configuration database storing a hardware and software configuration stack (e.g. see tables, data structures, and memory for storing software configuration stack parameters, see para [0226] - "An exemplification of hardware and software control architecture for a PV-EDR system is shown in FIG. 5. It implements in real-time the optimal control strategy created for varying the ED stack voltage and pumping flow rate. Details about the hardware components (brand, model) can be found in Table 1, below."; para [0258]-[0260] - "The control problem can, therefore, be considered as a two-stage decision, detailed as follows... given the power, PED,op(t), provided to the ED system, the optimal ED stack voltage, V(t), and flow rate, Q(t), are computed for maximizing the instant desalination rate."; para [0321] - "Because of the dominant cost of membranes, a cost-optimal constant PVEDR system favors a small number of ED cell pairs, which entails a lower flow rate and longer operation, over a high-flow-rate configuration where water production and solar-power generation could be synchronized. In other words, the cost-optimal ED module for off-grid operation is generally very close to the on-grid optimal configuration."; para [0377] - "Moreover, while embodiments may be described in connection with various illustrative data structures, database schemas and the like, systems may be embodied using a variety of data structures, schemas, etc.");
a deployment configurator (e.g. see predictor as deployment configurator), configured to:
access the hardware and software configuration stack (0225-0226, Figure 45-4500)
configure a baseline application-specific deployment by selecting and configuring components from the hardware and software configuration stack based on the application-specific parameters (Figure 45-4500, see also quantify the improvements enabled by the fully flexible operation created herein, we benchmark it against two reference PV-EDR systems (i.e., a conventional PV-EDR system and a state-of-the-art PV-EDR system) in constant operation, as well as a conventional commercial on-grid RO system."; see also para [0157]-[0158], [0226], [0258]-[0260], [0320] and [0377]
enumerate feasible application-specific deployment options by generating multiple stack configurations and identify optimal configurations from the feasible application-specific deployment option; and an artificial intelligence (AI) predictor configured to select a best configuration from the optimal configurations (Figure 45-4500, see para [0174] - "At 4502, for each combination of component sizes and operational parameters, operation of the system is automatically modeled over a time period. This involves a loop, in which successive combinations are selected, and each combination is modeled for a model period, typically one year to model all seasons, although other time periods can be modeled."; para [0225] - "After creating the relevant control theory for flexible PV-EDR systems, we experimentally validated its feasibility and performance using a full-scale pilot PV-EDR system at the Brackish Groundwater National Desalination Research Facility (BGNDRF) in Alamogordo, N. Mex. In this section, we present the software- and hardware-control architecture that was designed and built for realizing the flexible operation with a time-variant voltage and flow rate. We then describe the full-day experiments that were run for assessing the performance of the flexible PV-EDR under various solar conditions. Finally, we discuss the experimental results in terms of direct solar energy utilization, flexibility of water production, battery savings, and reduction in operation time."; para [0353] - "To this end, we have replaced the machine learning controller 52 of the previous design into two submodules 156 and 158 (FIG. 27). First, a deep-learning algorithm 156 predicts the solar power in the future for a known prediction horizon (e.g., 24 hours). Second, a model predictive controller (MPC) 158 optimizes the system response by predicting the system response in the future (FIG. 28). Both predicted responses 160 and optimal responses 162 are included, along with preceding data 164, for each plot (top-to-bottom: cost function, state of charge, diluate tank level, battery aging, and control action) in FIG. 28."; see also para [0157]-[0158], [0226], [0258]-[0260], [0320] and [0377]
a stack generator configured to generate an optimized stack for the ESU deployment based on the best configuration selected by the AI predictor (e.g. see generating optimal energy storage configurations for deployment based on predictors, see also Figure 45-4500, see para [0157]-[0158], [0174], [0225]-[0226], [0258]-[0260], [0320] and [0377]).
Claim 2. The energy management system of claim 1, wherein the application-specific parameters include performance requirements, resource constraints, compatibility specifications, available energy sources, expected load patterns, and regulatory requirements (para [0124]-[0125] - "Optionally, as illustrated in FIG. 36, at another level, a second controller 3600 divides the time-varying electric power 3502 between the processes 3504-3508 fed by the first level controller 3500 and an energy storage unit 3604, based on a prediction 3608 of future power availability and a function 3610. In the EDR case, power generated by a PV array 3612 is divided between the EDR desalination process and a battery, based on a prediction 3608 of future PV power availability and a function 3610, to ensure reliable water production in the future... Model predictive control (MPC) is a well-known advanced method of process control that is used to control a process while satisfying a set of constraints."; para [0151] - "This feature facilitates system design and performance evaluation, as goals and constraints can be updated on the fly, based on possibly changing or unforeseen needs."; para [0171] - "Furthermore, these concepts, systems and methods are applicable to allocating resources, such water, even if the resource is not used to power a process."; para [0211] - "By combining this operation technique with system-level design optimization to realize a system sized appropriately for an Indian village, this study demonstrates a cost-competitive solution relative to the leading commercial standard for small-scale, grid-powered RO desalination plants."; para [0221] - "Using this strategy, He experimentally validated that actively controlled electrodialysis systems can concomitantly (1) flexibly operate at arbitrary power levels instead of needing to follow the decreasing power-consumption pattern observed in constant operation and (2) maximize desalination performance with reduced batch times."
Claim 3. The energy management system of claim 1, wherein the analysis engine employs machine learning algorithms to determine the intended application, environment, and uses (para [0149] - "Thus, the second controller 3600 is in charge of overall energy management for the system, maximizing solar power use while optimizing battery usage, and the first controller 3500 controls the pump speed and the voltage supply, maximizing water production for the power available to the first controller 3500."; see also para [0157]-[0158], [0170], [0189] and [0200]).
Claim 5. The energy management system of claim 1, wherein the deployment configurator is further configured to simulate performance of each generated configuration under various operational scenarios and use simulation results to refine the feasible application-specific deployment options (para [0157]-[0158], [0170], [0189], [0200] para [0225])
Claim 6. The energy management system of claim 1, wherein the historical data analyzer is configured to analyze performance metrics, configuration details, and outcomes from previous ESU deployments to identify relevant patterns and trends for generating and evaluating new configurations (para [0342] - "Using the yearly historical solar power data, the daily production of the PV-ED system in time-variant operation has been simulated for various daily solar profiles characterized by different total daily solar energy available Esol, and power-profile shapes"; see also para [0221] and [0353])
Claim 7. The energy management system of claim 1, wherein the machine learning engine employs deep learning algorithms to analyze complex patterns in system behavior and identify one or more precursors to potential reliability issues (para [0124] - "In the EDR case, power generated by a PV array 3612 is divided between the EDR desalination process and a battery, based on a prediction 3608 of future PV power availability and a function 3610, to ensure reliable water production in the future. Reliability can be a measure of the likelihood of having sufficient electric power from the PV array 3612 and/or the energy storage unit 3604 to produce a predetermined amount of desalinated water each day."; see also para [0221], [0342] and [0353])
Claim 8. The energy management system of claim 1, wherein the stack generator is configured to generate detailed specifications, control parameters, integration instructions, and safety protocols for the optimized stack ((para [0138] - "The first controller 3500 is configured to automatically allocate, in real time, at least a portion of the time-varying electric power 3502 received at the input port 3510 between respective output ports 3512-3516 of the plurality of output ports, based on respective characteristics of the plurality of processes, so as to maximize instantaneous aggregate production by the plurality of processes 3504-3508, as described herein."; para [0287] - "The performance of the two ML algorithms was assessed on 182 examples for every model, Mi, i=l. 14. FIG. 16 shows the average and standard deviation of the predictionperformance as a function of the time range of the models, Mi, for each of the three model outputs, where the raw performance of the ML prediction algorithms (Random forest and Neural Network) are detailed per output type."; para [0329] - "In each stack segment, y, the current density in the stack must not exceed the limiting-current density above which water splitting occurs in ion-depleted boundary layers, with a safety factor, r."; see also para [0170], [0221], [0342] and [0353])
Claim 10. The energy management system of claim 1, wherein the deployment configurator is configured to continuously update the feasible application-specific deployment options based on real-time performance feedback from deployed ESUs and dynamically adjust configuration parameters in response to changing environmental conditions, load patterns, and system performance metrics ((para [0138] - "The first controller 3500 is configured to automatically allocate, in real time, at least a portion of the time-varying electric power 3502 received at the input port 3510 between respective output ports 3512-3516 of the plurality of output ports, based on respective characteristics of the plurality of processes, so as to maximize instantaneous aggregate production by the plurality of processes 3504-3508, as described herein. Here, "real time" means after performing necessary calculations and before the next time interval for allocating the time-varying electric power 3502"; para [0189] - "We, therefore, investigated the potential benefits of adding a 3-kWh battery to the experimental PV-EDR system, where it was shown that, with foreknowledge of the solar irradiance, the battery can be used to reshape the solar-power profile into an optimal ED-power operating profile that, on average, increases water production by 25 percent compared to the direct-drive. We then implemented a machine-learning algorithm able to decide the optimal ED operating power in real-time, based on current and past irradiance data only"; para [0248]-[0249] - "The voltage step applied to the ED Stack generates a transient in the stack outlet conductivities with a characteristic time smaller than the control time step. The conductivity readings are used by the controller to predict the current, I, using an ED model. refining the control strategy at the beginning of the batch by modeling the transient behavior in the feedforward control or implementing closed-loop control of the power consumption with feedback..."; see also para [0170], [0221], [0225], [0342] and [0353]
Claim 11. The energy management system of claim 1, wherein the system is configured to manage reliability across a network of distributed ESUs by dynamically allocating energy storage and distribution tasks across multiple units ((para [0164] - "In some embodiments, the second controller is configured to allocate the first, second and third portions 3618-3622 so as to maximize the function. In some embodiments, the function is configured to represent reliability of at least one process of the plurality of processes 3504-3508. As noted, reliability can be a measure of the likelihood of having sufficient electric power from the PV array 3612 and/or the energy storage unit 3604 to produce a predetermined amount of desalinated water each day."; see also para [0138], [0124]-[0125] and [0157]-[0158]).
Claim 13. The energy management system of claim 1, wherein the system is configured to integrate with external data sources including weather forecasts and grid stability reports to enhance predictive capabilities and make more informed decisions para [0125] - "Model predictive control (MPC) is a well-known advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics."; para [0155] - "In some embodiments, the predictor 3614 is coupled to a radio antenna 4402 and is configured to receive irradiance forecasts and/or weather forecasts via a radio link, such as forecasts broadcast by the U.S. National Weather Service."; para [0301] - "It also facilitates comparison across technologies, such as RO and ED, that have different balances between capital expense (capex) and operating expense (opex)"; see also para [0138], [0124]-[0125] and [0157]-[0158]).
claim 19. The energy management system of claim 1, wherein the system employs advanced simulation capabilities using digital twin technologies to create virtual representations of the deployment environment and test different scenarios and configurations before physical deployment (para [0172] - "Modeling operation of a system having one or both of the controllers 3500 and/or 3600 facilitates designing a minimal system that meets a production goal. For example, modeling a desalination system for a year of simulated operation shows whether, under the modeled weather conditions, the system operates reliably, i.e., produces sufficient potable water every day."; para [0170], [0221], [0226], [0342] and [0353]
claim 23. The energy management system of claim 1, wherein the AI predictor uses the historical deployment data to initialize machine learning models that improve configuration selection accuracy over time through continuous learning from deployment outcomes (para [0157]-[0158], [0174], [0225]-[0226], [0258]-[0260], [0320] and [0377]
claim 24. The energy management system of claim 1, wherein the system is configured to adapt configurations in real-time based on operational data collected from active ESU deployments, including modifying power distribution strategies, charging algorithms, and load balancing parameters (para [0353] - "Second, a model predictive controller (MPC) 158 optimizes the system response by predicting the system response in the future (FIG. 28). Both predicted responses 160 and optimal responses 162 are included, along with preceding data 164, for each plot (top-to-bottom: cost function, state of charge, diluate tank level, battery aging, and control action) in FIG. 28."; para [0359] - "This higher layer 165 optimizes the system performance by balancing the amount of energy to be used now or stored for use sometime in the future."; para [0371] - "The system further includes a control system configured to control flow rates of the feed liquid through the channels and to control distribution of electrical power from the power module to generate and apply a control voltage to at least one of the electrodes to generate an electrical charge in response to variations in power or an absence of power generated by the power module or to achieve optimized production of product water from the diluate channels."; see also para [0138], [0124]-[0125], [0157]-[0158] and [0164)
claim 25. The energy management system of claim 1, wherein the deployment configurator eliminates configurations that fail to meet minimum performance thresholds based on simulation results before presenting optimal configurations to the AI predictor (para [0145] - "However, using all available power to maximize water production may be suboptimal, from a reliability of water supply viewpoint. For example, although it may be possible to meet a production goal for a given sunny day, if the next day or two are cloudy, it may not be possible to meet the production goal on the second or third day, which can have dire consequences."; para [0355] - "The algorithm can be formally verified, whereas previous development could only be validated statistically. More specifically, it's possible to evaluate whether constraints are satisfied and if the cost function is indeed minimized. It is also easier to verify which submodule of the system is underperforming and take actions to improve it. Furthermore, it is easier to update or perform code maintenance. Variations of the constraints or of the objective function can be updated on the fly. Accuracy of the solar irradiance forecast can be improved when advancements in the field are available."- The system only provides feasible simulation results that meet the performance criteria.; see also para [0138], [0124]-[0125], [0157]-[0158], [0164] and [0353]
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) 4, 9, 12, 22, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over (PG/PUB 20220270123) in view over Shaikh et al. PG/PUB 20220270123 (e.g. Virtual Power Systems)
Claim 4, Massachusetts teaches the energy management system of claim 1, wherein the hardware and software configuration stack includes components (para [0172] - "Modeling various combinations of PV array sizes, electrodialysis stack sizes, operating voltages, pump capacities, battery capacities, and other system component sizes, as well as expected operating conditions, such as weather, shows which combinations of these component sizes reliably meet the production goal under expected weather conditions, and which of these combinations is least expensive."), and energy storage (para [0179] - "At 4514, an electric energy storage unit is modeled. The electric energy storage unit may be the electric energy storage unit 3604 described with reference to FIG. 36.").
Massachusetts does not explicitly teach the interfaces, control software, guest operating systems, host operating systems, control hardware described below. However, Virtual teaches the interfaces, control software, guest operating systems, host operating systems, control hardware described below (para [0029] - "The controller 14 is connected to the devices to provide communication. The communication lines may be a hard line such as an Ethernet network, or another communication mechanism such as a wireless connection. The controller 14 is also configured to provide resources to the devices as needed by the various devices at various times, and subject to the operating procedures disclosed herein"; para [0089] - "It will be understood that a computer may include a computer program product from a computer-readable storage medium and that this medium may be internal or external, removable and replaceable, or fixed. In addition, a computer may include a Basic Input/Output System (BIOS), firmware, an operating system, a database, or the like that may include, interface with, or support the software and hardware described herein.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Virtual for including interfaces, control software, guest operating systems, host operating systems, control hardware, to the teachings of Massachusetts, would achieve an expected and predictable result of enabling the system to simulate an energy storage unit for determining an optimal energy usage using a software stack, as specified by the tenant application (see Virtual para [0029] and [0089]).
Claim 9, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses comprising a user interface configured to display real-time performance metrics (para [0077] - "Each output port of the plurality of output ports 3512-3516 is configured to supply power to a respective associated process 3504-3508 of a plurality of processes. The first controller 3500 is configured to automatically allocate, in real time, at least a portion of the time-varying power 3502 received at the input port 3510 between respective output ports 3512-3516 of the plurality of output ports, based on respective characteristics of the plurality of processes 3504-3508, so as to maximize instantaneous aggregate production by the plurality of processes 3505-3508."; para [0151] - "This feature facilitates system design and performance evaluation, as goals and constraints can be updated on the fly, based on possibly changing or unforeseen needs."; para [0224] - "The simulation of the daily time-variant operation of the PV-EDR system using the flexible control strategy includes plots of (from top to bottom) voltage, flow rate, power, desalination rate, and battery state of charge (SOC), each as a function of time."), to explore different scenarios and adjust parameters of the optimized stack (para [0157]-[0158], [0226], [0258]-[0260], [0320] and [0377]).
Massachusetts does not explicitly disclose alerting operators to potential issues, and include interactive elements that allow stakeholders to explore. However, Virtual does disclose alerting operators to potential issues, and include interactive elements that allow stakeholders (para [0031] - "At 36 the modification is broadcast to one or more devices. The broadcast can also include an offer to the devices that will compensate the device(s) (in reality the compensation reaches the owner/operator of the device and not the device itself) in exchange for their agreement to receive less power."; para [0052] - "Additionally, warnings, alarms, and/or faults can be communicated to the controllers. The warnings can include over-temperature warnings, failed component errors, and the like. In some situations, a controller can shut down or disable an entire rack based on the communicated information from the data rack."; para [0079] - "Information about the various power policies can be shown on a display 1314 connected to the one or more processors 1310. The display can comprise a television monitor, a projector, a computer monitor (including a laptop screen, a tablet screen, a netbook screen, and the like), a cell phone display, a mobile device, or another electronic display.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Virtual for alerting users to potential issues to the system of Massachusetts, as it would allow the system to allow stakeholders to adapt the parameters deploy an optimal energy storage unit, as specified by the tenant application (see Virtual para [0031], [0052] and [0079]).
Claim 12, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses comprising a load balancing module configured to dynamically adjust power distribution among connected load (para [0138], [0124]-[0125] and [0157]-[0158]), and consider long-term energy security and resilience when adjusting power distribution (para [0184] - "Water-related challenges must be tackled urgently, but they cannot be addressed in isolation from their energy requirements; integrated solutions should be implemented for long-term water and energy security."; para [0199] - "Finally, these renewable-powered systems achieve such economic improvements with greatly reduced environmental cost and offer a resilient solution to energy insecurity."; see also para [0138], [0124]-[0125] and [0157]-[0158]). Massachusetts does not explicitly disclose prioritizing critical systems during periods of reduced capacity or increased demand. However, Virtual does disclose prioritizing critical systems during periods of reduced capacity or increased demand (para [0003] - "A typical datacenter houses a vast network of heterogeneous, critical systems, the continuous operation of which is vital to the success of the organization."; para [0053] - "During periods of lower power consumption, the power caches are recharged, enabling them to be used again during a future period of increased power demand and consumption."; para [0077] - "The identifying of datacenter situations, the determining of policy priorities, and the modifying of power arrangements, etc., can be performed by the power policy engine based on several techniques.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings Virtual for prioritizing critical systems during periods of reduced capacity to the system of Massachusetts, would achieve an expected and predictable result of optimizing operations, as specified by the tenant application (see Virtual para [0003], [0053] and [0077]).
Regarding claim 22, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the resource constraints include available space, budget limitations, and installation complexity factors that are evaluated during the optimization process. However, Virtual does disclose wherein the resource constraints include available space, budget limitations and installation complexity factors that are evaluated during the optimization process (para [0023] - "Datacenter power systems are designed to meet the dynamic power needs of large installations of disparate electrical equipment."; para [0030] - "In an example used for illustration, suppose the cost of power rises, and the datacenter wishes to stay on budget and to accomplish this there needs to be a cut in power distribution for the next period of time."; para [0060] - Thus, embodiments include performing the optimizing with an optimization engine."; para [0080] - "The power descriptions can include physical space needs, electrical equipment cooling requirements, maintenance requirements, service histories, etc.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Virtual for evaluating complex parameters to the system of Massachusetts achieve an expected and predictable result of providing an optimal stack based on operational parameters, as specified by the tenant application (see Virtual para [0023], [0030], [0060] and [0080]).
Regarding claim 29, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses wherein the system is configured to support both terrestrial applications including industrial facilities (para [0003] - "The invention relates to control systems, such as industrial control systems, and more particularly to a control system that, at a first level, allocates a time-varying currently available resource, such as photovoltaic electric power, between a plurality of processes to maximize instantaneous aggregate output of the processes, and optionally at a higher level allocates the time-varying currently available resource between the plurality of processes and storage for future use, based on predicted future availability of the resource and a function."), and space-based applications with battery systems qualified for operation in vacuum, radiation, and temperature extreme environments (para [0126] - "For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the moon with minimum fuel expenditure. An optimal controller is a controller that is based on the optimal control theory."; para [0149] - "Thus, the second controller 3600 is in charge of overall energy management for the system, maximizing solar power use while optimizing battery usage, and the first controller 3500 controls the pump speed and the voltage supply, maximizing water production for the power available to the first controller 3500."; para [0202] - "Processes, procedures and phenomena, described below, can occur at ambient pressure (e.g., about 50-120 kPa-for example, about 90-110 kPa) and temperature (e.g., -20 to 50 degrees centigrade - for example, about 10-35 degrees centigrade) unless otherwise specified." - A spacecraft would need a battery that is able to operate in the vacuum of space as well as radiation from the sun.).
Massachusetts does not explicitly disclose including data centers. However, Virtual does disclose including data centers (para [0003] - "The computing and other electrical equipment that is required to support the information technology (IT) operations associated with an organization is housed in facilities called datacenters."; para [0076] - "Software-defined policies are processed 1200 for managing power within a data center.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Virtual to the teachings of Massachusettss would achieve an expected and predictable result of providing an optimized energy storage unit for a plurality of different application locations, as specified by the tenant application (see Virtual para [0003]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over (PG/PUB 20220270123) in view over Virtual Power Systems US 2015/0083196 Alto Prime Photonics, LC (hereinafter 'Prime').
Regarding claim 15, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the system is configured to integrate with and optimize energy harvesting technologies including piezoelectric energy harvesting from vibrations and thermoelectric generators that convert waste heat into electricity. However, Prime does disclose wherein the system is configured to integrate with and optimize energy harvesting technologies including piezoelectric energy harvesting from vibrations and thermoelectric generators that convert waste heat into electricity (para [0120] - "Embodiments of the invention provide a Hybrid Power Harvesting System with improved energy conversion efficiency."; para [0127] - "Optimized thermal to mechanical conversion and high energy density magnetostrictive/PZT mechanical-to-electrical conversion."; para [0179] - "ith regards to energy harvesting for the MTG device, ferromagnetic transducers can convert the oscillating magnetic field into additional strain on the PZT layers of the laminate. In this way both the mechanical energy from vibrations and the oscillating magnetic field are harvested."; para [0195] - "This involves proper design of not only the piezoelectric/magnetostrictive laminate, but also the power conversion circuits needed to condition the power from AC to DC.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the combined teachings would achieve an expected and predictable result of enabling the system to efficiently harvest power using an energy converter, as specified by the tenant application (see Prime para [0120], [0127], [0179] and [0195]).
Claims 14, 17-18, 20, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over (PG/PUB 20220270123) in view over Mcnamara (WO2024173534 LANCIUM LLC (hereinafter 'Lancium').
Regarding claim 14, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses wherein the system is configured to optimize energy distribution(para [0193] - "Particularly described herein is a fully flexible operation of batch photovoltaic (PV) electrodialysis reversal (EDR) systems for desalinating brackish ground water in off-grid communities."; para [0126] - "Optimal control theory is a well-known branch of mathematical optimization that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in both science and engineering. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the moon with minimum fuel expenditure."; see also para [0170], [0221], [0226], [0342] and [0353]), and mobile applications (para [0126]). Massachusetts does not explicitly disclose stationary applications. However, Lancium does disclose stationary applications (para [0156] - "For example, the flexible datacenter 500 may be situated within a building or another type of stationary environment.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings Lancium to the system of Massachusetts would achieve an expected and predictable result of enabling the system to create a configuration that is optimal for a variety of applications, a specified by the tenant application (see Lancium para [0156]).
Regarding claim 17, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the system is configured to analyze real-time energy pricing data and market conditions and participate in demand response programs or energy arbitrage opportunities based on the analysis.
However, Lancium, does disclose wherein the system is configured to analyze real-time energy pricing data and market conditions and participate in demand response programs or energy arbitrage opportunities based on the analysis (para [0050] - "Among other benefits, the load center including the charging station in conjunction with a controlled load can provide controlled granular load ramping that allows a local station to avoid negative power market pricing and to respond quickly to grid directives."; para [0087] - "These algorithms may utilize past and current information in real-time to manage operations of the different components."; para [0089] - "In some examples, the information can include the cost for power from available sources (e.g., BTM power at the generation station 202 versus metered grid power) to enable comparisons to be made as to which power source costs less. In some instances, the information may include historic prices for power to enable the remote master control system 262 or another system to predict potential future prices in similar situations (e.g., the cost of power tends to trend upwards for grid power during warmer weather and peak-use hours)."; para [0160] - "The conditions may correspond to economic conditions (e.g., cost for power, aspects of computational operations to be performed), power-related conditions (e.g., availability of the power, the sources offering power), demand response, and/or weather-related conditions, among others.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Lancium to the system of Massachusetts would achieve an expected and predictable result of allowing the system to optimize an energy storage system and market pricing data, as specified by the tenant application (see Lancium para [0050], [0087], [0089] and [0160]).
Regarding claim 18, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the system implements a hybrid deployment strategy that combines behind-the-meter approaches for optimizing energy consumption for individual buildings or facilities with front-of-meter approaches for managing power distribution at the grid level. However, Lancium does disclose wherein the system implements a hybrid deployment strategy that combines behind-the-meter approaches for optimizing energy consumption for individual buildings or facilities with front-of-meter approaches for managing power distribution at the grid level (para [0045] - "The embodiments provided herein relate to providing a load center including an electric vehicle charging station combined with a controllable load, such as one or more computing devices capable of ramping up or ramping down power consumption in a controlled fashion"; para [0051] - "For example, one or more blockchain miners, or groups of blockchain miners, in an array may be turned on or off. In another embodiment, control systems can direct time- insensitive computational tasks to computational hardware, such as CPUs and GPUs, sited behind the meter, while other hardware is sited in front of the meter and possibly remote from the behind-the-meter hardware"; para [0156] - "For example, the flexible datacenter 500 may be situated within a building or another type of stationary environment."; para [0172] - "The datacenter control system 504 may disable 714 the power input system 502 from providing power (e.g., three-phase nominal AC voltage) to the power distribution system 506 to power down the computing systems 512 while the datacenter control system 504 remains powered and is capable of returning service to operating mode at the flexible datacenter 500 when behind-the-meter power becomes available again.").
One of ordinary skill in the art before the effective filing date of the claimed invention applying a pertinent function of implementing a hybrid deployment strategy that combines behind-the-meter approaches for optimizing energy consumption for individual buildings or facilities with front-of-meter approaches for managing power distribution at the grid level, as per Lancium to the system of Massachusetts, would achieve an expected and predictable result of providing an optimal energy consumption based on power availability, as specified by the tenant application (see Lancium para [0045], [0051], [0156] and [0172]).
Regarding claim 20, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose herein the system is configured to adapt to potential future changes in energy policies, regulations, or market structures by incorporating a rules engine that can be updated to reflect new regulatory requirements or pricing structures and using scenario analysis to evaluate potential future changes. However, Lancium does disclose wherein the system is configured to adapt to potential future changes in energy policies, regulations, or market structures by incorporating a rules engine that can be updated to reflect new regulatory requirements or pricing structures and using scenario analysis to evaluate potential future changes (para [0051] - "Any parallel computing processes, such as Monte Carlo simulations, batch processing of financial transactions, graphics rendering, and oil and gas field simulation models are all good candidates for such interruptible computational operations."; para [0092] - "The information may be updated in-real time and used to make the different operational decisions within the arrangement of Figure 2. For instance, the information may help a component (e.g., the remote master control system 262 or a control system at a flexible datacenter 220) determine when to ramp up or ramp down power use at a flexible datacenter 220 or when to switch one or more computing systems at a flexible datacenter 220 into a low power mode or to operate at a different frequency, among other operational adjustments."; see also para [0050], [0087], [0089] and [0160]).
One of ordinary skill in the art before the effective filing date of the claimed inventio incorporating a rules engine that can be updated to reflect new regulatory requirements or pricing structures and using scenario analysis to evaluate potential future changes, as taught by Lancium to the system of Massachusetts, would achieve an expected and predictable result via modifying operational parameters to provide the efficient uses of an energy storage system, as specified by the tenant application (see Lancium para [0050]-[0051], [0087], [0089], [0092] and [0160]).
Regarding claim 26, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the system is configured for deployment in applications requiring Level 2 and Level 3 electric vehicle charging capabilities with dynamic power allocation between different charging levels. However, Lancium does disclose wherein the system is configured for deployment in applications requiring Level 2 and Level 3 electric vehicle charging capabilities with dynamic power allocation between different charging levels (para [0049] - "Power may be dynamically routed to both the charging station and the computing system load based on power market conditions and/or power demands of the charging station and the computing system load."; para [0095] - "The remote master control system 262 may be capable of directing one or more flexible datacenters 220 to ramp-up or ramp-down to desired power consumption levels, and/or to control cooperative action of multiple flexible datacenters by determining how to power each individual flexible datacenter 220 in accordance with operational directives (e.g., instructions)."; para [0103] - "As such, the load center 302 may obtain generated power from the generation station 202 and provide the generated power to electric vehicles via the electric vehicle (EV) charging station 306."; para [0207] - "For example, a vehicle using DC fast charging may change the amount of power consumed over time based on the battery charge level (e.g., reducing power consumption as the battery becomes full or otherwise changing the rate of charging)." - The system may dynamically allocate energy based on market data. The levels may be a plurality of difference charge levels, including a level 2 or level 3.).
One of ordinary skill in the art before the effective filing date of the claimed invention recognizing that the deployment in applications requiring Level 2 and Level 3 electric vehicle charging capabilities with dynamic power allocation between different charging levels, as taught by Lancium to the system of Massachusetts, as achieve an expected and predictable result of optimizing a system to efficiently charge an electric vehicle, as specified by the tenant application (see Lancium para [0049], [0095], [0103] and [0207]).
Claims 21, 27-28 are rejected under 35 USC 101 as being unpatentable over Massachusetts in view of US 2023/0035506 Al to LytEn, Inc. (hereinafter 'Lyten').
Regarding claim 21, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses wherein the multi-criteria optimization weighs trade-offs (para [0253] - "In the rest of this section, we investigate how to adapt the flexible control strategy for PV-EDR systems in the presence of a battery in order to optimally use its capacity. We address the feasibility of implementing this new control strategy in practice by presenting a weather-prediction algorithm, and discuss the benefits in terms of water production and the trade-off between increased capital cost and reduced operating cost."; para [0362] - "The controller numerically finds the optimal sequence of control actions (i.e., what the algorithm can control-power threshold for the stack, battery charge, and battery discharge power) for a 24-hour horizon by predicting the response of the system."), and operational temperature ranges (para [0202] - "Processes, procedures and phenomena, described below, can occur at ambient pressure (e.g., about 50-120 kPa- for example, about 90-110 kPa) and temperature (e.g., -20 to 50 degrees centigrade -for example, about 10-35 degrees centigrade) unless otherwise specified."). Massachusetts does not explicitly disclose energy density, cycle life, cost, and safety parameters while considering power density specifications, discharge rate capabilities. However, Lyten does disclose energy density, cycle life, cost, and safety parameters while considering power density specifications, discharge rate capabilities (para [0098] - "Certain types of batteries, such as lithium-ion or lithium-sulfur batteries, may be limited in performance by the type of electrolyte used or by uncontrolled battery side reactions. As a result, optimization of the electrolyte may improve the cyclability, the specific discharge capacity, the discharge capacity retention, the safety, and the lifespan of a respective battery."; para [0100] - "In some implementations, the porosity of a carbonaceous cathode may be adjusted to achieve a desired balance between maximizing energy density and inhibiting the migration of polysulfides into and/or throughout the battery's electrolyte."; para [0287] - "Reducing lithium-containing dendritic growth from the anode may, in turn, increase the charge rate, the discharge rate, the energy density, the cycle life of the battery 2800, or any combination thereof.").
One of ordinary skill in the art before the effective filing date of the claimed invention considering power density specifications, discharge rate capabilities, as taught by Layten to the system of Massachusetts, would achieve an expected and predictable result of allowing the system to compare operational parameters to determine an optimal system for the ESU, as specified by the tenant application (see Layten para [0098], [0100] and [0287]).
Regarding claim 27, Massachusetts discloses the energy management system of claim 1. Massachusetts does not explicitly disclose wherein the ESU deployment includes lithium-sulfur battery systems with enhanced energy density and reduced thermal runaway risk compared to conventional battery technologies, wherein the lithium-sulfur battery systems comprise anodes with protective layers and cathodes with nanostructured three-dimensional carbonaceous scaffolds configured to micro-confine sulfur species.
However, Lyten does disclose wherein the ESU deployment includes lithium-sulfur battery systems with enhanced energy density and reduced thermal runaway risk compared to conventional battery technologies, wherein the lithium-sulfur battery systems comprise anodes with protective layers and cathodes with nanostructured three-dimensional carbonaceous scaffolds configured to micro-confine sulfur species (para [0100]-[0101] - "In some implementations, the porosity of a carbonaceous cathode may be adjusted to achieve a desired balance between maximizing energy density and inhibiting the migration of polysulfides into and/or throughout the battery's electrolyte... In addition, or in the alternative, one or more protective layers or regions can be added to surfaces of the cathode and/or the anode exposed to the electrolyte to adjust cathode porosity levels... Various aspects of the subject matter disclosed herein relate to a lithium-sulfur battery including a liquid-phase electrolyte, which may include a ternary solvent package and one or more additives. In some implementations, the lithium-sulfur battery may include a cathode, an anode positioned opposite to the cathode, and an electrolyte."; para [0108] - "In addition, or the alternative, one or more protective layers, sheaths, films, and/or regions (collectively referred to herein as "protective layers") may be disposed on the anode and/or the cathode and/or the separator and in contact with the electrolyte."; para [0202] - "The anode 702 may be formed as an alkali metal layer having one or more exposed surfaces that include any number of alkali metal-containing nanostructures or microstructures."; para [0274] - "Similar to the various other lithium-sulfur battery configurations disclosed herein, the battery 2600A may generate undesirable lithium-containing polysulfide species (not shown in FIG. 26A for simplicity) during operational discharge-charge cycling of the battery 2600A."; para [0346] - "Example applications for the disclosed battery pack 4300 include providing power to electric vehicles (EVs), portable electronic devices, aerospace applications, and energy storage systems.").
One of ordinary skill in the art before the effective filing date of the claimed invention recognizing the ESU deployment includes lithium-sulfur battery systems with enhanced energy density and reduced thermal runaway risk compared to conventional battery technologies, wherein the lithium-sulfur battery systems comprise anodes with protective layers and cathodes with nanostructured three-dimensional carbonaceous scaffolds configured to micro-confine sulfur species as it would allow the system to determine an optimal performance for a ESU battery, as specified by the tenant application (see Layten para [0100]-[0101], [0108], [0202], [0274] and [0346]).
Regarding claim 28, Massachusetts discloses the energy management system of claim 1. Massachusetts further discloses the AI predictor uses machine learning algorithms to correlate (para [0280] - "We investigated primarily machine learning (ML) algorithms for this prediction task."), based on the determined intended application and expected load patterns (para [0210] - "This methodology implements real-time control of the ED load coupled with PV power and flexibly achievable load shaping while maintaining target desalination performance and water production."; para [0221] - "Using this strategy, He experimentally validated that actively controlled electrodialysis systems can concomitantly (1) flexibly operate at arbitrary power levels instead of needing to follow the decreasing power-consumption pattern observed in constant operation and (2) maximize desalination performance with reduced batch times.").
Massachusetts does not explicitly disclose wherein the deployment configurator is configured to dynamically select cathode nanostructure configurations including agglomerate size distributions and pore density gradients for sulfur confinement, the selected nanostructure parameters with predicted polysulfide shuttle suppression performance and cycle life. However, Lyten does disclose wherein the deployment configurator is configured to dynamically select cathode nanostructure configurations including agglomerate size distributions and pore density gradients for sulfur confinement (para [0116] - "The plurality of agglomerates is disposed on one or both surfaces of a current collector and the plurality of agglomerates has various sizes determined based on the thickness of the cathode. In some instances, a relatively large agglomerate may have a diameter that is at a certain ratio of the thickness of the cathode, and a relatively small agglomerate may have a diameter that is approximately one third(?) of the diameter of the relatively large agglomerate."; para [0185] - "In some implementations, the graded layer 514 may include various distinct types and/or forms of carbon and/or carbonaceous materials, each having one or more physical attributes that can be selected or configured to adjust the reactivity of carbon with contaminants (such as polysulfides) present in the electrolyte 540 and/or the anode 502. In some aspects, the selectable physical attributes may include (but are not limited to) porosity, surface area, surface functionalization, or electric conductivity."; para [0258] - "In addition, the macropores 2218 may be grouped into first macropores that may have a first pore density, and second macropores (both not shown in FIG. 22 for simplicity) that may have a second pore density different than the first pore density."). the selected nanostructure parameters with predicted polysulfide shuttle suppression performance and cycle life (para [0169] - "For example, in one implementation, the protective lattice 402 may serve as sheath on exposed surfaces of the cathode and bind with polysulfides to prevent their migration and diffusion into the electrolyte 130. In this way, aspects of the subject matter disclosed herein may prevent (or at least reduce) battery capacity decay by suppressing the polysulfide shuttle effect."; see also para [0202] and [0287]).
One of ordinary skill in the art before the effective filing date of the claimed invention recognizing the deployment configurator is configured to dynamically select cathode nanostructure configurations including agglomerate size distributions and pore density gradients for sulfur confinement, the selected nanostructure parameters with predicted polysulfide shuttle suppression performance and cycle life, as taught by Layten to the system of Massachusetts, would achieve an expected and predictable result of determining an optimal performance for a ESU battery, as specified by the tenant application (see Layten para [0116], [0169], [0185], [0202], [0258] and [0287]).
Claim 30 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Henaff (PG/PUB 20220401883) in view over Wang (PG/PUB 20220245297)
Claim 30. The energy management system of claim 1 but does not tach the maintenance limitations described below. Wang teaches the maintenance limitations described below
wherein the system incorporates predictive maintenance capabilities that analyze system performance data to optimize operational efficiency, reduce downtime of energy-related equipment, and schedule maintenance activities based on predicted component degradation patterns (0209 e.g. see downtime minimization based on predictive maintenance optimizing efficiency, “As such, technology has provided improvements to maintenance and reduction of failures in renewable energy assets, however, the same technology creates technical problems in addressing the failures in a manner that corrects the problem and reduces downtime. Systems and methods discussed herein not only predict failures using SCADA information and historical information to train multiple models with different observation windows and lead windows, but may enable predictions of components with an improved degree of accuracy such that the predicted failure may be addressed in a manner that reduces downtime that otherwise would have resulted in lost power, damage to infrastructure, poor client service, and possible power loss (which may impact infrastructure and lives), see also patterns of degradation, 0004 e.g. “the geographical location of the wind turbines of the subset of wind turbines being within the wind turbine asset data, generating an event and alarm vendor agnostic representation of the event and alarm data creating a feature matrix, wherein the feature matrix includes a unique feature identifier for each feature of the event and alarm data and one or more features from the event and alarm data, extracting patterns of events based on the feature matrix, receiving first historical sensor data of the first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of the any number of renewable energy assets, the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset during the first time period, generating a first set of failure prediction models using the first historical sensor data and the patterns of events, each of the first set of failure prediction models being trained using different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated,,” see also predictive maintenance of energy-elated equipment, [0147] FIG. 10 depicts a calculation of an example predictive maintenance cost in one example. In FIG. 10, the Number of Cases for Failure may be a false negative rate times the total number of cases. The number of cases of preventative fixes may be a true positive rate times the total number of cases. The number of cases of unnecessary visits may be a false positive rate times the total number of cases,” 0146, ABSTRACT)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Wang for implementing predictive maintenance to the teachings of Henaff, namely optimizing energy storage operation, would achieve an expected and predictable result via combining said elements using known methods. Wang is reasonably pertinent to a problem of optimizing energy performance and would commend itself to the energy storage stack of Henaff, ABSTRACT, summary of invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20250326327, see claim 1 relevancy to stack, ABSTRACT “An electrical storage system comprises a first energy storage system and a second energy storage system having a lower electrical energy density and a higher rated electrical power output capability than the first energy storage system, at least one electrical power sensor configured to sense over a plurality of time intervals, electrical power usage information for a load electrically coupled to the first energy storage system and the second energy storage system, and at least one computer processor programmed to determine based, at least in part, on the sensed electrical power usage information and a power requirement of the load in a current time interval, charging/discharging parameters for each of the first energy storage system and the second energy storage system, and control charging/discharging of each of the first and second energy storage systems in accordance with the determined charging/discharging parameters during the current time interval.”
20240426918, see battery design optimization in light of the stack optimization of claim 1, A method for optimising battery design comprising receiving one or more battery performance targets indicating design constraints on one or more battery performance characteristics, applying one or more trained machine learning models to search and determine one or more optimal battery designs. The machine learning model(s) adapted to replace one or more replace complex computing intensive battery design physical/mechanical models for creating a plurality of battery designs complying with the design constraint(s) and compute their performance scores are trained to learn the physical/mechanical model(s), created using experimental data collected for a plurality of batteries, using a plurality of training samples extracted from simulations of the physical/mechanical model(s).ABSTRACT
20050055110, see determining optimal product design in light of the energy storage stack of claim 1, A process simulation system that simulates the operation of the manufacturing and measurement systems used to produce and measure the articles being analyzed relative to engineering design targets, engineering design tolerances, producibility and/or quality. In one embodiment, the user is able to assess, without risk or production cost while accelerating speed-to-market, the effect of contemplated changes (i.) to engineering design targets, (ii.) to engineering design tolerances, (iii.) to tooling, (iv.) to part pre-process dimensions and (v.) to the measurement system—on manufactured part dimensions, producibility and quality (i.) without modifying tooling, (ii.) without changing part pre-process dimensions, (iii.) without producing new parts, (iv.) without measuring article characteristics on the new parts and (v.) without changing the measurement system. The simulation functionalities, according to embodiments of the present invention, enable the user to verify whether or not the contemplated changes will have the desired effect without incurring the time and expense involved in actually making the changes, producing parts, measuring part characteristics, changing the measurement system and then determining whether the changes accomplished the desired objectives
20200241564 – 0023 e.g. “In some examples, the dispatch system learns about the capabilities of different types of autonomous vehicles using vehicle capability data provided by the manufacturer or other party associated with a type of autonomous vehicle. In some examples, the vehicle capability data is referred to as operational domain (OD) or operational design domain (ODD) data. ODD data may describe particular capabilities of an autonomous vehicle type”)
20240070349, see optimal energy storage design, ABSTRACT, summary of invention, e.g. “A computer-implemented method and corresponding system perform generative design of an energy storage device. The method automatically builds at least one model of the energy storage device. The building is based on a design parameter space and employs a machine learning process. The method automatically performs a simulation of the energy storage device using the design parameter space, a design evaluation space, and the at least one model built. The performing produces at least one prediction. The method automatically evolves at least one of (i) the design parameter space and (ii) the design evaluation space. In an event the at least one prediction indicates that a product design objective or model design objective has been achieved, the method automatically converges on the design parameter space evolved, thereby completing a generative design of the energy storage device and, otherwise, repeats the building, performing, and evolving.:
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
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