Patent Application 17901446 - SYSTEMS AND METHODS FOR HYDROGEN ENERGY AND

Title: SYSTEMS AND METHODS FOR HYDROGEN ENERGY AND ENERGY AGGREGATION
Application Information

Invention Title: SYSTEMS AND METHODS FOR HYDROGEN ENERGY AND ENERGY AGGREGATION
Application Number: 17901446
Submission Date: 2025-04-08T00:00:00.000Z
Effective Filing Date: 2022-09-01T00:00:00.000Z
Filing Date: 2022-09-01T00:00:00.000Z
National Class: 700
National Sub-Class: 297000
Examiner Employee Number: 87683
Art Unit: 2118
Tech Center: 2100
Rejection Summary

102 Rejections: 0
103 Rejections: 12
Cited Patents

The following patents were cited in the rejection:
Office Action Text



    Detailed Act

The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 

This action is responsive to the communications filed 2/13/2025. As per the claims filed 9/01/2022
Claims 1-16 are pending.
Claim(s) 1, 6, 11 is/are independent claim(s).

Note Regarding Prior Art

Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. 

Note Regarding AIA  Status

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 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.  

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) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Christopher N. Allo (US PG Pub No. US 2021/0104764; Published: 04/08/2021)(hereinafter Allo) in view of Michael Strizki (US PG Pub No. US 2009/0189445; Published: 07/30/2009)(hereinafter: Strizki).

Claim 1:
As per independent claim 1, Allo discloses a hydrogen storage assembly comprising: 
an enclosure substantially encompassing an electrolyzer [[0034] The input power is supplied to an electrolyzer, also sometimes referred to as a hydrolyzer, which operates upon a supply of water (H.sub.2O) from a water source to generate a stream of hydrogen (H.sub.2) gas.], 
a hydrogen storage system [[0052] The tank 132 is a standard hydrogen-compatible, pressurized tank and is easily sized to provide sufficient energy storage capacity for the needs of the system] 
a hydrogen fuel cell [[0054] A hydrogen fuel cell 134 operates to take the hydrogen (either in-stream or from the tank) and essentially perform the reverse operation of the electrolyzer.] 
a power conversion system[[0089] The 240V AC output from the inverter 206 is directed (via a 240V AC bus 316) to some elements of the system rack 202, including the conditioner 214, the electrolyzer 216 and the control electronics 204. A separate transformer/power supply (not separately depicted) can be utilized to generate the necessary low voltages (e.g., 3 VDC, 5 VDC, 12 VDC, etc.) used by the control electronics and 
a control system[[0048] The input power is provided at a suitable voltage level as a power input]; 
wherein the electrolyzer is configured to separate, via electrolysis, water into hydrogen gas that is stored in the hydrogen storage system [[0034] The input power is supplied to an electrolyzer, also sometimes referred to as a hydrolyzer, which operates upon a supply of water (H.sub.2O) from a water source to generate a stream of hydrogen (H.sub.2) gas…[0051] The stream of dry hydrogen gas is directed to a hydrogen tank 132]; 
the hydrogen fuel cell is configured to convert the stored hydrogen gas into electrical energy and water [[0054] A hydrogen fuel cell 134 operates to take the hydrogen (either in-stream or from the tank) and essentially perform the reverse operation of the electrolyzer. That is, the fuel cell operates to take oxygen from the surrounding atmosphere and reactively combines this with the hydrogen to generate electrical power and water (condensate)]. 
the power conversion system is configured to convert the produced electrical energy to a desired form [[0089] The 240V AC output from the inverter 206 is directed (via a 240V AC bus 316) to some elements of the system rack 202, including the conditioner 214, the electrolyzer 216 and the control electronics 204. A separate transformer/power supply (not separately depicted) can be utilized to generate the necessary low voltages (e.g., 3 VDC, 5 VDC, 12 VDC, etc.) used by the control electronics] and 
the control system is configured to to control the storage and distribution of the stored hydrogen and electrical energy in an optimized manner to achieve predefined financial and energy-use objectives [[0038] The controller performs various functions including monitoring load needs over time, adjusting the rate of operation of the various elements to meet both the current and anticipated future needs of the system, and, as required, switching in or out the application of other sources of electrical power…[0068] The controller can take the necessary steps to either accumulate sufficient charged hydrogen to meet this need, or make other arrangements such as activating the transfer switch to allow the lower power grid to supply the necessary additional power to meet this new load requirement.].
Allo disclosed the hydrogen storage assembly capable of charging battery based equipment and vehicles but failed to specifically disclose an electrochemical energy storage module, the electrochemical energy storage module is configured to function as an energy buffer.
Strizki, in the same field of renewable energy management and storage discloses this limitation in that [[0029] the present invention is also capable of storing energy by charging a battery bank or ultracapacitors. The battery bank or ultracapacitors can also be used in conjunction with the hydrogen energy storage system [0031] Energy storage subsystems components, such as an electrochemical battery bank 160, an ultracapacitor bank (not shown), or hydrogen storage tanks 170, can be housed in one or more separate enclosures, readily connectible to the rest of the system 100].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the hydrogen storage assembly of Allo to include an electrochemical energy storage module, the electrochemical energy storage module is configured to function as an energy buffer as disclosed by Strizki. The motivation for doing so would have been to be able to schedule the hydrogen storage system to take advantage of the lower, off-peak rates (0029)

Claim(s) 2, 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo and Strizki in view of Sugumar Murugesan et al (US PG Pub No. US2021/0056452; Published: 02/25/2021)(hereinafter: Murugesan).
Claim 2: 
As per claim 2, which depends on claim 1, Allo and Strizki disclose wherein the dataset includes at least one of irradiance data, meteorological data, and real time energy consumption data. Allo [[0065] Still other information can be supplied as desired, such as the status of solar panels, weather forecast information, etc. The controller is adaptive and can utilize outside information to help predict and adaptively configure the system. This enables the system to be autonomous to the user. For example, if the National Weather Service predicts a large snowfall (or other inclement weather) that may affect the ability of the solar panels to generate power in the coming days, additional steps may be taken to accumulate higher than normal levels of hydrogen].
Allo and Strizki failed to specifically disclose wherein the control system provides energy forecasting by using a Long Short-Term memory (LSTM) model with a gating mechanism.
Murugesan, in the same field of power distribution and prediction models discloses this limitation in that [[0044] building control software uses predictive models, [0045] a long-short term memory (LSTM) sequence to sequence (S2S) neural network (a type of RNN) and/or any other type of RNN (e.g., a gated recurrent unit (GRU) neural network) can be utilized by the building system as the prediction model, i.e., to predict a particular point forecast and/or probabilistic forecast of a data point. [0047] During model training, a probabilistic forecast in one time-step can be modified by the building system to represent actual energy consumption in that time-step, and the modified probabilistic forecast (the actual probabilistic forecast) can be fed-back into the LSTM S2S neural network in the next time-step to obtain probabilistic forecast in that time-step. More specifically, the building system can be configured to generate a probability distribution representing an actual energy value and the generated probability distribution can be fed into a decoder of the LSTM S2S neural network in one or more next steps during training of the LSTM S2S neural network.].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the forecasting controller of Allo and Strizki to provide energy forecasting by using a Long Short-Term memory (LSTM) model with a gating mechanism as disclosed by Murugesan. The motivation for doing so would have been to provide energy controllers that are both cost efficient and able to conform to future deviations (0002).

Claim 5:
As per claim 5, which depends on claim 1, Allo and Strizki disclose Neural networks, AI, machine learning systems, etc. can be incorporated locally or remotely to provide the requisite control as desired (Allo, [0107]). Allo and Strizki failed to specifically disclose further including an analytics system communicatively coupled to the control system, the analytics system configured to employ one or more machine learning models to aggregate status and energy data received from the control system.
Murugesan, in the same field of power distribution and prediction models discloses this limitation in that [[0047] During model training, a probabilistic forecast in one time-step can be modified by the building system to represent actual energy consumption in that time-step, and the modified probabilistic forecast (the actual probabilistic forecast) can be fed-back into the LSTM S2S neural network in the next time-step to obtain probabilistic forecast in that time-step. [0088] Referring more particularly to FIG. 8, the LSTM S2S neural network 700, during the inference phase, generates an energy forecast for a building for use in operating equipment of a building or providing metrics to a user.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo and Strizki to employ one or more machine learning models to aggregate status and energy data received from the control system as disclosed by Murugesan. The motivation for doing so would have been to provide energy controllers that are both cost efficient and able to conform to future deviations (0002).

Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo Strizki and Murugesan in view of Sam West et al (US PG Pub No. 2019/0158011; Published: 05/23/2019)(hereinafter: West).

Claim 3: 
As per claim 3, which depends on claim 2, Allo, Strizki and Murugesan disclose the controller predicting future status of the solar panels due to weather events (see Allo [0065]). However, Allo, Strizki and Murugesan failed to specifically disclose wherein the control system predicts the direct normal irradiance (DNI) from a solar panel.
West, in the same field of solar power forecasting discloses this limitation in that [[0154] Determining a predicted solar irradiance value may comprise determining a predicted direct normal irradiance for individual collectors comprising heliostats, linear Fresnel, parabolic trough, or dish collectors in a solar thermal facility, and the method may comprise automated or manual control to optimise plant output, short term operability or long term operability].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the forecasting controller of Allo, Strizki and Murugesan to predict the direct normal irradiance (DNI) from a solar panel as disclosed by West. The motivation for doing so would have been to optimize operation of the device by facilitating prediction of solar irradiance.

Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo and Strizki, Murugesan and West in view of Dabeerruddin Syed et al. (Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition; Published: 04/08/2021)(hereinafter: Syed).

Claim 4: 
As per claim 4, which depends on claim 3, Allo, Strizki, Murugesan and West disclose a machine learning model to improve energy generation and consumption (Murugesan). However, the combination of references failed to specifically disclose wherein the control system uses at least one of a min-max scaler and lagged features to improve the accuracy of a model.
Syed, in the same field of deep-learning based load forecasting discloses this limitation in that [[page 2, introduction] The energy consumption may vary from one location to another owing to different weather and climate conditions. And for the same reason, the energy demand may vary on different days of the week and at different times of the day…. Finally, the deep learning models, including Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM), are employed to train and generate predictions. [page 6 – a: DATA NORMALIZATION] For accurate and efficient learning of machine learning algorithms, it is required that all the attributes have the same numerical contribution and variance in the same order. If one attribute has variance much larger than another attribute, then it dominates whilst learning the objective function. To incorporate a non-distorting scaler, Minimum-Maximum (min-max) Scaler has been utilized in this work].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the forecasting controller of Allo, Strizki, Murugesan and West to use at least one of a min-max scaler and lagged features to improve the accuracy of a model as disclosed by Syed. The motivation for doing so would have been to normalize data prior to model fitting in order to prevent bias as known in the art.

Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo in view of Murugesan.

Claim 6:
As per independent claim 6, Allo discloses an energy aggregation system comprising: 
a plurality of network communication interfaces, each coupled to a respective hydrogen storage assembly associated with a customer, wherein each hydrogen storage assembly includes a control system [[0101] As described previously, the system can operate using a knowledge base that monitors, tracks and optimizes the operation of the hydrogen generation, storage and utilization processes. The local system 200 thus includes a local controller 336 (similar to the controller described above) that receives various parametric inputs from various sensors (collectively denoted at 338). In some embodiments, these and other values may be communicated to the server 332 via the network 334.] 
an analytics system communicatively coupled to the plurality of network communication interfaces [[0064] The controller operates to collect and apply analytics associated with the operation of the system. As explained below, the analytics and other information can be stored locally as well as remotely, can be displayed on a user device, etc. [0102] The server 332 may include a server controller 340, which processes the data received from the system 200. This can include storage of the system data in a history database structure 342 in server memory, and analysis of the data using an analysis engine 344], 
a network operations center communicatively coupled to the analytics system [0100] FIG. 16 shows a functional block representation of a data communication and processing system 330 in accordance with further embodiments. In FIG. 16, the system 200 is connected to a server 332 using a suitable network 334. The server 332 can be a remote server geographically distant from the system 200, such as in a data center or other cloud based environment. The server 332 can additionally or alternatively be located locally. Hence, the network 334 can comprise a local area network, a wide area network, a wireless network, the Internet, etc. or any combination of these or other configurations.[0102] analysis engine].
Allo failed to specifically disclose the analytics system configured to employ one or more machine learning models to aggregate status and energy data received from the control system; and
Murugesan, in the same field of power distribution and prediction models discloses this limitation in that [[0047] During model training, a probabilistic forecast in one time-step can be modified by the building system to represent actual energy consumption in that time-step, and the modified probabilistic forecast (the actual probabilistic forecast) can be fed-back into the LSTM S2S neural network in the next time-step to obtain probabilistic forecast in that time-step. [0088] Referring more particularly to FIG. 8, the LSTM S2S neural network 700, during the inference phase, generates an energy forecast for a building for use in operating equipment of a building or providing metrics to a user.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo to employ one or more machine learning models to aggregate status and energy data received from the control system as disclosed by Murugesan. The motivation for doing so would have been to provide energy controllers that are both cost efficient and able to conform to future deviations (0002).

Claim(s) 7-8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo and Murugesan in view of Yuki Kudo et al. (US PG Pub No.2022/0122164; Priority: 10/16/2020)(hereinafter: Kudo).

Claim 7:
As per claim 7, which depends on claim 6, Allo and Murugesan failed to specifically disclose further including a utility bidding platform communicatively coupled to the network operations center and a power utility.
Kudo, in the same field of electricity transaction markets discloses this limitation in that [[0036] Electricity traded through a P2P electricity selling and buying transaction market is transmitted through the power grid PL between electricity demanders, for which places to receive or supply electricity bid or offered via a business operator agent or a home agent are fixed [0005] The “general transaction market” is a market on which contracts are executed for electricity that is supplied from an electricity seller to a buyer through an electric power line of a grid-electricity network (an electric power grid for supplying electricity from a large-scale power plant operated by an electric power company), and the “direct transaction market” is a market on which contracts are executed for a case where one of parties of an electricity transaction moves a mobile object such as an electrically driven vehicle to a place of the other party and electricity transmission is directly performed between the parties].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo and Murugesan to include a utility bidding platform communicatively coupled to the network operations center and a power utility as disclosed by Kudo. The motivation for doing so would have been to have a direct transaction market in which no wheeling charge is incurred because an electric power line is not used, so that an electricity buyer can reduce a cost required in the electricity transaction(0018).

Claim 8:
As per claim 8, which depends on claim 7, it is rejected under the same rationale as claim 6 above. Additionally, Allo, Murugesan and Kudo disclose wherein each control system provides energy forecasting by using a Long Short-Term memory (LSTM) model with a gating mechanism, wherein the dataset includes at least one of irradiance data, meteorological data, and real time energy consumption data. Murugesan, [[0044] building control software uses predictive models, [0045] a long-short term memory (LSTM) sequence to sequence (S2S) neural network (a type of RNN) and/or any other type of RNN (e.g., a gated recurrent unit (GRU) neural network) can be utilized by the building system as the prediction model, i.e., to predict a particular point forecast and/or probabilistic forecast of a data point. [0047] During model training, a probabilistic forecast in one time-step can be modified by the building system to represent actual energy consumption in that time-step, and the modified probabilistic forecast (the actual probabilistic forecast) can be fed-back into the LSTM S2S neural network in the next time-step to obtain probabilistic forecast in that time-step. More specifically, the building system can be configured to generate a probability distribution representing an actual energy value and the generated probability distribution can be fed into a decoder of the LSTM S2S neural network in one or more next steps during training of the LSTM S2S neural network.].

Claim 10:	
As per claim 10, which depends on claim 7, Allo, Murugesan and Kudo disclose wherein the utility bidding platform allows the power utility to obtain ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy. Kudo, [[0034] a function of accessing a market for P2P electricity selling and buying transactions, which is configured on the system 2, and enabling a bid to buy or an offer to sell electricity to be placed… An operator of the market for P2P electricity selling and buying transactions issues a contract for a transaction of selling and buying electricity between a seller and a buyer that have matching bid-offer conditions [0036] Electricity traded through a P2P electricity selling and buying transaction market is transmitted through the power grid PL between electricity demanders, for which places to receive or supply electricity bid or offered via a business operator agent or a home agent are fixed.].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo and Murugesan to allow the power utility to obtain ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy as disclosed by Kudo. The motivation for doing so would have been to allow a P2P electricity transaction, which enables renewable energy to be more effectively utilized by an electricity demander, while allowing a mobile object such as an electrically driven vehicle to minimize an electricity cost(0007).

Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, Murugesan and Kudo in view of West.

Claim 9:
As per claim 9, which depends on claim 8, Allo, Murugesan and Kudo disclose the controller predicting future status of the solar panels due to weather events (see Allo [0065]). However, Allo, Murugesan and Kudo failed to specifically disclose wherein the control system predicts the direct normal irradiance (DNI) from a solar panel.
West, in the same field of solar power forecasting discloses this limitation in that [[0154] Determining a predicted solar irradiance value may comprise determining a predicted direct normal irradiance for individual collectors comprising heliostats, linear Fresnel, parabolic trough, or dish collectors in a solar thermal facility, and the method may comprise automated or manual control to optimise plant output, short term operability or long term operability].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the forecasting controller of Allo, Murugesan and Kudo to predict the direct normal irradiance (DNI) from a solar panel as disclosed by West. The motivation for doing so would have been to optimize operation of the device by facilitating prediction of solar irradiance

Claim(s) 11  is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, in view of Murugesan further in view of Kudo.

Claim 11:
As per independent claim 11, Allo discloses an energy aggregation system comprising: a plurality of network communication interfaces, each coupled to a respective hydrogen storage assembly associated with a customer, wherein each hydrogen storage assembly includes a control system[[0101] As described previously, the system can operate using a knowledge base that monitors, tracks and optimizes the operation of the hydrogen generation, storage and utilization processes. The local system 200 thus includes a local controller 336 (similar to the controller described above) that receives various parametric inputs from various sensors (collectively denoted at 338). In some embodiments, these and other values may be communicated to the server 332 via the network 334.]  
an analytics system communicatively coupled to the plurality of network communication interfaces[[0064] The controller operates to collect and apply analytics associated with the operation of the system. As explained below, the analytics and other information can be stored locally as well as remotely, can be displayed on a user device, etc. [0102] The server 332 may include a server controller 340, which processes the data received from the system 200. This can include storage of the system data in a history database structure 342 in server memory, and analysis of the data using an analysis engine 344].
wherein each control system provides energy forecasting based on at least one of irradiance data, meteorological data, and real time energy consumption data Allo [[0065] Still other information can be supplied as desired, such as the status of solar panels, weather forecast information, etc. The controller is adaptive and can utilize outside information to help predict and adaptively configure the system. This enables the system to be autonomous to the user. For example, if the National Weather Service predicts a large snowfall (or other inclement weather) that may affect the ability of the solar panels to generate power in the coming days, additional steps may be taken to accumulate higher than normal levels of hydrogen].
a network operations center communicatively coupled to the analytics system [0100] FIG. 16 shows a functional block representation of a data communication and processing system 330 in accordance with further embodiments. In FIG. 16, the system 200 is connected to a server 332 using a suitable network 334. The server 332 can be a remote server geographically distant from the system 200, such as in a data center or other cloud based environment. The server 332 can additionally or alternatively be located locally. Hence, the network 334 can comprise a local area network, a wide area network, a wireless network, the Internet, etc. or any combination of these or other configurations.[0102] analysis engine].
Allo failed to specifically disclose
 the analytics system configured to employ one or more machine learning models to aggregate status and energy data received from the control system, and a utility bidding platform communicatively coupled to the network operations center and a power utility, the utility bidding platform configured such that the power utility obtains ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy.
Murugesan, in the same field of power distribution and prediction models discloses the analytics system configured to employ one or more machine learning models to aggregate status and energy data received from the control system [[0047] During model training, a probabilistic forecast in one time-step can be modified by the building system to represent actual energy consumption in that time-step, and the modified probabilistic forecast (the actual probabilistic forecast) can be fed-back into the LSTM S2S neural network in the next time-step to obtain probabilistic forecast in that time-step. [0088] Referring more particularly to FIG. 8, the LSTM S2S neural network 700, during the inference phase, generates an energy forecast for a building for use in operating equipment of a building or providing metrics to a user.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo to employ one or more machine learning models to aggregate status and energy data received from the control system as disclosed by Murugesan. The motivation for doing so would have been to provide energy controllers that are both cost efficient and able to conform to future deviations (0002).
Allo and Murugesan failed to specifically disclose a utility bidding platform communicatively coupled to the network operations center and a power utility, the utility bidding platform configured such that the power utility obtains ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy.
Kudo, in the same field of electricity transaction markets discloses a utility bidding platform communicatively coupled to the network operations center and a power utility [[0036] Electricity traded through a P2P electricity selling and buying transaction market is transmitted through the power grid PL between electricity demanders, for which places to receive or supply electricity bid or offered via a business operator agent or a home agent are fixed [0005] The “general transaction market” is a market on which contracts are executed for electricity that is supplied from an electricity seller to a buyer through an electric power line of a grid-electricity network (an electric power grid for supplying electricity from a large-scale power plant operated by an electric power company), and the “direct transaction market” is a market on which contracts are executed for a case where one of parties of an electricity transaction moves a mobile object such as an electrically driven vehicle to a place of the other party and electricity transmission is directly performed between the parties].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo and Murugesan to include a utility bidding platform communicatively coupled to the network operations center and a power utility as disclosed by Kudo. The motivation for doing so would have been to have a direct transaction market in which no wheeling charge is incurred because an electric power line is not used, so that an electricity buyer can reduce a cost required in the electricity transaction(0018).
the utility bidding platform configured such that the power utility obtains ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy [[0034] a function of accessing a market for P2P electricity selling and buying transactions, which is configured on the system 2, and enabling a bid to buy or an offer to sell electricity to be placed… An operator of the market for P2P electricity selling and buying transactions issues a contract for a transaction of selling and buying electricity between a seller and a buyer that have matching bid-offer conditions [0036] Electricity traded through a P2P electricity selling and buying transaction market is transmitted through the power grid PL between electricity demanders, for which places to receive or supply electricity bid or offered via a business operator agent or a home agent are fixed.].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify analytic system of Allo and Murugesan to allow the power utility to obtain ancillary services from the customers in a P2P energy trading community to thereby manage supply and demand of electrical energy as disclosed by Kudo. The motivation for doing so would have been to allow a P2P electricity transaction, which enables renewable energy to be more effectively utilized by an electricity demander, while allowing a mobile object such as an electrically driven vehicle to minimize an electricity cost(0007).

Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, Murugesan and Kudo in view of Strizki.

Claim 12:
As per claim 12, which depends on claim 11, Allo, Murugesan and Kudo disclose wherein at least one of the hydrogen storage assemblies includes: an enclosure substantially encompassing an electrolyzer [[0034] The input power is supplied to an electrolyzer, also sometimes referred to as a hydrolyzer, which operates upon a supply of water (H.sub.2O) from a water source to generate a stream of hydrogen (H.sub.2) gas.], 
a hydrogen storage system [[0052] The tank 132 is a standard hydrogen-compatible, pressurized tank and is easily sized to provide sufficient energy storage capacity for the needs of the system] 
a hydrogen fuel cell [[0054] A hydrogen fuel cell 134 operates to take the hydrogen (either in-stream or from the tank) and essentially perform the reverse operation of the electrolyzer.] 
a power conversion system[[0089] The 240V AC output from the inverter 206 is directed (via a 240V AC bus 316) to some elements of the system rack 202, including the conditioner 214, the electrolyzer 216 and the control electronics 204. A separate transformer/power supply (not separately depicted) can be utilized to generate the necessary low voltages (e.g., 3 VDC, 5 VDC, 12 VDC, etc.) used by the control electronics and 
a control system[[0048] The input power is provided at a suitable voltage level as a power input]; 
wherein the electrolyzer is configured to separate, via electrolysis, water into hydrogen gas that is stored in the hydrogen storage system [[0034] The input power is supplied to an electrolyzer, also sometimes referred to as a hydrolyzer, which operates upon a supply of water (H.sub.2O) from a water source to generate a stream of hydrogen (H.sub.2) gas…[0051] The stream of dry hydrogen gas is directed to a hydrogen tank 132]; 
the hydrogen fuel cell is configured to convert the stored hydrogen gas into electrical energy and water [[0054] A hydrogen fuel cell 134 operates to take the hydrogen (either in-stream or from the tank) and essentially perform the reverse operation of the electrolyzer. That is, the fuel cell operates to take oxygen from the surrounding atmosphere and reactively combines this with the hydrogen to generate electrical power and water (condensate)]. 
the power conversion system is configured to convert the produced electrical energy to a desired form [[0089] The 240V AC output from the inverter 206 is directed (via a 240V AC bus 316) to some elements of the system rack 202, including the conditioner 214, the electrolyzer 216 and the control electronics 204. A separate transformer/power supply (not separately depicted) can be utilized to generate the necessary low voltages (e.g., 3 VDC, 5 VDC, 12 VDC, etc.) used by the control electronics] and 
Allo, Murugesan and Kudo disclosed the hydrogen storage assembly capable of charging battery based equipment and vehicles but failed to specifically disclose an electrochemical energy storage module, the electrochemical energy storage module is configured to function as an energy buffer.
Strizki, in the same field of renewable energy management and storage discloses this limitation in that [[0029] the present invention is also capable of storing energy by charging a battery bank or ultracapacitors. The battery bank or ultracapacitors can also be used in conjunction with the hydrogen energy storage system [0031] Energy storage subsystems components, such as an electrochemical battery bank 160, an ultracapacitor bank (not shown), or hydrogen storage tanks 170, can be housed in one or more separate enclosures, readily connectible to the rest of the system 100].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the hydrogen storage assembly of Allo, Murugesan and Kudo to include an electrochemical energy storage module, the electrochemical energy storage module is configured to function as an energy buffer as disclosed by Strizki. The motivation for doing so would have been to be able to schedule the hydrogen storage system to take advantage of the lower, off-peak rates (0029).

Claim 13:
As per claim 13, which depends on claim 12, it is rejected under the same rationale as claim 12 above. Additionally, Allo, Murugesan, Kudo and Strizki disclose wherein the hydrogen storage system includes at least one of an ultra-capacitor, a LiPo battery array, and a NiMH battery array. Strizki, [[0029] the present invention is also capable of storing energy by charging a battery bank or ultracapacitors. The battery bank or ultracapacitors can also be used in conjunction with the hydrogen energy storage system.].

Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, Murugesan, Kudo and Strizki in view of Surenda Saxena et al. (US PG Pub No. US2012/0266863: Published: 10/25/2012)(hereinafter: Saxena).

Claim 14:
As per claim 16, which depends on claim 12, Allo, Murugesan, Kudo and Strizki disclose the hydrogen storage system being tanks. Allo, Murugesan, Kudo and Strizki failed to disclose wherein the hydrogen storage system is a metal hydride storage device.
Saxena, in the same field of hydrogen storage discloses this limitation in that [[0012] The invention also includes the use of magnesium hydride or any hydride in obtaining hydrogen for the above systems. [0013] The invention also includes a hydrogen generator which uses a recyclable hydride. [0014] The invention also includes a system of solar concentrators which provides solar power to the hydride for dissociation.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the hydrogen storage assembly of Allo, Murugesan, Kudo and Strizki to use a metal hydride as hydrogen storage as disclosed by Saxena. The motivation for doing so would have been to take advantage of the economic and environmental reasons behind solid hydrogen storage.


Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, Murugesan, Kudo and Strizki in view of Young Park (US PG Pub No. US2018/0086450; Published: 03/29/2018)(hereinafter: Park).

Claim 15:
As per claim 15, which depends on claim 12, Allo, Murugesan, Kudo and Strizki failed to specifically disclose wherein the hydrogen storage system stores hydrogen in the range of 2.5 kg to 10 kg.
Park, in the same field of hydrogen storage discloses this limitation in that [[0050]  the magnesium has a good bonding strength with hydrogen, so it is known that the magnesium can function as a solid storage (a bowl) of hydrogen… The hydrogen of about 4 kg can be stored in a solid fuel state using the technology.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the hydrogen storage assembly of Allo, Murugesan, Kudo and Strizki to store hydrogen in the range of 2.5 kg to 10 kg as disclosed by Park. The motivation for doing so would have been to take advantage of the economic and environmental reasons behind solid hydrogen storage.

Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allo, Murugesan, Kudo and Strizki in view of Karl-Heinz Lentz (US PG Pub No. US2023/0407182: priority: 11/19/2020)(hereinafter: Lentz).

Claim 16:
As per claim 16, which depends on claim 12, Allo, Murugesan, Kudo and Strizki disclose a source of input power is supplied by a bank of solar (photovoltaic) panels 120. … The input power is provided at a suitable voltage level as a power input to an input power converter circuit 122. The circuit 122 converts the power and directs it to various elements in the system, including to an electrolyzer (hydrolyzer) 124 (Allo [0047-0048]). Allo, Murugesan, Kudo and Strizki disclose failed to specifically disclose wherein the electrolyzer is operated using excess energy from solar panels in accordance with scheduling determined by the control system.
Lenz, in the same field of autonomous energy generation discloses this limitation in [[0199] Surplus electricity from the photovoltaic system is used to operate reactor 3. In addition, generated electricity from the photovoltaic system is used to operate the electrolyzer 10. For this purpose, the hybrid power plant includes an electrolyzer 10 that is powered by electricity from renewable energy and that converts excess electricity into hydrogen].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the hydrogen storage assembly of Allo, Murugesan, Kudo and Strizki to operate the electrolyzer using excess energy from solar panels in accordance with scheduling determined by the control system as disclosed by Lentz. The motivation for doing so would have been to be to take advantage of excess energy for the system to be more cost effective.

Conclusion

The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.


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/HOWARD CORTES/           Primary Examiner, Art Unit 2118