DETAILED ACTION
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 .
Response to Arguments
Applicant’s arguments, see page 11, filed 11/03/2025, with respect to claim 1 have been fully considered and are persuasive. The claim objection of claim 1 has been withdrawn.
Applicant’s arguments, see page 8, filed 11/03/2025, with respect to the rejection(s) of claim(s) 1 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Aselage et al. (US 6,479,919), herein after Aselage.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a).
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.
Claim(s) 1, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba et al. (US 2015/0349387), herein after Inaba, Singer et al. (US 2023/0071975), herein after Singer, Aselage (US 6,479,919) and Song (US 2013/0200852).
Regarding claim 1, Inaba discloses a power system (fig. 1) based on a beta source (power generation system, there is a nuclear power generation system, paragraph [0054]), the power system comprising:
a power generating section including a plurality of beta source-based generators (power generation system, there is a nuclear power generation system, paragraph [0054]; nuclear power generation systems can include sources of beta decay. Beta decay is an inherent part of the nuclear fission process and the subsequent radioactive decay of fission products, making it a natural component of nuclear power generation systems and the resulting waste. https://tmi.dickinson.edu/how-does-a-nuclear-power-plant-work/#:~:text=One%20aspect%20of%20the%20fission%20products%2C%20which,which%20is%20just%20a%20high%20speed%20electron.);
a power storage (The power source device 1 is provided with an electricity storage system, paragraph [0056]) section including a plurality of power storages (The electricity storage system is provided with power source strings 3 to 5, paragraph [0063]) to store electrical energy which is generated from the plurality of beta source-based generators (paragraph [0059] the string 3 has a unit 31 with the battery cell (energy storage device) 100; figs, 1-2);
a multiplexer (181, fig. 4 (expanded part of fig. 1-3)) configured to select at least some of the plurality of power storages (paragraph [0170]);
an optical power learning section (160, fig. 6) configured to receive electrical signals provided from the plurality of power storages, and estimate a state of charge (SOC) of each of the plurality of power storages (The electricity storage control circuit performs processing by inputting a plurality of signals including a command signal which is output from the power control circuit 80 or measurement signals which are output from the voltage measurement circuits 180 and 190 and the current measurement device 109; and outputs a plurality of signals including control signals with respect to the discharging switch drive circuit 170 and the charging switch drive circuits 171 to 173 or signals relating to the state estimation amount of the electricity storage modules 120 to 140, paragraph [0178]). Although, Inaba does disclose the power generation system is a nuclear power generation system and arithmetic portion to estimate the state of charge of the plurality of energy storage devices (energy sources strings 3-5, fig. 1).
However, Inaba is silent over the power generating section includes beta source -based generators that convert energy of absorbed beta particles emitted from the beta source directly into electrical energy via one or more semiconductor junctions; estimating the SOC based on machine learning an optimal power selecting section configured to select a power storage, which provides the optimal power, based on the SOC of each of the plurality of power storages; an output section including a plurality of output devices to output power provided from the power storage selected by the optimal power selecting section; and a de-multiplexer configured to select at least one output device of the plurality of output devices.
Aselage discloses a power generation system includes beta source -based generators that convert energy of absorbed beta particles emitted from the beta source directly into electrical energy via one or more semiconductor junctions (Col. 1, lines 15-24).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, to have the beta source -based generators with semiconductor junctions as taught by Aselage, in order to produce high power, to have a long half-life, or to require little shielding by choosing among different radioisotope energy sources (Col. 1, lines 45-47).
Singer discloses a controller which is capable of estimating the SOC based on machine learning (paragraph [0109]);
an optimal power selecting section (controller, paragraph [0026]) configured to select a power storage, which provides the optimal power, based on the SOC of each of the plurality of power storages);
an output section (point A to B is the output section and produce output voltage for external devices, fig. 1) including a plurality of output devices to output power provided from the power storage selected by the optimal power selecting section (charging or discharging of the module to achieve the necessary measurements for determining characteristics of the energy storage device of a module may be done during on-load operation of the ESS making use of the load situation and/or the available energy of other modules, paragraph [0014]);
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Although, Singer does disclose an output section to provide power to external devices. However, Inaba and Singer do not explicitly disclose a de-multiplexer configured to select at least one output device of the plurality of output devices.
Song discloses a controller (240, fig. 3) having a de-multiplexer (250, fig. 3) configured to select at least one output device of the plurality of output devices (The demultiplexer (DEMUX) 150 selects one among the output channels 154 to 157 according to the control signal as the output channel selection signal to perform discharging [0041] Note: the discharging power can also be provided to the selected loads connected to the DEMUX 250).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Aselage and Singer, to include a de-multiplexer in a controller as taught by Song, in order to efficiently distribute power, reduced hardware and wiring complexity, and increased system scalability and flexibility.
Regarding claim 8, Inaba in view of Aselage, Singer and Song discloses the power system of claim 1. However, Inaba is silent about wherein the machine learning is based on at least one of Forward Neural Network (FNN), Decision Tree Learning, Support Vector Machine, a Genetic algorithm, an Artificial Neural Network (ANN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), Reinforcement Learning, or Auto Encoder.
Singer discloses wherein the machine learning is based on at least one of Forward Neural Network (FNN), Decision Tree Learning, Support Vector Machine, a Genetic algorithm, an Artificial Neural Network (ANN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), Reinforcement Learning, or Auto Encoder (paragraph [0109]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), Aselage (US 6,479,919) and Song (US 2013/0200852) as applied to claim 1 above, and further in view of Macris (US 10,283,974).
Regarding claim 2, Inaba in view of Aselage , Singer and Song discloses the power system of claim 1. Inaba further discloses , wherein the optimal power learning section includes:
an input module to sense a voltage value or a current value from the electrical signals, a pre-processing module to receive the voltage value or the current value from the input module and transform the voltage value or the current value to input data for the machine learning (As shown in FIG. 6, the electricity storage control circuit 160 is provided with an arithmetic portion 161, a storage portion 162, a voltage detection portion 163, a current detection portion 164, and a switch control portion 165 as main constituents. The electricity storage control circuit performs processing by inputting a plurality of signals including a command signal which is output from the power control circuit 80 or measurement signals which are output from the voltage measurement circuits 180 and 190 and the current measurement device 109; paragraph [0178]); However, Inaba, and Aselage do not explicitly disclose a neuron array module to estimate the SOC of each of the plurality of power storages through the machine learning, based on a digital signal obtained from the pre-processing module a memory to store a weight value for an operation of the machine learning, and provide the weight value to the neuron array module; and an optimal power classifying module to classify the power storage which provides the optimal power, based on the estimated SOC.
Singer discloses a neuron array module to estimate the SOC of each of the plurality of power storages through the machine learning, based on a digital signal obtained from the pre-processing module (paragraph [0109] neuronal network is use to estimate the soc of the battery modules);
a memory (a cloud server 170 inherently has a memory to store data, paragraph [0079] ) to store a weight value for an operation of the machine learning, and provide the weight value to the neuron array module (paragraph [0079]-[0081]);
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Macris discloses an optimal power classifying module (203, fig. 2) to classify the power storage which provides the optimal power, based on the estimated SOC (Col. 5, lines 53-65).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Aselage, Singer and Song to include an optimal power classifying module as taught by Macris, in order to classify the battery modules by their power output, which allows to have a more granular and informed approach to battery system design, leading to improved performance, reliability, and safety.
Regarding claim 3, Inaba in view of Aselage, Singer, Song and Macris discloses the power system of claim 2. Inaba further discloses wherein the pre-processing module includes: an analog/digital converter to convert the voltage value or the current value into the digital signal (The voltage detection can convert the measurement signals as analog signals to digital signals using an analog/digital converter, paragraph [0180]); and a standardization unit to standardize the digital signal with a specific bit number to generate the input data (The electricity storage control device is provided with an electricity storage control circuit 160 which includes an arithmetic processing device (microcomputer), paragraph [0163] A fundamental aspect of microcomputer languages is their use of standardized codes of bit numbers).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), Aselage (US 6,479,919), Song (US 2013/0200852), and Macris (US 10,283,974) as applied to claim 3 above, and further in view of Ramirez (US 5,299,529).
Regarding claim 4, Inaba in view of singer, Song, Aselage and Macris discloses the power system of claim 3. However, they are silent over wherein the specific bit number of machine learning language is '8'.
Ramirez discloses wherein the specific bit number is '8' (the controller uses the microcomputer with single 8-bit code, Col. 6; lines 42-45).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Singer, Song, Aselage and Macris to have the machine learning language with the specific bit number 8 as taught by Ramirez, in order to have the simple, cost-effective, and low power consumption system which make them suitable for specific applications in embedded systems.
Claim(s)5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), Aselage (US 6,479,919), Song (US 2013/0200852), and Macris (US 10,283,974) as applied to claim 2 above, and further in view of Iida (US 8,682,517).
Regarding claim 5, Inaba in view of singer, Song, Aselage and Macris discloses the power system of claim 2. However, they are silent over wherein the neuron array module includes: an input buffer to store the input data; a weight buffer to store the weight value provided from the memory; a multiplier to perform a multiplication operation with respect to the input data provided from the input buffer and the weight data provided from the weight buffer; an adder to perform an add operation with respect to a result value of the multiplication operation derived from the multiplier; and a register to temporarily store a result of the add operation, which is provided from the adder.
Iida discloses wherein the neuron array module includes:
an input buffer (control device 2, fig. 2) to store the input data; a weight buffer to store the weight value provided from the memory (the control device 2 has the memory to store the input data, Col. 7 and the reference values too; lines 4-10);
a multiplier (241, fig. 5) to perform a multiplication operation with respect to the input data provided from the input buffer and the weight data provided from the weight buffer (Col. 11, lines 16-19);
an adder (244, fig. 5) to perform an add operation with respect to a result value of the multiplication operation derived from the multiplier (the data is processed in 244 after 241, fig. 5); and a register (245, fig. 5) to temporarily store a result of the add operation, which is provided from the adder (Register 245 holds and outputs SOC1(t) that is the SOC of power storage unit 4-1 in each processing cycle. Col. 11; lines 26).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Singer, Song, Aselage and Macris to have a specific neuron module as taught by Iida, in order to have the advance technique to deal with complex data.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), Aselage (US 6,479,919), Song (US 2013/0200852), and Macris (US 10,283,974) as applied to claim 1 above, and further in view of Park (KR 20190023952A), with publication date: March 8, 2019.(attached is the human translation of this foreign reference)
Regarding claim 7, Inaba in view of Singer, Aselage and Song discloses the power system of claim 1. However, they do not disclose wherein the plurality of output devices are Internet of thing (IoT) sensors.
Park discloses wherein the plurality of output devices are Internet of thing (IoT) sensors (paragraph [0008]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Singer, Aselage and Song to have the external devices as Internet of thing (IoT) sensors as taught by Park, in order to provide continuous power from the nuclear power generation system to the electronic communication devices which are underground , where the replacement of the battery is difficult (Paragraph [0002]).
Claim(s)9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), and Aselage (US 6,479,919).
Regarding claim 9, Inaba discloses a method for operating a power system based on a beta source power generation system (there is a nuclear power generation system, paragraph [0054]), the method comprising:
Storing the electrical energy via a plurality of power storages (electrical storage modules 120-140, fig. 4);
sensing a voltage value or a current value from each of the plurality of power storages (paragraph [0181]-[0182]);
pre-processing the sensed voltage value or the sensed current value (The current detection can convert the measurement signals as analog signals to digital signals using an analog/digital converter and detect the converted digital signals through signal processing, paragraph [0182]);
estimating an state of charge (SOC) of each of the plurality of power storages , from the pre-processed voltage value or the pre-processed current value paragraph [0179]; [0185] the state of charge is estimated by the 161 athematic portion by using input variables), Although, Inaba does disclose arithmetic portion to estimate the state of charge of the plurality of energy storage devices (energy sources strings 3-5, fig. 1). However, Inaba is silent over converting the energy of absorbed beta particles emitted from the beta source into electrical energy via one or more semiconductor junctions, and estimating the SOC based on machine learning and to select an optimal power device based on the SOC; determining whether a power value, which is provided from the optimal power device, is equal to or greater than a preset threshold value; and outputting the power value, when the power value provided from the optimal power device is equal to or greater than the preset threshold value.
Aselage discloses a power generation system includes beta source -based generators which convert energy of absorbed beta particles emitted from the beta source directly into electrical energy via one or more semiconductor junctions (Col. 1, lines 15-24).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, to have the beta source -based generators with semiconductor junctions as taught by Aselage, in order to produce high power, to have a long half-life, or to require little shielding by choosing among different radioisotope energy sources (Col. 1, lines 45-47).
Singer discloses estimating the SOC based on machine learning (paragraph [0109]) and to select an optimal power device based on the SOC (charging or discharging of the module to achieve the necessary measurements for determining characteristics of the energy storage device of a module may be done during on-load operation of the ESS making use of the load situation and/or the available energy of other modules, paragraph [0014]);
determining whether a power value, which is provided from the optimal power device, is equal to or greater than a preset threshold value; and outputting the power value, when the power value provided from the optimal power device is equal to or greater than the preset threshold value (When the current falls below a specified threshold, the battery is considered fully discharged and the discharge process is terminated, paragraph [0004]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Regarding claim 10, Inaba in view of Aselage and Singer discloses the power system of claim 9. However, Inaba and Aselage are silent about wherein the machine learning is based on at least one of Forward Neural Network (FNN), Decision Tree Learning, Support Vector Machine, a Genetic algorithm, an Artificial Neural Network (ANN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), Reinforcement Learning, or Auto Encoder.
Singer discloses wherein the machine learning is based on at least one of Forward Neural Network (FNN), Decision Tree Learning, Support Vector Machine, a Genetic algorithm, an Artificial Neural Network (ANN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), Reinforcement Learning, or Auto Encoder (paragraph [0109]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Aselage, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Regarding claim 11, Inaba in view of Aselage and Singer discloses the power system of claim 9. Inaba discloses the power generation source is the beta source (paragraph [0054]). However, Inaba and Aselage do not explicitly disclose the method further comprising selecting some output devices of a plurality of output devices included in the power system.
Singer discloses the method further comprising selecting some output devices of a plurality of output devices included in the power system (point A to B is the output section and produce output voltage for external devices, fig. 1).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Aselage, including a machine-learning process to estimate the SOC of the battery module and provide the optimal power to output devices as taught by Singer, in order to estimate the SOC of the battery precisely and accurately which in return enhance safety and performance of energy storage devices, and prolonging the lifespan of the batteries.
Regarding claim 12, Inaba further discloses wherein the pre-processing of the sensed voltage value or the sensed current value includes: converting the sensed voltage value or the sensed current value in a form of a digital signal (The voltage detection can convert the measurement signals as analog signals to digital signals using an analog/digital converter, paragraph [0180]); and standardizing the digital signal with a specific bit number(The electricity storage control device is provided with an electricity storage control circuit 160 which includes an arithmetic processing device (microcomputer), paragraph [0163] A fundamental aspect of microcomputer languages is their use of standardized codes of bit numbers).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inaba (US 2015/0349387), Singer (US 2023/0071975), and Aselage (US 6,479,919) as applied to claim 12 above, and further in view of Ramirez (US 5,299,529).
Regarding claim 13, Inaba in view of Aselage and singer discloses the power system of claim 3. However, they are silent over wherein the specific bit number of machine learning language is '8'.
Ramirez discloses wherein the specific bit number is '8' (the controller uses the microcomputer with single 8-bit code, Col. 6; lines 42-45).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of claimed invention to modify Inaba’s system in view of Singer and Aselage to have the machine learning language with the specific bit number 8 as taught by Ramirez, in order to have the simple, cost-effective, and low power consumption system which make them suitable for specific applications in embedded systems.
Allowable Subject Matter
Claim 6 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.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SADIA KOUSAR whose telephone number is (571)272-3386. The examiner can normally be reached M-Th 7:30am-5:30pm.
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SADIA . KOUSAR
Examiner
Art Unit 2859
/JULIAN D HUFFMAN/ Supervisory Patent Examiner, Art Unit 2859
Prosecution Insights