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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/30/2026 has been entered.
Claims 1-4, 7-14 are pending. Claims 5-6 are cancelled. Claims 14 have been amended. Entry of this amendment is accepted and made of record.
Claim Objections
Claim 14 objected to because of the following informalities: the claim recitationon-- to avoid antecedent basis issues. Appropriate correction is required.
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) 1-4, and 8-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by Gullapalli et al. US2022/0091062A1 (hereinafter Gullapalli) in view of Baumann US 20220374568 A1 (hereinafter Baumann).
Regarding claim 1, Gullapalli discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, para. [0015], [0017], [0043]), wherein the battery test system comprises a battery test bench (see Fig. 1; abstract; para. [0015], [0017], [0040], [0043], [0050]-[0051])
wherein the battery test bench (see Fig. 1; abstract; para. [0015], [0017], [0040], [0043], [0050]-[0051]) comprises:
a measurement device configured for performing a battery capacity measurement (see abstract; Fig. 1, 3-4,8-9; para. [0012-0014]; [0020-0021], [0044]-[0045], [0052]) and an electrical impedance spectrum measurement on an electrochemical battery connected to the measurement device (see Figs. 1, 3-4, 8-9; para. 0015-0018,0020-00021, 0035, 0045, 0050-0051, BMS Monitor 106), and
a communication interface configured for communicating with a network manager 110 via a communication network (see Fig. 1, para. 0019, 0022-0025),
wherein the system comprises:
a machine learning algorithm for processing a measured electrical impedance spectrum (see para. 0039-0040, 0042, 0045, 0047, 0049, 0051),
wherein the battery test system is configured for first performing first measurements on a first plurality of batteries of a same kind to obtain first measurement data of each of the batteries of the same kind (see Figs. 1-2, 7, 8; para. 0016-0018, 0021-0022, 0024, 0029, 0039, 0045, wherein multiple batteries of the same type may be used), transmitting the first measurement data to the network manager 110 via the communication network (see Fig. 1-2, para. 0022-0023,0025), and training the machine learning algorithm based on the first measurement data (see para. 0039), when the first plurality of batteries of the same kind are connected to the measurement device of the battery test bench one after another (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039), wherein for each battery of the first plurality of batteries of the same kind, the first measurements are performed while the respective battery is connected to the measurement device (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039),
and then performing a second measurement on at least one further battery of the same kind to obtain second measurement data (see Figs. 1-2, 7-9, para. 0039-0040, 0045-0047)
transmitting the second measurement data to the network manager110 via the communication network (see Figs. 1-2,7-8; para. 0022-0025),
and using the trained machine learning algorithm for evaluating the second measurement data (see Figs. 7-8; para. 0039-0040, 0045),
wherein the second measurement is performed for a respective battery of the at least one further battery of the same kind while the respective battery is connected to the measurement device (see Figs. 1-2, 7, 8; para. 0016-0018, 0021-0022, 0024, 0029, 0039, 0045, wherein multiple batteries of the same type may be used; see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039)
wherein performing the first measurements includes measuring a battery capacity of a respective battery and measuring an electrical impedance spectrum of the battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein performing the second measurement includes measuring an electrical impedance spectrum of a respective battery, see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein using the trained machine learning algorithm for evaluating the second measurement data includes inputting the measured electrical impedance spectrum of a respective battery to the machine learning algorithm (see Figs. 1-2, 7-9; para. 0029, 0039-0042; claim 20),
processing the measured electrical impedance spectrum by the machine learning algorithm (see Figs, 1-2, 7-9; para 0015-0016, 0039-0040;), and generating an output by the machine learning algorithm, wherein the output represents battery state information relating to a battery capacity (see Figs. 7-9; abstract, para. 0015-0016, 0039-0040, 0042-0045, 0051),
wherein the battery test system is configured to automatically switch from a training phase, in which the first measurements are performed for each of said first plurality of batteries of the same kind, to an estimating phase, in which the second measurement is performed on said at least one further battery of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8).
However Gullapalli do not expressly or explicitly discloses a server, a communication interface configured for communicating with the server via a communication network, wherein the server comprises: transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), wherein the battery test system comprises a battery test bench (see. abstract, para. 0014, wherein monitoring the state of a battery, para. 0031-0032, 0051 wherein the monitoring can be implemented on a central server and wherein a one or more management systems 61 (e.g. BMS) of the battery is disclosed) and a server (see para. 0016, 0018, 0032, wherein server is disclosed; see Fig. 1, para. 0032, 0045, 0047, 0051 wherein communication connections 49 between the server 81 and each of several batteries 91-96 could be implemented via a cellular network and wherein a communication interface 62 is disclosed and wherein the management system 61 can establish a communication connection 49 with the server via the communication interface 62)), wherein the server comprises: a machine learning algorithm for processing a measured electrical impedance spectrum (see para. 0032, 0083, 0085, wherein machine learning is disclosed).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with a server, a communication interface configured for communicating with the server via a communication network, wherein the server comprises: transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Regarding claim 2, the combination of Gullapalli and Baumann discloses the materials as applied above. Gullapalli further disclose that the output of the machine learning algorithm represents one of battery capacity, state of health, and a classification (Figs. 3-4, 7-9; see abstract, para. 0003, 0026-0027, 0030, 0035, 0039-0040).
Regarding claim 3, the combination of Gullapalli and Baumann discloses the materials applied above. Gullapalli further discloses that the battery test system is configured for determining and outputting battery state information relating to a current battery capacity (see Figs. 3-4, 7-9; para. 0029, 0035, 0037-0041), wherein for said number first plurality of batteries of the same kind, the battery state information is determined based on the first measurement data of the respective battery (see Figs. 3-4, 6-9; para. 0039-0040, 0045), and wherein for said at least one further battery of the same kind, the battery state information is determined based on the evaluating of the second measurement data by the machine learning algorithm (see Figs. 3-4, 6-9; para. 0039-0040, 0045).
Regarding claim 4, the combination of Gullapalli and Baumann discloses the materials applied above. Although Gullapalli further discloses that battery fault can be caused by operation issues such as over discharge and thermal stress (storage, charging, discharging) (see para. 0032).
However, it is silent as to performing the first measurements includes measuring the battery capacity of the respective battery by discharging.
Baumann discloses wherein performing the first measurements includes measuring the battery capacity of the respective battery by discharging (see para. 0053, 0061, 0098-0099, 0150, 0160, wherein measurements in which the cells are being discharged is disclosed).
Therefore it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of Baumann to configure the system of Gullapalli for performing the first measurements includes measuring the battery capacity of the respective battery by discharging, for the benefit or providing an enhanced and robust battery monitoring system and providing accurate assessment of actual energy storage.
Regarding claim 8, the combination of Gullapalli and Baumann discloses the materials applied above. Gullapalli further discloses the BMS monitor may inject the stimulus signal into the battery module and then monitor the impedance response to the stimulus and that the MBS monitors may communicate through wired communication interface (i.e. cabling connected in serial fashion) (see para. 0024-0025) and that the battery monitoring techniques may be used in a wired BMS (see para. 0019, 0024-0025). Therefore the battery test bench comprises measurement connectors for connecting a battery to the measurement device (see Figs. 1-2; para. 0019, 0024-0025).
Also, Baumann further discloses that the battery test bench further comprises measurement connectors for connecting a battery to the measurement device (see para. 0028, wherein connections and couplings between functional units and elements shown in the figures can be implemented as direct connection or coupling and can be implemented wired or wirelessly, see para.0045, 0051, wherein the batteries are coupled to a respective device 69 and are associated with one or more management systems 61 i.e. BMS, therefore the battery is connected to the measurement device, see Figs. 1-3).
Regarding claim 9, the combination of Gullapalli and Baumann discloses the materials applied above. Gullapalli further discloses a wireless BMS which may include a network manager 110 (see para. 0019) and that a wireless node may include wireless system which may include a radio transceiver to communicate the battery measurements to the Network manager over wireless network (see para. 0022), therefore the communication network is a remote communication network.
However Gullapalli do not expressly or explicitly discloses the server and wherein the server is a remote server.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), comprising a server (see para. 0016, 0018, 0032, wherein server is disclosed), wherein the server is a remote server, and the communication network is a remote communication network (para. 0032, 0045 wherein the server can be implemented as a central server separate from the battery or the battery operated device, and wherein the communication connections could be implemented via a cellular network see Fig. 1).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with a server, and a remote communication network interface for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system. And to provide and enhanced system by enhancing the data collection process and providing real-time monitoring, increased safety, and improve accuracy in data analysis from remote or inaccessible locations, replacing the need for traditional, localized and manual data collection methods.
Regarding claim 10, the combination of Gullapalli and Baumann discloses the materials applied above. Gullapalli further discloses the machine learning algorithm includes an artificial neural network (see Figs. 7-9; para. 0039-0040, 0047, 0049, 0051), wherein training the machine learning algorithm based on the first measurement data includes inputting the measured electrical impedance spectrum of a respective battery to the neural network (see Figs. 7-9; para. 0039-0040, 0051), processing the measured electrical impedance spectrum by the neural network (see para. 0039-0040, 0051), and adapting the neural network based on an output of the neural network and on the measured battery capacity of the battery (see Figs. 7-9, para. 0039-0040, 0051).
Also Baumann discloses that the machine learning algorithm includes an artificial neural network (see para. 0085, wherein an artificial neural network is disclosed), wherein training the machine learning algorithm based on the first measurement data includes inputting the measured electrical impedance spectrum of a respective battery to the neural network (see para. 0032, 0050, 0073, 0085-0086, wherein impedance measurements are disclosed in association with models based on machine learning, and wherein impedance spectrum is disclosed, see para. 0135, 0140, and wherein the machine learning may be continually adapted through machine learning based on state data obtained from different batteries of the same type i.e. impedance, capacity), processing the measured electrical impedance spectrum by the neural network, and adapting the neural network based on an output of the neural network and on the measured battery capacity of the battery (see para. 0032, 0085, wherein the machine learning may be continually adapted through machine learning based on state data obtained from different batteries of the same type i.e. impedance, capacity).
Regarding claim 11, Gullapalli further discloses automatically performing the training of the machine learning algorithm based on the first measurement data (see Figs. 7-8; para. 0039-0040, 0045, 0047, 0051; figs. 7-8).
However Gullapalli do not expressly or explicitly discloses a server.
Regarding claim 11, Baumann discloses the server is configured for automatically performing the training of the machine learning algorithm based on the first measurement data (see para. 0085, wherein the machine learning may be continually adapted through machine learning based on state data and wherein iterative adaptation of capacity and impedance enables an especially precise prediction and wherein artificial neural networks i.e. convolutional neural network is disclosed, therefore it is automatically performing the training).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with server configured for automatically performing the training of the machine learning algorithm based on the first measurement data for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Regarding claim 12, Gullapalli disclose A battery test bench (see Figs. 1-2, 7-8; see abstract, para. 0015, 0017, 0040, 0043, 0050-0051), wherein the battery test bench comprises:
a measurement device configured for performing a battery capacity measurement (see abstract; Fig. 1, 3-4,8-9; para. [0012-0014]; [0020-0021], [0044]-[0045], [0052]) and an electrical impedance spectrum measurement on an electrochemical battery connected to the measurement device (see Figs. 1, 3-4, 8-9; para. 0015-0018,0020-00021, 0035, 0045, 0050-0051, BMS Monitor 106), and
a communication interface configured for communicating with a network manager 110 via a communication network (see Fig. 1, para. 0019, 0022-0025),
the system comprising
a machine learning algorithm for processing a measured electrical impedance spectrum (see para. 0039-0040, 0042, 0045, 0047, 0049, 0051),
wherein the battery test bench is configured for first performing first measurements on a number first plurality of batteries of a same kind to obtain first measurement data of each of the batteries of the same kind (see Figs. 1-2, 7, 8; para. 0016-0018, 0021-0022, 0024, 0029, 0039, 0045, wherein multiple batteries of the same type may be used), transmitting the first measurement data to network manager 110 via the communication network (see Fig. 1-2, para. 0022-0023,0025), when the first plurality of batteries of the same kind are connected to the measurement device of the battery test bench one after another (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039), wherein for each battery of the first plurality of batteries of the same kind,
the first measurements are performed while the respective battery is connected to the measurement device (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039), transmitting the first measurement data to network manager 110 via the communication network (see Figs. 1-2,7-8; para. 0022-0025), when the first plurality of batteries of the same kind are connected to the measurement device of the battery test bench one after another (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039), wherein for each battery of the first plurality of batteries of the same kind, the first measurements are performed while the respective battery is connected to the measurement device (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039), and then performing a second measurement on at least one further battery of the same kind to obtain second measurement data (see Figs. 1-2, 7-9, para. 0039-0040, 0045-0047),
transmitting the second measurement data to network manager 110 via the communication network (see Figs. 1-2,7-8; para. 0022-0025), and
receiving estimated battery state information relating to a battery capacity (see abstract; para. 0016-0018, 0029),
wherein the second measurement is performed for a respective battery of the at least one further battery of the same kind(see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0020-00021, 0035, 0045, 0051-0052); while the respective battery is connected to the measurement device (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039),
wherein performing the first measurements includes measuring a battery capacity of a respective battery and measuring an electrical impedance spectrum of the battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein performing the second measurement includes measuring an electrical impedance spectrum of a respective battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein the battery test bench is configured to switch from a training phase to an estimating phase in response to the server having determined that the training of the machine learning algorithm based on the first measurement data is completed for the first plurality of batteries of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8), wherein in the training phase, the first measurements are performed for each of said first plurality of batteries of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8), and wherein in the estimating phase, the second measurement is performed on said at least one further battery of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8).
However Gullapalli do not expressly or explicitly discloses a server, a communication interface configured for communicating with the server via a communication network, transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network, and receiving from the server estimated battery state information relating to a battery capacity.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), wherein the battery test system comprises a battery test bench (see. abstract, para. 0014, wherein monitoring the state of a battery, para. 0031-0032, 0051 wherein the monitoring can be implemented on a central server and wherein a one or more management systems 61 (e.g. BMS) of the battery is disclosed) and a server (see para. 0016, 0018, 0032, wherein server is disclosed; see Fig. 1, para. 0032, 0045, 0047, 0051 wherein communication connections 49 between the server 81 and each of several batteries 91-96 could be implemented via a cellular network and wherein a communication interface 62 is disclosed and wherein the management system 61 can establish a communication connection 49 with the server via the communication interface 62).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with a server, a communication interface configured for communicating with the server via a communication network, transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network and receiving from the server estimated battery state information relating to a battery capacity for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Regarding claim 13, Gullapalli discloses a system for assessing a battery state of electrochemical batteries (see abstract, para. [0015], [0017], [0043]), the system comprises:
a machine learning algorithm for processing a measured electrical impedance spectrum (see para. 0039-0040, 0042, 0045, 0047, 0049, 0051),
wherein the system is configured for first receiving first measurement data of each of a first plurality of batteries of a same kind (see Figs. 1-2, 7, 8; para. 0016-0018, 0021-0022, 0024, 0029, 0039, 0045, wherein multiple batteries of the same type may be used) from a battery test bench via a communication network (see Fig. 1-2, para. 0022-0023,0025), training the machine learning algorithm based on the first measurement data (see Figs. 7-8; para. 0039-0040), determining that the training of the machine learning algorithm based on the first measurement data is completed for the batteries of the same kind (see Figs. 7-8; para. 0039-0040, 0042, 0045, 0047, 0051, training is conducted for multiple batteries under simulated conditions, and shift in impedance are determined form Zn impedance measurements and the training phase concludes at step 716 ‘”learn Model Parameters”), and then receiving second measurement data of at least one further battery of the same kind (see Figs. 1-2, 7-9, para. 0039-0040, 0045-0047) from the battery test bench via the communication network see Figs. 1-2,7-8; para. 0022-0025), and using the trained machine learning algorithm for evaluating the second measurement data (see Figs. 1-2, 7-8; para. 0039-0040, 0045),
wherein the first measurement data include a measured battery capacity of the respective battery and a measured electrical impedance spectrum of the battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein the second measurement data include the measured electrical impedance spectrum of the battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein using the trained machine learning algorithm for evaluating the second measurement data includes inputting the measured electrical impedance spectrum of a respective battery to the machine learning algorithm (see Figs. 1-2, 7-9; para. 0029, 0039-0042; claim 20), processing the measured electrical impedance spectrum by the machine learning algorithm (see Figs. 1-2, 7-9; para. 0029, 0039-0042, wherein Zn Impedance measurements are processed), and generating an output by the machine learning algorithm, wherein the output represents battery state information relating to a battery capacity (see Figs. 7-9; abstract, para. 0015-0016, 0039-0040, 0042-0045, 0051).
However Gullapalli do not expressly or explicitly discloses that the system is a server configured for executing the steps set forth by claim 13.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), wherein the battery test system comprises a battery test bench (see. abstract, para. 0014, wherein monitoring the state of a battery, para. 0031-0032, 0051 wherein the monitoring can be implemented on a central server and wherein a one or more management systems 61 (e.g. BMS) of the battery is disclosed) and a server (see para. 0016, 0018, 0032, wherein server is disclosed; see Fig. 1, para. 0032, 0045, 0047, 0051 wherein communication connections 49 between the server 81 and each of several batteries 91-96 could be implemented via a cellular network and wherein a communication interface 62 is disclosed and wherein the management system 61 can establish a communication connection 49 with the server via the communication interface 62)),
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with a server configured to executing the steps set forth by the claim for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Regarding claim 14, Gullapalli discloses a method for assessing a battery state of electrochemical batteries (see abstract, para. [0015], [0017], [0043]; Figs. 1-2, 7-8), the method comprising:
for each of a number first plurality of batteries of a same kind (see Figs. 1-2, 7, 8; para. 0016-0018, 0021-0022, 0024, 0029, 0039, 0045, wherein multiple batteries of the same type may be used):
performing a first measurement on the battery to obtain first measurement data of the battery, wherein the first measurement is performed by a measurement device of a battery test bench (see Fig. 1; abstract; para. [0015], [0017], [0040], [0043], [0050]-[0051]), wherein the first measurement is performed while the respective electrochemical battery is connected to the measurement device (see Figs. 1, 3-4, 8-9; para. 0015-0018,0020-00021, 0035, 0045, 0050-0051, BMS Monitor 106),
transmitting the first measurement data from the battery test bench to the network manager 110 via a communication network (see Figs. 1-2,7-8; para. 0022-0025), and
training a machine learning algorithm of the system, based on the first measurement data (see Figs. 7-8; para. 0039-0040),
wherein the first plurality of batteries of the same kind are connected to the measurement device of the battery test bench one after another (see Fig. 1, wherein a plurality of battery module 102.1-102.n are connected one after another and are connected to BMS; para. 0019-0023, 0039); and, afterwards,
for at least one further battery of the same kind: performing a second measurement on the battery to obtain second measurement data (see Figs. 1-2, 7-9, para. 0039-0040, 0045-0047), wherein the second measurement is performed by the measurement device of the battery test bench (Figs. 1-2, 7-8; para. 0039-0040, 0045-0047) wherein the second measurement is performed while the respective electrochemical battery is connected to the measurement device (see Figs. 1, 3-4, 8-9; para. 0015-0018,0020-00021, 0035, 0045, 0050-0051, BMS Monitor 106),
transmitting the second measurement data from the battery test bench to the network manager 110 via the communication network (see Figs. 1-2,7-8; para. 0022-0025), and
the system using the trained machine learning algorithm for evaluating the second measurement data (Figs. 1-2, 7-8; para. 0039-0040, 0045-0047),
wherein performing the first measurement includes measuring a battery capacity of a respective battery and measuring an electrical impedance spectrum of the battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein performing the second measurement includes measuring an electrical impedance spectrum of the respective battery (see Figs. 1-2, 7-9; abstract, para. 0015-0018, 0035, 0045, 0051-0052),
wherein using the trained machine learning algorithm for evaluating the second measurement data includes inputting the measured electrical impedance spectrum of the respective battery to the machine learning algorithm (see Figs. 1-2, 7-9; para. 0029, 0039-0042; claim 20), processing the measured electrical impedance spectrum by the machine learning algorithm (see Figs, 1-2, 7-9; para 0015-0016, 0039-0040), and generating an output by the machine learning algorithm, wherein the output represents battery state information relating to a battery capacity (see Figs. 7-9; abstract, para. 0015-0016, 0039-0040, 0042-0045, 0051),
wherein the method comprises: automatically switching from a training phase, in which the first measurements are performed for each of said first plurality of batteries of the same kind, to an estimating phase, in which the second measurement is performed on said at least one further battery of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8).
However Gullapalli do not expressly or explicitly discloses a server.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), wherein the battery test system comprises a battery test bench (see. abstract, para. 0014, wherein monitoring the state of a battery, para. 0031-0032, 0051 wherein the monitoring can be implemented on a central server and wherein a one or more management systems 61 (e.g. BMS) of the battery is disclosed) and a server (see para. 0016, 0018, 0032, wherein server is disclosed; (see Fig. 1, para. 0032, 0045, 0047, 0051 wherein communication connections 49 between the server 81 and each of several batteries 91-96 could be implemented via a cellular network and wherein a communication interface 62 is disclosed and wherein the management system 61 can establish a communication connection 49 with the server via the communication interface 62),
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of the system of Baumann, discussed above, to configure the system of Gullapalli with a server, transmitting the first measurement data from the battery test bench to the server via a communication network, and training a machine learning algorithm of the server, based on the first measurement data, transmitting the second measurement data from the battery test bench to the server via the communication network, and the server using the trained machine learning algorithm for evaluating the second measurement data for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gullapalli et al. US2022/0091062A1 (hereinafter Gullapalli) in view of Baumann US 20220374568 A1 in view of Clarke et al. US20200044294 (hereinafter Clarke).
Regarding claim 7 the combination of Gullapalli and Baumann discloses the materials as applied above with respect to claim 1. Baumann further discloses measurements/assessment of batteries done as a function of time being done over time (see para. 0059, Figs. 5,and further discloses test times normally significantly shorter as compared to the relaxation measurements (see para. 0150).
However the combination of Gullapalli and Baumann do not expressly or explicitly discloses that a total duration of performing the second measurement on at least one further battery of the same kind to obtain second measurement data, transmitting the second measurement data to the server via the communication network, and using the trained machine learning algorithm for evaluating the second measurement data is less than 10 minutes, preferably less than 5 minutes, in particular less than 2 minutes.
Clarke discloses a battery health state detection system (see abstract, para. 0005, ) in which battery measurements are made after a first period of time an wherein a third measurement can be mad after a second period of time (see para. 0008-0009, 0019), and wherein the first period of time may be between 1 minute and 10 minutes, or about 5 minutes and the second period of time may be between 1 minute and 10 minutes, or about 5 minutes (see para. 0025).
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Gullapalli as modified by Baumann such that a total duration of performing the second measurement on at least one further battery of the same kind to obtain second measurement data, transmitting the second measurement data to the server via the communication network, and using the trained machine learning algorithm for evaluating the second measurement data is less than 10 minutes, preferably less than 5 minutes, in particular less than 2 minutes as the prior art teaches that battery measurements can be taken within such time frames i.e. 1 minute and 10 minutes, or about 5 minutes and is commonly known that data processing by a general purpose computer can be done in “real-time” therefore, obtaining a total duration in the desired time frame.
Response to Arguments
Applicant's arguments filed 04/30/2026 have been fully considered but they are not persuasive.
With respect to objections made to claim 14, objections have been overcome by the amendments filed on 04/30/2026.
With respect to rejections made to claims 35 USC 112(b), made to claims 5-6, the rejections have been overcome in view of the amendments filed on 04/30/2026.
With respect to rejections made to claims 1-14 under 35 USC 103 applicant argues with respect to claim 1 that “Gullapalli do not disclose a test bench having a measurement device to which a plurality of batteries could be connected one after another. (see last paragraph of page 10 of the remarks)
In response the examiner disagrees and submits that Gullapalli teaches a battery monitoring system for detection of battery anomalies or faults, therefore the battery test system comprises a battery test bench (see Fig. 1; abstract; para. [0015], [0017], [0040], [0043], [0050]-[0051]). As can be seen in Figure 1, the test bench is shown by battery management system 100 shown in Figure 1, which is performing first impedance measurement from the battery and second impedance measurement from the battery, wherein multiple impedance responses may be measured for every cycle (see para. 0016-0018, 0021-0022, 0024, 0029, 0039-0040, 0045, and wherein consecutive measurements are disclosed) as can be seen from fig 1 the plurality of battery modules 102.1-102.n are connected one after another and are connected to BMS.
Applicant argues that in Gullapalli there is also no system that could “automatically switch from training phase, in which the first measurements are performed for each of said first plurality of batteries of the same kind, to an estimating phase, in which the second measurement is performed on said at least one further battery of the same kind.” as claimed in claim 1 (see last paragraph of page 10 through first paragraph on page 11 of the remarks; and last paragraph on page 13 of the remarks). Applicant further submits that the learning phase is performed for “multiple batteries under simulated conditions” and that in the operation phase a test battery alone is used that may be located in the incorporated device.
In response the examiner disagrees and submit that it is evident from Fig. 7 the system automatically switch from a training phase in which first measurements are performed for each first plurality of batteries of the same kind to an estimating phase in which measurement is performed on at least one further battery of the same kind (see para. 0039-0040, 0045, 0047, 0051; figs. 7-8). In Gullapalli it is discussed that in a learning phase multiple batteries of the same type may be used under simulated conditions and that at 712 multiple impedance responses (two or more) may be measured for every cycle, it is evident from Figure 7 and 1 that the measurements are performed on multiple batteries connected one after another, therefore the operation phase test battery is performed in multiple batteries in both the Learning Phase 710 and Operaton phase 710 as shown in Figure 7. Therefore Gullapalli disclose the materials as discussed above.
Applicant argues with respect to claim 1 that Gullapali does not disclose a machine learning algorithm generating an output that represents battery state information relating to a battery capacity. (see first paragraph on page 12 of the remarks).
In response the examiner disagrees and submits that as claimed Gullapalli disclose a machine learning algorithm generating an output that represents battery state information relating to a battery capacity (see Figs. 7-9; abstract, para. 0015-0016, 0039-0040, 0042-0045, 0051). In particular Figure 7 shows Impedance and shifts in impedance parameters which are passed to the learn model parameters in the training phase and then on the operation phase the measured impedance shifts are determined and if there a shift that is not within range a warning is raised. Said parameters are indicative of battery capacity and state of health “Moreover, using the measured impedance responses, total capacity of the battery (e.g., total amount of energy it can hold when fully charged at certain specified conditions such as temperature and load as specified by the manufacturer) may also be calculated using historical data of the battery and other relevant inputs. Moreover, from the calculated total capacity at measurement temperature, the total battery capacity that can be expected at different temperatures may be calculated. This information may be valuable for an EV implementation because temperature can affect battery capacity”. Therefore Gullapalli disclose the argued features.
Applicant have argued on penultimate paragraph of page 12 of the remarks that the providing simulated conditions for the batteries is not done in the operation phase of Gullapalli.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., providing simulated condition for the batteries is done in the operation phase) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicants arguments stating that Gullapalli teaches away from providing a battery test bench as a part of a battery test system, and that Gullapalli teaches away from providing a battery test system that is configured as recited above (see penultimate paragraph on page 13 or remarks), the examiner disagrees and submits that in Gullapalli the battery test bench (residing within elements 104.1-104.n) is part of a battery test system (100) (Figs. 1-2), as in Gullapalli the testing is being performed in the system (100) shown in Fig. 1 and as discussed above. The fact that the test battery and testing system shown in Fig. 1 may be located in an electric vehicle do not constitute a teaching away. Contrary to applicant assertions, the test bench of Gullapalli is part of the battery test system which may be within an electric vehicle, and is therefore part of the system.
Applicant argues that Baumann do not teach to modify the method of Gullapalli and achieve the subject matter of the present claim 1 and does not cure the deficiencies of Gullapalli. In particular, continually adapting an aging model (as disclosed in para. 0085 of Baumann) does not teach the claimed feature of switching “from a training phase in which the first measurements are performed for each of said first plurality of batteries of the same kind, to an estimating phase, in which the second measurement is performed on said at least one further battery of the same kind” as recited by claim 1. (see second paragraph on page 14 of the remarks.
In response the examiner points to the fact that Baumann was brought into the combination as disclosing a server, a communication interface configured for communicating with the server via a communication network, wherein the server comprises: transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network, and that the features applicant is relying on are covered by Gullapalli as discussed above.
Baumann discloses a battery test system for assessing a battery state of electrochemical batteries (see abstract, wherein monitoring of a battery, e.g. a lithium-ion battery is disclosed; para. 0032,0049-0050, 0064-0066, wherein the SOH of a battery is disclosed), wherein the battery test system comprises a battery test bench (see. abstract, para. 0014, wherein monitoring the state of a battery, para. 0031-0032, 0051 wherein the monitoring can be implemented on a central server and wherein a one or more management systems 61 (e.g. BMS) of the battery is disclosed) and a server (see para. 0016, 0018, 0032, wherein server is disclosed; see Fig. 1, para. 0032, 0045, 0047, 0051 wherein communication connections 49 between the server 81 and each of several batteries 91-96 could be implemented via a cellular network and wherein a communication interface 62 is disclosed and wherein the management system 61 can establish a communication connection 49 with the server via the communication interface 62)), wherein the server comprises: a machine learning algorithm for processing a measured electrical impedance spectrum (see para. 0032, 0083, 0085, wherein machine learning is disclosed).
Given the teachings of Baumann discussed above it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Gullapalli with a server, a communication interface configured for communicating with the server via a communication network, wherein the server comprises: transmitting the first measurement data to the server via the communication network, and training the machine learning algorithm based on the first measurement data transmitting the second measurement data to the server via the communication network for the benefit of adding functionality to the system by incorporating a means capable of providing resources, centralizing data, enabling advanced, complex testing to be performed and improving operational efficiency of the system.
Therefore for the reasons discussed above the combination of Gullapalli and Baumann disclose the argued limitations as claimed.
With respect to claims 12, 13 and 14, Applicant have submitted similar arguments to those with respect to claim 1. In response the examiner disagrees for similar reasons discussed above with respect to claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YARITZA H PEREZ BERMUDEZ whose telephone number is (571)270-1520. The examiner can normally be reached Monday-Friday.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights