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 .
This action is responsive to communication filed on 12/22/2025.
Claims 1-14 are pending. Claims 1, 3, 5-6 and 12-14 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the recitation “at least the larger part of a duration of the first measurements on a battery is used for discharging and/or charging the battery” in lines 1-3 renders the claim indefinite. It is unclear from the claim what is meant by “the larger part of a duration” since the term “larger part” is a relative term it is unclear the metes and bounds of the claim, since no proper definition as to ascertain which subject matter the term “larger part of a duration” is trying to cover and the claim does not present a particular definition as to ascertain the degree of what the term “larger part of a duration” is trying to encompass and the degree of the duration the term larger is trying to cover since the duration have not been defined as to ascertain what constitutes to be “a larger part of a duration”. Clarification and correction is required.
Regarding claim 6, the recitation “at least the larger part of a duration of the second measurement on a battery is used for measuring the electrical impedance spectrum” in lines 1-3 renders the claim indefinite. It is unclear from the claim what is meant by “the larger part of a duration” since the term “larger part” is a relative term it is unclear the metes and bounds of the claim, since no proper definition as to ascertain which subject matter the term “larger part of a duration” is trying to cover and the claim does not present a particular definition as to ascertain the degree of what the term “larger part of a duration” is trying to encompass and the degree of the duration the term larger is trying to cover since the duration have not been defined as to ascertain what constitutes to be “a larger part of a duration”. Clarification and correction is required.
For examination on the merits the claims will be interpreted as best understood in light of the 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejections above.
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) 1-6, 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 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 [[a]] 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 5, 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) and over charge (see para. 0032) and monitoring a battery at an excited state (e.g. full charge) or steady state or a combination thereof (see para. 0027, 0036-0037, 0039-0040, 0045).
However, it is silent as to disclose that at least the larger part of a duration of the first measurements on a battery is used for discharging and/or charging the battery.
Baumann discloses that at least the major part of a duration of the first measurements on a battery is used for discharging and/or charging the battery (para. 0059, 0098, wherein measurements involve the cells being respectively discharged 0160, discloses the capacity is determined with CC-CV charging and discharging according to the specified voltage window and the capacity value).
Examiner Note: claim 5 is been interpreted in light of the 35 USC 112(b) as measurements involving discharging/charging of the battery.
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 so that at least the major part of a duration of the first measurements on a battery is used for discharging and/or charging the battery, for the benefit or providing an enhanced and robust battery monitoring system and providing accurate assessment of actual energy storage.
Regarding claim 6, 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) and over charge (see para. 0032) and monitoring a battery at an excited state (e.g. full charge) or steady state or a combination thereof (see para. 0027, 0036-0037, 0039-0040, 0045).
However, it is silent as to disclose that at least the larger part of a duration of the second measurement on a battery is used for measuring the electrical impedance spectrum.
Baumann discloses that at least the larger part of a duration of the second measurement on a battery is used for measuring the electrical impedance spectrum (para. 0049-0050, 0073, 0082, 0086, wherein impedance is being determined/obtained for a respective time step; and para. 0135, 0140 , wherein impedance spectrum is disclosed).
Examiner Note: claim 6 is been interpreted in light of the 35 USC 112(b) as measurements of impedance determined/obtained.
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 so that at least the larger part of a duration of the second measurement on a battery is used for measuring the electrical impedance spectrum, 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),
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 with respect to amended claim(s) 1, 12, 13 and 14 have been considered but are moot because the new ground of rejection necessitated by the amendments and because the arguments are directed to the newly added limitations now being treated on the merits over the newly discovered prior art, and because the new ground of rejection does not rely on any reference applied does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857