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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/03/2026, 04/11/2025 and 10/15/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) have been considered by the Examiner.
Claim Objections
Claim(s) 1, 3, 9, 13 and 19-20 are objected to because of the following informalities:
Claim 1 recites a phrase “the plurality of battery cell” in last paragraph. Examiner suggests amending the phrase to recite “the plurality of battery cells” to restore clarity.
Claim 1 recites a phrase “one or more computer-readable media storing computing instructions” in second paragraph. Examiner suggests amending the phrase to recite “one or more non-transitory computer-readable media storing computing instructions” to restore clarity.
Claim 3 recites a phrase “the at least one battery” in last paragraph. Examiner suggests amending the phrase to recite “the at least one battery cell” to restore clarity.
Claim 9 recites a phrase “the plurality of battery cell cells” in last line. Examiner suggests amending the phrase to recite “the plurality of battery cells” to restore clarity.
Claim 13 recites a phrase “the plurality of cells” in last but two line. Examiner suggests amending the phrase to recite “the plurality of battery cells” to restore clarity.
Claim 19 recites a term “colected” in last paragraph. Examiner suggests amending the term to recite “collected” to restore clarity.
Claim 20 recites a term “a plurality of battery cells” in last paragraph. Examiner suggests amending the term to recite “the plurality of battery cells” to restore clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 7, 10-11, 13-15 and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by KOUNO et al. (US 20240210485; hereinafter KOUNO).
Regarding claim 1, KOUNO discloses in figure(s) 1-21 a battery management apparatus comprising:
a controller (100; figs. 14-16) comprising one or more processors (120), and one or more computer-readable media storing computing instructions (@140) that, when executed on the one or more processors, cause the one more processors to perform:
receiving a plurality of resting voltages (rest voltage v. time; figs 18,6) for each of a plurality of battery cells (200; figs. 14-16, 6), in a resting period (rest period S1701; fig. 17) after charging or discharging of the plurality of battery cells is completed (para. 7 - rest period after a battery finishes charging or discharging; fig. 11),
calculating a plurality of voltage deviations (∆Va, ∆Vb) for each of the plurality of battery cells based on a difference between a representative value of the plurality of resting voltages and each of the plurality of resting voltages for each of the plurality of battery cells (calculate ∆Va, ∆Vb step S1702; figs. 12, 17-19),
calculating rates of change of the plurality of voltage deviations for each of the plurality of cells (∆Va/ta, ∆Vb/tb; para. 78 - change rate in the temporal variation curve of voltage; figs. 18-19), and
diagnosing a state of at least one of the plurality of battery cell based on a rate of change over time of the at least one battery cell (calculate SOH step S1703; fig. 17; SOH_E, R_E fig. 12).
Regarding claim 2, KOUNO discloses in figure(s) 1-21 the apparatus of claim 1, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating an average and a standard deviation of the rates of change of the plurality of battery cells (para. 78 - change rate in the temporal variation curve of voltage), and diagnosing whether or not a low voltage cell exists among the plurality of battery cells based on the average and the standard deviation (para. 60 - if there exists an outlier battery cell, the average value is dragged by the outlier cell; clm. 6 - calculating a second standard deviation of the voltage).
Regarding claim 3, KOUNO discloses in figure(s) 1-21 the apparatus of claim 2, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating a standard score for at least one of the plurality of battery cells based on the average and the standard deviation, and diagnosing the at least one battery as the low voltage cell when the standard score of the at least one battery is lower than a lower limit threshold (para. 59 - temporal variation of output voltage converges to an average of each battery cell. when estimating SOH or internal resistance of the assembled battery, it is typically assumed to use the average of each battery cell; para. 57 - SOH_E is calculated for each battery cell forming the assembled battery, and a standard deviation SOH_E_σ of SOH_E is also calculated).
Regarding claim 7, KOUNO discloses in figure(s) 1-21 the apparatus of claim 1, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: receiving the plurality of resting voltages, wherein the plurality of resting voltages are collected during a first time section after a buffering time has elapsed from an end time point of charging of the plurality of battery cells, and/or wherein the plurality of resting voltages are collected during a second time section from an end time point of discharging of the plurality of battery cells (para. 36 - a temporal change of electrical current and voltage outputted from a battery in a rest period after discharging. ΔVa is an amount of change of output voltage of the battery from a first start time point at or after the end of rest period to a first time when a first duration ta has elapsed from the first start time point; fig. 3).
Regarding claim 10, KOUNO discloses in figure(s) 1-21 the apparatus according to claim 1, further comprising a sensor (voltage sensor 131; figs. 6,16) configured to collect the plurality of resting voltages for each of the plurality of battery cells, in the resting period after charging or discharging of the plurality of battery cells is completed (para. 7 - rest period after a battery finishes charging or discharging; fig. 11).
Regarding claim 11, KOUNO discloses in figure(s) 1-21 the apparatus according to claim 1, wherein the apparatus comprises an interface that is configured to communicate with a sensor (voltage sensor 131; figs. 6,16) to receive the plurality of resting voltages for each of the plurality of battery cells, wherein the sensor is configured to collect the plurality of resting voltages for each of the plurality of battery cells, in the resting period after charging or discharging of the plurality of battery cells is completed (para. 7 - rest period after a battery finishes charging or discharging; fig. 11).
Regarding claim 13, KOUNO discloses in figure(s) 1-21 a battery management method implemented via execution of computing instructions configured to run at one or more processors (100; figs. 14-16), the method comprising:
receiving a plurality of resting voltages (rest voltage v. time; fig 18) collected for each of a plurality of battery cells (200; figs. 14-16, 6), in a resting period (rest period S1701; fig. 17) after charging or discharging of the plurality of battery cells is completed (para. 7 - rest period after a battery finishes charging or discharging; fig. 11);
calculating a plurality of voltage deviations (∆Va, ∆Vb) for each of the plurality of battery cells based on a difference between a representative value of the plurality of resting voltages and each of the plurality of resting voltages for each of the plurality of battery cells (calculate ∆Va, ∆Vb step S1702; figs. 12, 17-19;
calculating rates of change of the plurality of voltage deviations for each of the plurality of cells (∆Va/ta, ∆Vb/tb; para. 78 - change rate in the temporal variation curve of voltage; figs. 18-19); and
diagnosing a state of at least one of the plurality of battery cells based on a rate of change of the at least one battery cell (calculate SOH step S1703; fig. 17; SOH_E, R_E fig. 12).
Regarding claim 14, KOUNO discloses in figure(s) 1-21 the method of claim 13, wherein the diagnosing of the state of the at least one of the plurality of battery cell comprises calculating an average and a standard deviation of the rates of change of the plurality of battery cells (para. 78 - change rate in the temporal variation curve of voltage), and diagnosing whether or not a low voltage cell exists among the plurality of battery cells based on the average and the standard deviation (para. 60 - if there exists an outlier battery cell, the average value is dragged by the outlier cell; clm. 6 - calculating a second standard deviation of the voltage).
Regarding claim 15, KOUNO discloses in figure(s) 1-21 the method of claim 14, wherein the diagnosing of the state of at least one of the plurality of battery cells comprises calculating a standard score for at least one of the plurality of battery cells based on the average and the standard deviation, and diagnosing the at least one battery cell as the low voltage cell when the standard score of at least one battery cell is lower than a lower limit threshold (para. 59 - temporal variation of output voltage converges to an average of each battery cell. when estimating SOH or internal resistance of the assembled battery, it is typically assumed to use the average of each battery cell; para. 57 - SOH_E is calculated for each battery cell forming the assembled battery, and a standard deviation SOH_E_σ of SOH_E is also calculated).
Regarding claim 19, KOUNO discloses in figure(s) 1-21 the method of claim 13, wherein the receiving of the plurality of resting voltages comprises receiving a plurality of resting voltages collected during a first time section after a buffering time has elapsed from an end time point of charging of the plurality of battery cells, and/or receiving a plurality of resting voltages collected during a second time section from an end time point of discharging of the plurality of battery cells (para. 36 - a first start time point at or after the end of rest period to a first time when a first duration ta has elapsed from the first start time point; @ta, tb; figs. 18-19).
Regarding claim 20, KOUNO discloses in figure(s) 1-21 a battery management system comprising the battery management apparatus according to claim 1, the battery management system comprising: a charger/discharger (charger; fig. 13) configured to charge or discharge a plurality of battery cells.
Claim(s) 1, 4-5, 9, 12-13 and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by HAN et al. (KR 20210059566; hereinafter HAN).
Regarding claim 1, HAN discloses in figure(s) 1-4 a battery management apparatus comprising:
a controller comprising one or more processors, and one or more computer-readable media storing computing instructions that (pg. 5 - battery system 1, the measurement unit 21, the detection unit 22, or the control unit 23 is performed by a processor implemented by one or more central processing units CPU), when executed on the one or more processors, cause the one more processors to perform:
receiving a plurality of resting voltages for each of a plurality of battery cells (100; fig. 2), in a resting period after charging or discharging of the plurality of battery cells is completed (pg. 6 - voltage values measured in a rest period in which the battery module 10 is in a dormant state; fig. 4),
calculating a plurality of voltage deviations for each of the plurality of battery cells based on a difference between a representative value of the plurality of resting voltages and each of the plurality of resting voltages for each of the plurality of battery cells (pg. 4 - detection unit 22 may calculate a voltage change amount using only voltage values measured in a rest period in which the battery module 10 is in a rest state among the received cell voltage values; fig. 3),
calculating rates of change of the plurality of voltage deviations for each of the plurality of cells (pg. 6 - a voltage change amount (or voltage change rate) deviation for each cell 100 is calculated; fig. 4), and
diagnosing a state of at least one of the plurality of battery cell based on a rate of change over time of the at least one battery cell (pg. 6 - in the step S14, the abnormal cell detection apparatus 20 may detect a cell in which the voltage change rate deviation in a predetermined time interval is equal to or greater than a predetermined value among the plurality of cells 100 as an abnormal cell).
Regarding claim 4, HAN discloses in figure(s) 1-4 the apparatus of claim 1, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating a plurality of second deviations based on a difference between a representative value of the plurality of voltage deviations and each of the plurality of voltage deviations (pg. 4 - a difference value between the average value of the voltage variation amounts of all the cells 100 constituting the battery module 10 and the voltage variation amount of each cell 100.), and diagnosing whether or not a low voltage cell exists (pg. 4 - an abnormal cell (for example, a cell in which an internal short occurs) among the cells 100 constituting the battery module 10. ) Can be detected) among the plurality of battery cells based on rates of change of the plurality of voltage deviations for each of the plurality of battery cells, and the plurality of second deviations (pg. 6 - a voltage change amount (or voltage change rate) deviation for each cell 100 is calculated; fig. 4).
Regarding claim 5, HAN discloses in figure(s) 1-4 the apparatus of claim 4, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: setting a normal range of the plurality of battery cells based on the plurality of second deviations and the plurality of rates of change over time, and diagnosing a battery cell having a second deviation or rate of change outside the normal range as the low voltage cell (pg. 6 - abnormal cell detection device 20 registers at least one cell 100 selected in the order of the highest absolute deviation of the voltage change rate in each time interval as the abnormal cell candidate group, and the abnormal cell in one or more time intervals. The cell 100 registered as a candidate group may be detected as an abnormal cell.).
Regarding claim 9, HAN discloses in figure(s) 1-4 the apparatus of claim 1, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: diagnosing states of each of the plurality of battery cells (abs. - An apparatus for detecting an abnormal cell of a battery module) based on rates of change over time of the plurality of battery cell cells (pg. 4 - calculates a voltage change rate deviation by dividing each voltage change amount deviation by a time length of a corresponding time section).
Regarding claim 12, HAN discloses in figure(s) 1-4 the apparatus of claim 1, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: diagnosing at least one of the plurality of battery cells as a low-voltage cell and/or abnormal cell, and in response to the diagnosis, performing any of i) sending a warning alarm to a user device (pg. 5 - detection unit 22 may transmit identification information of the cell 100 identified as an abnormal cell by including it in the alarm signal) and/or display terminal regarding the at least one diagnosed cell, ii) opening or cutting power to the at least one diagnosed cell, iii) electrically grounding the at least one diagnosed cell, iv) limiting or modifying performance, output, and/or operating mode of an electrical device using the at least one diagnosed cell, and v) sending information regarding the at least one diagnosed cell to an external server.
Regarding claim 13, HAN discloses in figure(s) 1-4 a battery management method implemented via execution of computing instructions configured to run at one or more processors (pg. 5 - battery system 1, the measurement unit 21, the detection unit 22, or the control unit 23 is performed by a processor implemented by one or more central processing units CPU), the method comprising:
receiving a plurality of resting voltages collected for each of a plurality of battery cells (100; fig. 2), in a resting period after charging or discharging of the plurality of battery cells is completed (pg. 6 - voltage values measured in a rest period in which the battery module 10 is in a dormant state; fig. 4);
calculating a plurality of voltage deviations for each of the plurality of battery cells based on a difference between a representative value of the plurality of resting voltages and each of the plurality of resting voltages for each of the plurality of battery cells (pg. 4 - detection unit 22 may calculate a voltage change amount using only voltage values measured in a rest period in which the battery module 10 is in a rest state among the received cell voltage values);
calculating rates of change of the plurality of voltage deviations for each of the plurality of cells (pg. 6 - a voltage change amount (or voltage change rate) deviation for each cell 100 is calculated; fig. 4); and
diagnosing a state of at least one of the plurality of battery cells based on a rate of change of the at least one battery cell (pg. 6 - in the step S14, the abnormal cell detection apparatus 20 may detect a cell in which the voltage change rate deviation in a predetermined time interval is equal to or greater than a predetermined value among the plurality of cells 100 as an abnormal cell).
Regarding claim 16, HAN discloses in figure(s) 1-4 the method of claim 13, wherein the diagnosing of the state of at least one of the plurality of battery cells comprises calculating a plurality of second deviations based on a difference between a representative value of the plurality of voltage deviations and each of the plurality of voltage deviations (pg. 4 - a difference value between the average value of the voltage variation amounts of all the cells 100 constituting the battery module 10 and the voltage variation amount of each cell 100.), and diagnosing whether or not a low voltage cell exists among the plurality of battery cells based on the rates of change of the plurality of voltage deviations for each of the plurality of battery cells, and the plurality of second deviations (pg. 6 - a voltage change amount (or voltage change rate) deviation for each cell 100 is calculated; fig. 4).
Regarding claim 17, HAN discloses in figure(s) 1-4 the method of claim 16, wherein the diagnosing of the state of at least one of the plurality of battery cells comprises setting a normal range of the plurality of battery cells based on the plurality of second deviations and the plurality of rates of change over time, and diagnosing a battery cell having a second deviation or rate of change outside the normal range as the low voltage cell (pg. 6 - abnormal cell detection device 20 registers at least one cell 100 selected in the order of the highest absolute deviation of the voltage change rate in each time interval as the abnormal cell candidate group, and the abnormal cell in one or more time intervals. The cell 100 registered as a candidate group may be detected as an abnormal cell.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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) 6, 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KOUNO in view of STEFANOPOULOU et al. (US 20230029405).
Regarding claim 6, KOUNO teaches in figure(s) 1-21 the apparatus of claim 1,
KOUNO does not teach explicitly wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating the plurality of voltage deviations for each of the plurality of battery cells based on a median of the plurality of resting voltages for each of the plurality of battery cells, and estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis.
However, STEFANOPOULOU teaches in figure(s) 1-25 wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating the plurality of voltage deviations for each of the plurality of battery cells based on a median of the plurality of resting voltages for each of the plurality of battery cells (para. 55 - median plotted; fig. 8), and estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis (para. 31 - statistical model can comprise a regression model).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of KOUNO by having wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating the plurality of voltage deviations for each of the plurality of battery cells based on a median of the plurality of resting voltages for each of the plurality of battery cells, and estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis as taught by STEFANOPOULOU in order to provide use of known technique to improve similar devices (methods, or products) in the same way as evidenced by "statistical model can comprise a regression model. In the method, the optimized battery formation protocol can be determined by comparing resistances measured at states-of-charge less than or equal to 15%." (abstract).
Regarding claim 8, KOUNO teaches in figure(s) 1-21 the apparatus of claim 1,
KOUNO does not teach explicitly wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating rates of change of the plurality of voltage deviations for each of the plurality of cells through a regression analysis.
However, STEFANOPOULOU teaches in figure(s) 1-25 wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating rates of change of the plurality of voltage deviations for each of the plurality of cells through a regression analysis (para. 31 - statistical model can comprise a regression model).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of KOUNO by having wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to perform: calculating rates of change of the plurality of voltage deviations for each of the plurality of cells through a regression analysis as taught by STEFANOPOULOU in order to provide "statistical model can comprise a regression model. In the method, the optimized battery formation protocol can be determined by comparing resistances measured at states-of-charge less than or equal to 15%." (abstract).
Regarding claim 18, KOUNO teaches in figure(s) 1-21 the method of claim 13,
KOUNO does not teach explicitly wherein the calculating of the plurality of voltage deviations for each of the plurality of battery cells comprises calculating the plurality of voltage deviations based on a median of the plurality of resting voltages for each of the plurality of battery cells and the calculating of the plurality of rates of change of the plurality of voltage deviations comprises estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis.
However, STEFANOPOULOU teaches in figure(s) 1-25 wherein the calculating of the plurality of voltage deviations for each of the plurality of battery cells comprises calculating the plurality of voltage deviations based on a median of the plurality of resting voltages for each of the plurality of battery cells (para. 55 - median plotted; fig. 8), and the calculating of the plurality of rates of change of the plurality of voltage deviations comprises estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis (para. 31 - statistical model can comprise a regression model).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of KOUNO by having wherein the calculating of the plurality of voltage deviations for each of the plurality of battery cells comprises calculating the plurality of voltage deviations based on a median of the plurality of resting voltages for each of the plurality of battery cells and the calculating of the plurality of rates of change of the plurality of voltage deviations comprises estimating slopes of each of the plurality of rates of change for each of the plurality of battery cells through a linear regression analysis as taught by STEFANOPOULOU in order to provide "statistical model can comprise a regression model. In the method, the optimized battery formation protocol can be determined by comparing resistances measured at states-of-charge less than or equal to 15%." (abstract).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Myers et al. (US 20210408615) discloses "Monitoring System For Series-Connected Battery Cells".
EO et al. (US 20210098998) discloses "A battery system includes a battery module that includes a plurality of battery cells, a monitoring unit configured to monitor a parameter of each of the battery cells".
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKM ZAKARIA whose telephone number is (571)270-0664. The examiner can normally be reached on 8-5 PM (PST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Judy Nguyen can be reached on (571) 272-2258. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AKM ZAKARIA/
Primary Examiner, Art Unit 2858
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