DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 11/11/2025.
Status of the Claims
Claims 1, 4-13,18-19,21-22, and 25-26 are pending.
Claims 1 is amended.
Response to Arguments
Rejections Under 35 U.S.C. §103
Applicant's arguments, see remarks pages 6-9, filed 11/11/2025 with respect to the rejection(s) of Claims under 35 U.S.C.§103 has been considered, and are moot because the amendment has necessitated a new ground of rejections. The new rejections are set forth below.
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.
Claims 1, 4-13,18-19,21-22, and 25-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aliyev et al. (US 2017/0108551 A1, hereinafter Aliyev).
Regarding claim 1, Aliyev teaches,
A test system (Aliyev, Figure 2, Testing system 20) for determining battery performance (Aliyev, Figure 1, step 18, predict battery performance) during development of a battery configuration in a test environment (Aliyev, Figure 1, Step 19, [0030] “the predicted outputs from the acts represented by block 18 may be useful feedback from a design standpoint for early diagnosis and correction of engineering and/or battery manufacturing issues. Accordingly, the method 10 may include modifying (block 19) engineering design and/or manufacturing processes associated with the batteries of the type for which the predictions were made”. NOTE: figure 1, step 18 reads on the testing and adjusting battery parameters during the development of battery configuration),
the test system comprising at least one communication interface (Aliyev, Figure 2, Battery test computer 22 includes user interface 30) and at least one processing device (Aliyev, Figure 2, processing circuit 28),
wherein the test system is configured for receiving operating data (Aliyev, Figure 1, Step 14, collect test data) indicative of at least one test protocol via the communication interface (Aliyev, Figures 2, and 4, [0057] FIG. 4 depicts an example embodiment of a method 80 for building, training, and updating a statistical model in accordance with an aspect of the present disclosure. In certain embodiments, the method 80 may be an automated algorithm programmed in any suitable programming platform, and may be implemented as processor executable instructions stored on the memory circuitry 24. Accordingly, the method 80 may be one or more processes or applications run on the battery test computer 26”).
wherein the test system is configured for receiving battery performance input data via (Aliyev, Figure 1, Step 14) the communication interface (Aliyev, 0033] The battery test computer 22 may also include a user interface system 30, which may include one or more devices communicatively coupled to the memory 24 and
processor 28 to enable a user to provide input to the battery test computer 22 and to enable the battery test computer 22 to provide outputs to the user.”).
wherein the processing device is configured for determining at least one predicted time series of at least one state variable indicative of battery performance (Aliyev, Figure 1, Step 16, 18, [0029] “the predicted outcome may be a value of a particular parameter, such as a predicted discharge capacity that the battery will have at the end of the test period”) based on the battery performance input data and on the operating data using at least one data driven model (Aliyev, Figure 1, Step 16, and figure 3, [0045] “As illustrated, at a first time point after initiation of the testing procedure, instructions associated with the prediction module 46 (e.g., a statistical model) may be run (block 64), using the battery test data collected from the initiation time to the first time point as a predictive input”), and
wherein the test system is configured for providing at least parts of the predicted time series of the state variable, wherein the at least one test protocol comprises information about at least one battery performance test, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, (Aliyev, [0060] “The automated algorithm, in some embodiments, may construct different features from normalized capacity curves. The algorithm may use normalized discharge capacity per week, normalized charge capacity per week to determine an efficiency per week (the discharge capacity divided by the charge capacity), and may use these weekly values to then generate function curves and rate of change curves. For different time windows (e.g., different week
ranges), the algorithm may calculate averages and standard deviations of various rates of change, normalized capacity values, and so forth. Example features may include rate of change of charge capacity between certain time periods
(e.g., between weeks 6 and 7 of a test that lasts 18 weeks), an average efficiency between certain time periods (e.g., between weeks 3 and 5), and a standard deviation of discharge capacity between certain time periods ( e.g., weeks
6 and 9.” NOTE: Algorithm predicted battery charge /discharge capacity data for a time period in various ranges reads on “time series” of state variables. The instant application Publication disclose in [0017] the term “time series” as “The term specifically may refer, without limitation, to a chronological ordered data stream”)
And wherein the test system is configured to predict a battery lifetime and categorize a battery depending on the state variable at a future time point. (Aliyev, Figure 8, [0076] As set forth above, in a further aspect of the present disclosure, the predictions output by the prediction module 46 may also include predicted battery characteristics. Referring again to the examples set forth in FIGS. 6 and 7, the prediction module 46 may also output (e.g., to the user) a predicted final discharge capacity (e.g., a discharge capacity at the end of the standardized test). FIG. 8 illustrates an example output 140 of final battery discharge capacity. [0077] The output 140 of FIG. 8 includes a plot 142 of final discharge capacity as a function of distance to the hyperplane 96 (see FIG. 5). More specifically, the example distance 102 shown in FIG. 5 from the battery sample's location on the plot 88 to the hyperplane 96 may be used in combination with linear regression to predict the future discharge capacity, as shown in FIG. 8”.
if the state variable fulfills or does not fulfill predetermined or predetermined conditions (Aliyev, Figure 5, The prediction is made with greater confidence as the data point is farther away from the hyperplane 96; i.e., a distance 102 to the hyperplane 96 is large. Further, the prediction has a relatively low confidence factor if the data point is close to the hyperplane 96 (e.g., inside the margin
99). For example, the pass/fail status of the data points 98 each encompassed by a square would be predicted with a relatively lower confidence. Accordingly, each prediction may have a confidence factor that is commensurate with its distance from the hyperplane 96” NOTE: “pass/fail” status and “confidence level” reads on preconditions to be fulfilled).
Regarding claim 4, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the operating data indicative of at least one test protocol comprises at least one sequence of different charge cycles and/or discharge cycles(Aliyev, [0060] “The automated algorithm, in some embodiments, may construct different features from normalized capacity curves. The algorithm may use normalized discharge capacity per week, normalized charge capacity per week to determine an efficiency per week (the discharge capacity divided by the charge capacity), and may use these weekly values to then generate function curves and rate of change curves. [0069] The AK3.4 test is an endurance test that helps determine the ability of the AGM lead-acid battery to deliver energy under high cyclic conditions during its lifetime. During these 18 weeks, the particular
battery sample is subjected to full and partial charge discharge cycles. Each week, the discharge and charge capacity is measured”)
Regarding claim 5, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. (Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).
Regarding claim 6, Aliyev teaches the test system according to claim 1,
Aliyev further teaches, wherein the processing device is configured for using the test protocol as an input parameter for determining the predicted time series of the state variable with the data driven model (Aliyev, Figure 1-4, [0031] “The battery test computer 22 also includes one or more processor 28 configured to execute the instructions associated with the battery test management system 26 to perform certain routines to predict battery test outcomes, among other functions. [0039] [0039] As described herein, the prediction module 46 of the battery test management system 26 may include one or more statistical models constructed from historical battery test data obtained from battery tests performed on a number of battery sample” ) , and/or wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for selecting one of the data driven models for determining the predicted time series of the state variable depending on the test protocol. (Aliyev, Figure 1, Step 16, 18, [0029] “the predicted outcome may be a value of a particular parameter, such as a predicted discharge capacity that the battery will have at the end of the test period” )
Regarding claim 7, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the processing device is configured for using the battery performance input data as input parameter (Aliyev, Figure 1, Step 14) for determining the predicted time series of the state variable with the data driven model. (Aliyev, Figure 1, Step 16).
Regarding claim 9, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the battery performance input data comprises data generated in response to the test protocol. (Aliyev, Figure 1, Step14- collect test data during test”).
Regarding claim 10, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the test protocol is predefined. (Aliyev, Figure 1-2, “user may initiate testing of a battery in accordance with a battery testing standard, and the battery test management system 26 may automatically initiate data collection and storage of the battery test data as the test progressed”).
Regarding claim 11, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the data driven model was parametrized based on operating data indicative of the at least one test protocol and battery performance input data. (Aliyev, Figures 2, and 4, [0057] FIG. 4 depicts an example embodiment of a method 80 for building, training, and updating a statistical model in accordance with an aspect of the present disclosure. In certain embodiments, the method 80 may be an automated algorithm programmed in any suitable programming platform, and may be implemented as processor executable instructions stored on the memory circuitry 24. Accordingly, the method 80 may be one or more processes or applications run on the battery test computer 26”).
Regarding claim 12, Aliyev teaches the test system according to claim 11,
Aliyev further teaches wherein the data driven model uses knowledge of past and future charge-discharge-cycles following the at least one test protocol to predict future battery performance (Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).
Regarding claim 13, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the data driven model has a time memory and/or the data driven model is a time dependent model (Aliyev, Figure 4, [0058] The illustrated method 80 includes the use of training data 82 and a feature selection process (block 84) to generate one or more statistical models (e.g., battery test SVMs) along with a subset of features (e.g., battery characteristics) that serve as predictive inputs on which the statistical models may base predictions. [0060] The algorithm may use normalized discharge capacity per week, normalized charge capacity per week to determine an efficiency per week (the discharge capacity
divided by the charge capacity), and may use these weekly values to then generate function curves and rate of change curves. For different time windows (e.g., different week ranges), the algorithm may calculate averages and standard
deviations of various rates of changes”).
Regarding claim 14, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the test protocol comprises information about at least one battery performance test (Aliyev, Figure 1,step 14), wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein in the battery performance test discharge-charge curves are determined for each cycle. . [0060] The feature identification algorithm may
also include performance of certain processing steps, such as averaging of the normalized charge and discharge capacities, calculation of the standard deviation of these values, and so forth. The automated algorithm, in some embodiments, may construct different features from normalized capacity curves. The algorithm may use normalized discharge capacity per week, normalized charge capacity per week to determine an efficiency per week (the discharge capacity divided by the charge capacity), and may use these weekly values to then generate function curves and rate of change curves. For different time windows (e.g., different week
ranges), the algorithm may calculate averages and standard deviations of various rates of change”).
Regarding claim 18, Aliyev teaches the test system according to claim 1,
Aliyev further teaches A test rig (Aliyev, Figure 2, testing system 20),configured for performing at least one battery performance test on at least one battery based on at least one test protocol wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles(Aliyev, Figure 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data”).wherein the battery performance test comprises determining of discharge- charge curves for each cycle, wherein the test rig comprises at least one communication interface configured for providing operating data indicative of the test protocol and battery performance input data to at least one test system according to claim 1. (Aliyev, Figure 1, Step 16, 18, [0029] “the predicted outcome may be a value of a particular parameter, such as a predicted discharge capacity that the battery will have at the end of the test period”).
Regarding claim 19, Aliyev teaches the test system according to claim 1,
Aliyev further teaches A method for determining at least one data driven model for determining battery performance (Aliyev, Figure 1, step 14, 16, 18 during development of a battery configuration in a test environment (Aliyev, Figure 2, [0026] he battery test in accordance with the acts represented by block 12 may include, for example, connecting the battery to be tested to a testing apparatus, examples of which are described in further detail below with respect to FIG. 2.), wherein the method is carried out by the test system of claim 1, (Aliyev, Figure 1, Step 19, [0030] “the predicted outputs from the acts represented by block 18 may be useful feedback from a design standpoint for early diagnosis and correction of engineering and/or battery manufacturing issues. Accordingly, the method 10 may include modifying (block 19) engineering design and/or manufacturing processes associated with the batteries of the type for which the predictions were made”. NOTE: figure 1, step 18 reads on the testing and adjusting battery parameters during the development of battery configuration),
wherein the method comprises training the data driven model with at least one training data set, wherein the training data set comprises historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. Aliyev, Figure 2, 4, [0038] In one particular embodiment, at least a portion of the battery test management system 26 may be implemented as a specially configured battery test support vector machine (SVM) that has been trained using certain types of battery test data. [0039] As described herein, the prediction module 46 of the battery test management system 26 may include one or
more statistical models constructed from historical battery test data obtained from battery tests performed on a number of battery samples. The historical battery test data may be data that spans an entire test duration for each battery
sample”).
Regarding claim 21, Aliyev teaches the test system according to claim 1,
Aliyev further teaches
A computer implemented method for determining battery performance during development of a battery configuration in a test environment (Aliyev, Figure 2), wherein in the method at least one test system according to claim 1 is used, the method comprising:
a) retrieving operating data indicative of at least one test protocol via at least one communication interface (Aliyev, Figure 1, Step 14),;
b) retrieving battery performance input data via the communication interface Aliyev, Figure 2, Battery test computer 22 includes user interface 30,[0033] The battery test computer 22 may also include a user interface system 30, which may include one or more devices communicatively coupled to the memory 24 and
processor 28 to enable a user to provide input to the battery test computer 22 and to enable the battery test computer 22 to provide outputs to the user);
c) determining a predicted time series of a state variable indicative of battery performance based on the battery performance input data and on the operating data using a data driven model by using a processing device d) providing at least parts of the predicted time series of the state variable. (Aliyev, Figure 1, step 18, predict battery performance, Figure 1, Step 16, 18, [0029] “the predicted outcome may be a value of a particular parameter, such as a predicted discharge capacity that the battery will have at the end of the test period”).
Regarding claim 22, Aliyev teaches the test environment according to claim 21,
Aliyev further teaches A computer program for determining battery performance during development of a battery configuration in a test environment, configured for causing a computer or computer network to perform the method for determining battery performance (Aliyev, Figures 2, and 4, [0057] FIG. 4 depicts an example embodiment of a method 80 for building, training, and updating a statistical model in accordance with an aspect of the present disclosure. In certain embodiments, the method 80 may be an automated algorithm programmed in any suitable programming platform, and may be implemented as processor executable instructions stored on the memory circuitry 24. Accordingly, the method 80 may be one or more processes or applications run on the battery test computer 26”).during development of a battery configuration in a test environment according to the preceding claim, when executed on the computer or computer network, wherein the computer program is configured to perform at least steps a) to d) of the method for determining battery performance during development of a battery configuration in a test environment according to claim 21the preceding claim. (Aliyev, Figure 1, Step 19, [0030] “the predicted outputs from the acts represented by block 18 may be useful feedback from a design standpoint for early diagnosis and correction of engineering and/or battery manufacturing issues. Accordingly, the method 10 may include modifying (block 19) engineering design and/or manufacturing processes associated with the batteries of the type for which the predictions were made”. NOTE: figure 1, step 18 reads on the testing and adjusting battery parameters during the development of battery configuration),
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Aliyev and in view of HOLME, Tim; (WO 2019/017991 A1, hereinafter Holme, previously cited).
Regarding claim 8, Aliyev teaches the test system according to claim 1,
Aliyev further teaches wherein the processing device comprises a plurality of data driven models wherein the processing device is configured for analyzing the battery performance input data, (Aliyev, Figure 1, [0039] As described herein, the prediction module 46 of the battery test management system 26 may include one or more statistical models constructed from historical battery test data obtained from battery tests performed on a number of battery samples”).
Aliyev is silent on wherein the analyzing comprises determining at least one material characteristic, wherein at least one of the data driven models is selected based on the material characteristic.
However, Holme teaches wherein the analyzing comprises determining at least one material characteristic, wherein at least one of the data driven models is selected based on the material characteristic. (Holme, Figure 1-2, Table 2, “Input features Type, Cell chemistry/ cell constructions. Related input/ features Detail about battery cell constructions other than general chemistry” [0151] Examples of input parameters for battery models according to the disclosure are summarized in TABLE 2. According to the embodiments a specific battery model may
include as an input parameter all of the input parameters listed in TABLE 2, or only a portion of these input).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Aliyev’s method models to incorporate one of the model using battery chemistry /cell construction data to predict battery performance characteristics as taught by Holme with the benefit of measuring battery performance based on cell chemistry and predict accurate performance result. (Holme, [0134]-[0135]).
Regarding claim 25, Aliyev teaches the test system according to claim 1,
Aliyev teaches wherein the battery performance input data comprises metadata relating to cell set-up (Aliyev, Figure 1-2, Step 12-16)
Aliyev is silent on wherein the battery performance input data comprises metadata relating to one or more of cathode material.
However, Holme teaches wherein the battery performance input data comprises metadata relating to one or more of cathode material Holme, Figure 1-2, Table 2, “Input features Type, Cell chemistry/ cell constructions. Related input/ features Detail about battery cell constructions other than general chemistry” [0151] Examples of input parameters for battery models according to the disclosure are summarized in TABLE 2. According to the embodiments a specific battery model may include as an input parameter all of the input parameters listed in TABLE 2, or only a portion of these input).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Aliyev’s method models to incorporate one of the model using battery chemistry /cell construction data to predict battery performance characteristics as taught by Holme with the benefit of measuring battery performance based on cell chemistry and predict accurate performance result. (Holme, [0134]-[0135]).
Conclusions
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feng et al. (CN 103956781 B) recites “the invention provides a development device for a balancing algorithm of a power battery pack. The development device includes a charger, a discharger, and a plurality of single-body balance controllers. The charger is used for charging a tested battery pack. The discharger is used for discharging the tested battery pack. The number of the single-body balance controllers is identical with the number of single-body batteries of the tested battery pack and one single-body balance controller is used for detecting the temperature and voltage of one single-body battery. The development device for the balancing algorithm of the power battery pack is capable of rapidly performing development of battery management system algorithms including the balancing algorithm. The development device is capable of helping related enterprises and research institutes to improve efficiency and save cost during development of the power battery management system algorithm”.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9:00AM-6 PM.
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/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 3/3/2026