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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/26/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 04/30/2026.
Status of the Claims
Claims 1-18 are pending.
Claims 1-7, and13-18 are amended.
Claim 19 is canceled.
Response to Arguments
Claim Interpretation under 35 U.S.C. 112(f)
Applicant’s amendment/arguments see remarks pages 9-11, filed 04/30/2026, with respect to the Claim Interpretation under 35 U.S.C. 112(f) have been fully considered, and are found persuasive. The claim interpretation under 35 U.S.C. 112(f) have been withdrawn.
Rejections Under 35 U.S.C. 112(a)
Applicant’s amendment/arguments see remarks pages 9-11, filed 04/30/2026, with respect to the Claim Interpretation under 35 U.S.C. 112(a) have been fully considered, and are found persuasive. The claim interpretation under 35 U.S.C. 112(a) have been withdrawn.
Rejections Under 35 U.S.C. 112(b)
Applicant’s amendment/arguments see remarks pages 9-11, filed 04/30/2026, with respect to the Claim Interpretation under 35 U.S.C. 112(b) have been fully considered, and are found persuasive. The claim interpretation under 35 U.S.C. 112(b) have been withdrawn.
rejected under 35 U.S.C.§101
Applicant’s arguments see remarks pages 9-11, filed 04/30/2026, with respect to the rejection(s) of Claim 1 under 35 U.S.C.§101 have been fully considered, and are not persuasive. The claim limitation “outputting a third low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries” is just outputting post solution result, however there is nothing indicating how this result is implemented. The claim limitation does not recite how the result is presented to the user, and do not integrate into a practical application such as normal/defect/ failure determination and updating new models to improve accuracy of prediction of low voltage failure of the secondary battery as disclosed in the specification page 28 “The normal/defect determination unit may be updated with new models whenever the data for machine learning is updated, so that it is possible to improve accuracy of the prediction of the low-voltage failure of the secondary battery”.
Therefore, the applicant argument/amendment are not persuasive, and the rejections under 35 U.S.C.§101 is maintained.
Rejections Under 35 U.S.C. §103
Applicant argues in the remarks pages 9-11, filed on 04/30/2026, with respect to the rejection(s) of claim 1 under 35 U.S.C.§103 that
“With regard to claim 1, itrecites, inter alia, the following:
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.
Applicant submits that the cited references do not disclose the above-recited feature of claim 1. In particular, in connection with the "process conditions" of claim 1, the Office Action cites to pages 352-353 of Obeid, in which sixteen different batteries are discharged through "varying loads" and "were drained at 16 different current levels and current profiles." (Office Action, pg. 34). However, the cited portions of Obied merely refer to collecting data sets from the discharging of 16 different batteries. There is no disclosure in which at least one of charging, discharging, and resting process
conditions of a first and second group of batteries is selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target, in the manner that is recited in claim 1.
In addition, the other cited references fail to disclose the above-recited feature of claim 1. For at least this reason, Applicant submits that claim 1 is not obvious over the recited combination of references. Similar arguments can also be made in connection with independent claims 7 and 13.
Accordingly, Applicant submits that claims 1-18 are not obvious over the cited references. As it is believed that all of the rejections set forth in the Official Action have been fully met, favorable reconsideration and allowance are earnestly solicited.”
Examiner’s Response:
Applicant’s arguments see remarks pages 9-11, filed 04/30/2026, with respect to the rejection(s) of Claim 1 under 35 U.S.C.§103 have been fully considered, and are not persuasive.
Obeid teaches in (Page 353, Table 3, Page 352, 4.2, Right col. Top paragraph) that sixteen different batteries were charged/discharged through varying loads, varying current level and varying time period of charging, discharging”. See Table 3. Which implies that each battery had different charging discharging and resting condition. Obeid also teaches one set of training battery and one set of prediction target batteries. each batteries 2, 4,5,6,8,14 are individual “prediction target” see table 3. Algorithm 1 teaches that step 1-4, Algorithm 1: Supervised learning-based battery terminal voltage collapse detection methodology 1: Obtain the simulated training data by solving (l}--(4) 2: Label the data based on SOC level 3: Capture segments of data with overlapping windows: obtain N windows”. “Step 4 i=[l:N] do” of algorithm 1 reads on first second third, and fourth set of batteries. The algorithm can be implemented on any ith battery where i= [1, N] number of different batteries and the training data can be generated using any different third group of batteries under different charging, discharging conditions. This is an algorithm design choice. Obeid teaches in Page 350, left col. Middle paragraph, “each battery may take a different route to battery failure. The overlapping windows that document the different routes to failure are then labelled as either coming from the safe regions or the failure regions, depending on the SOC information as described earlier. The extracted windows are then split into two groups: a training set (TR) and a testing set (TE). The TR constitutes 70% of all windows. We ensure that windows from the same battery/load combination do not appear simultaneously in training or testing sets. We extract data both from simulated battery and load models, and also from tests on real batteries. Table I explains the different constructed datasets”. Therefore, each battery process condition is different and distinct from each other groups. Obeid, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14 and each batteries of 2, 4,5,6,8,14 are individual “prediction target” see table 3.Therefore, Applicant argument is not persuasive. The rejections are maintained.
Claim Rejections- 35 USC §101
U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine,
manufacture, or composition of matter, or any new and useful improvement thereof,
may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 18 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1,
An system for predicting a low-voltage failure of a secondary battery, the system comprising one or more computing devices is configured to perform:
receiving first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as a first training target;
receiving first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as a first prediction target;
performing machine learning on the first training data of the first group of the plurality of the secondary batteries and selecting a main factor among the first training data;
comparing a second low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to a first low-voltage prediction model of the first group of the plurality of the secondary batteries generated using the first training data with first low-voltage determination prediction result of the second group of the plurality of the secondary batteries based on the first measurement data and
finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries;
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k.
receiving second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets;
receiving second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets;
generating a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the optimized low-voltage prediction model of the first group of the plurality of the secondary batteries and the second training data of the third group of the plurality of the secondary batteries; and
outputting a third low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries,
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.
The claim limitations underlined above is abstract idea (a process)
The remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process). Therefore, it is directed to a statutory category, i.e., a process.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers evaluation of mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations).
For example, steps of “predicting a low-voltage failure of a secondary battery”, “performing machine learning on the first training data of the first group of the plurality of the secondary batteries and selecting a main factor among the first training data”;
“comparing a second low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to a first low-voltage prediction model of the first group of the plurality of the secondary batteries generated using the first training data with first low-voltage determination prediction result of the second group of the plurality of the secondary batteries based on the first measurement data” and
“ finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries”;
“generating a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the optimized low-voltage prediction model of the first group of the plurality of the secondary batteries and the second training data of the third group of the plurality of the secondary batteries”; and
represent mathematical concept. The above limitations represent data processing using machine learning algorithm (Specification, pages 13-14) generating/developing model and validating the model with measurement data. (Specification, pages 27-29, “The computing unit receives the stored data values for machine learning, and evaluates and selects main factors for predicting low-voltage failures, and then generates a model.) These steps represent a process that, under its broadest reasonable interpretation, it encompasses a computing unit/computer implementing abstract idea and making valuation/judgement based on the output data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements
“receiving first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as a first training targets;
receiving first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k.
receiving second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets;
receiving second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets
outputting a third low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries,
wherein process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.”;
are data gathering steps for the particular technological environment or field of use. Collecting receiving first, second training data of first, third group, receiving first, second measurement data of a second, fourth groups, receiving the optimized low voltage prediction model, represent mere data gathering steps and data gathering conditions only add an insignificant extra-solution activity to the judicial exception. and outputting result step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "computing unit") needs to be used to carry out the abstract idea. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and do not integrate the judicial exception into a practical application such as normal/defect determination and updating new models to improve accuracy of prediction of low voltage failure
of the secondary battery as disclosed in the specification page 28. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity and insignificant post-solution activity which are simply routine and conventional steps previously known to the pertinent industry. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 2-6, 13-16, 18-19 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-6, 13-16, 18-19 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 2-6, 13-16, 18-19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 7,
A method of predicting a low-voltage failure of a secondary battery, the method comprising:
Inputting, by the one or more computing devices, first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as a first training targets;
Generating, by the one or more computing devices, a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data;
Inputting by the one or more computing devices, first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets;
Comparing, by the one or more computing devices, a low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to the first low- voltage prediction model with a first low-voltage determination prediction result of the secondary battery of the first measurement data and finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model;
Transferring, by the one or more computing devices, the optimized first low voltage prediction model and the optimal value of the weighting factor k;
Inputting by the one or more computing devices, second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the
Generating by the one or more computing devices, a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the transferred optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the second training data;
Inputting, by the one or more computing devices, second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets; and
Outputting, by the one or more computing devices, a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries,
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.
The claim limitations underlined above is abstract idea (a process). The remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process). Therefore, it is directed to a statutory category, i.e., a process.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers evaluation of mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations).
For example, steps of
“A method of predicting a low-voltage failure of a secondary battery,
“generating, by the one or more computing devices, a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data”;
“comparing, by the one or more computing devices, a low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to the first low- voltage prediction model with a first low-voltage determination prediction result of the secondary battery of the first measurement data and finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model”
“generating, by the one or more computing devices, a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the transferred optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the second training data”; represent mathematical concept. The above limitations represent data processing using machine learning algorithm (Specification, pages 13-14) generating/developing model and validating the model with measurement data. (Specification, pages 27-29, “The computing unit receives the stored data values for machine learning, and evaluates and selects main factors for predicting low-voltage failures, and then generates a model.) These steps represent a process that, under its broadest reasonable interpretation, it encompasses a computing unit/computer implementing abstract idea and making valuation/judgement based on the output data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 7 recites additional elements
“inputting, by the one or more computing devices, first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as a first training targets”
“inputting, by the one or more computing devices, first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets”;
“transferring, by the one or more computing devices, the optimized first low voltage prediction model and the optimal value of the weighting factor k”;
“inputting, by the one or more computing devices, second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets”;
“outputting, by the one or more computing devices, a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries”,
“wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.”
These steps are data gathering steps for the particular technological environment or field of use. Collecting receiving first, second training data of first, third group, receiving first, second measurement data of a second, fourth groups, receiving the optimized low voltage prediction model, represent mere data gathering steps and data gathering conditions only add an insignificant extra-solution activity to the judicial exception. and outputting result step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "computing unit") needs to be used to carry out the abstract idea. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and do not integrate the judicial exception into a practical application such as normal/defect determination and updating new models to improve accuracy of prediction of low voltage failure of the secondary battery as disclosed in the specification page 28. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity and insignificant post-solution activity which are simply routine and conventional steps previously known to the pertinent industry. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 8-12 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 7 and also has the routine and conventional structure above of claim 7. In addition, claims 8-12 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 8-12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 17,
A non-transitory machine-readable medium comprising machine-readable instructions encoded thereon for performing a method of predicting the low-voltage failure of the second battery, the method comprising:
inputting first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as first training targets;
generating a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data;
inputting first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as first prediction targets;
comparing a low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to the first low-voltage prediction model with a first low-voltage determination prediction result of the secondary battery of the first measurement data and finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model;
transferring the optimized first low voltage prediction model and the optimal value of the weighting factor k;
inputting second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as second training targets;
generating a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the transferred optimized low-voltage prediction model of the first group of the plurality of the secondary batteries and the second training data;
inputting second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, wherein the fourth group of the plurality of the secondary batteries are selected as second prediction targets; and
outputting a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries,
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.”
The claim limitations underlined above is abstract idea (a process). The remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process). Therefore, it is directed to a statutory category, i.e., a process.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers evaluation of mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations).
For example, steps of
“predicting a low-voltage failure of a secondary battery”, “performing machine learning on the first training data of the first group of the plurality of the secondary batteries and selecting a main factor among the first training data”;
“generating a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data;”
“comparing a low-voltage determination prediction result of the second group of the plurality of the secondary batteries in which the first measurement data is applied to the first low-voltage prediction model with a first low-voltage determination prediction result of the secondary battery of the first measurement data and finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model.”
“generating a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the transferred optimized low-voltage prediction model of the first group of the plurality of the secondary batteries and the second training data;”
represent mathematical concept. The above limitations represent data processing using machine learning algorithm (Specification, pages 13-14) generating/developing model and validating the model with measurement data. (Specification, pages 27-29, “The computing unit receives the stored data values for machine learning, and evaluates and selects main factors for predicting low-voltage failures, and then generates a model.) These steps represent a process that, under its broadest reasonable interpretation, it encompasses a computing unit/computer implementing abstract idea and making valuation/judgement based on the output data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 17 recites additional elements
“A non-transitory machine-readable medium comprising machine-readable instructions encoded thereon for performing” which is a memory, a processor, and means for processing the data signal are conventional components of a computer means for data acquisition and processing. The limitation is just insignificant components of a general data acquisition which fail to amount to significantly more than the judicial exception.
“inputting first training data of a first group of a plurality of secondary batteries measured during a first specific time period of charging, discharging, and resting processes, wherein the first group of the plurality of the secondary batteries are selected as first training targets”;
“inputting first measurement data of a second group of the plurality of the secondary batteries selected during a second specific time period of charging, discharging, and resting processes, wherein the second group of the plurality of the secondary batteries are selected as first prediction targets”
“transferring the optimized first low voltage prediction model and the optimal value of the weighting factor k”;
“inputting second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as second training targets”;
“inputting second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, wherein the fourth group of the plurality of the secondary batteries are selected as second prediction targets”; and
“outputting a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries”,
“wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.”
are data gathering steps for the particular technological environment or field of use. Collecting receiving first, second training data of first, third group, receiving first, second measurement data of a second, fourth groups, receiving the optimized low voltage prediction model, represent mere data gathering steps and data gathering conditions only add an insignificant extra-solution activity to the judicial exception. and outputting result step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "computing unit") needs to be used to carry out the abstract idea. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and do not integrate the judicial exception into a practical application such as normal/defect determination and updating new models to improve accuracy of prediction of low voltage failure
of the secondary battery as disclosed in the specification page 28. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity and insignificant post-solution activity which are simply routine and conventional steps previously known to the pertinent industry. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Obeid et al. hereinafter (Obeid, IDS ref.). “Supervised learning for early and accurate battery terminal voltage collapse detection”, IET Circuits, Devices & Systems, IET journals, 27 February 2020. and in view of Tan et al. “Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 67, NO. 10, OCTOBER 2020.
Regarding Claim 1, Obeid teaches,
A system (Obeid, Figure 10, Experimental setup of batte1y discharging, Page 352, left column, “actual runtime operation of the proposed algorithm may happen on a BMS”) for predicting a low-voltage failure of a secondary battery (Obeid, Figure 10, Page 348, left col. lower middle paragraph, “we approach the problem of battery failure prediction from a pattern recognition perspective. also for this paper, by ' battery failure' we mean 'battery terminal voltage collapse. The battery's terminal voltage patterns are monitored”. Using an algorithm by the BMS see Page 350, Right col. Middle paragraph, “the supervised learning-based battery terminal voltage collapse detection methodology Algorithm 1 (see below)”. Figure 6”. Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries reads on “secondary battery” (Obeid, Page347, left col. Top paragraph introduction:” Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries,” NOTE: It is well known in the art that for Electric Vehicle /(EV) “rechargeable batteries/ Lithium-ion batteries” are used as “secondary batteries”) the system comprising one or more computing devices (Obaid, Figure 10, While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer”) configured to perform:
receiving first training data (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 1: “Obtain the simulated training data” of a first group of a plurality of secondary batteries (Obeid, Figure 6, Page 348, Right Col. top paragraph, “the training is conducted based on data simulated using a mathematical model for a 4 V, 850 mAh Li-ion battery” NOTE: “4 V, 850 mAh Li-ion battery” represents a first group of plurality of battery used for training data. See Page 352, right column top paragraph, “Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process.” this algorithm can be applied to plurality of same group of batteries.) measured during a first specific time period of charging, discharging, and resting processes (Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries, (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).”), wherein the first group of the plurality of the secondary batteries are selected as a first training targets (NOTE: Battery number 1 is first group training target trained by NN. see (Obeid, Page 352, Bottom paragraph, “we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1”)
receiving first measurement data of a second group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 3-5: Step 3: Capture segments of data with overlapping windows: obtain N windows, Step 4: for i= [l: N]do, and Step 5: dataset: - raw data”) selected during a second specific time period of charging, discharging, and resting processes (Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries,)
wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each batteries 2, 4,5,6,8,14 are individual “prediction target” see table 3 ) ;
(Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 9-11: Step 10-Train model. and selecting a main factor among the first training data; (Obeid, Figure 6, Page 352, F 1 score, equation 9, Table 2, Page 351, Right Col, bottom paragraph, “All three metrics are shown in Table 2. The recall and precision are defined in (7) and (8), respectively. Moreover, the F 1 score, defined in (9), is included in Table 2 as well).
comparing a second low-voltage determination prediction result of the second group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 11-13:” 11: Test model on simulated data 12: Pre-process real data,13: Test/use model on real data”) in which the first measurement data is applied to a first low-voltage prediction model of the first group of the plurality of the secondary batteries generated using the first training (Algorithm 1, step 1-3) data with first low-voltage determination prediction result of the second group of the plurality of the secondary batteries based on the first measurement data (Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model) (Algorithm 1, Step 13) and
receiving second training data of a third group of the plurality of the secondary batteries (Algorithm 1 step 1-4, Algorithm 1: Supervised learning-based battery terminal voltage collapse detection methodology 1: Obtain the simulated training data by solving (l}--(4) 2: Label the data based on SOC level 3: Capture segments of data with overlapping windows: obtain N windows”. “Step 4 i=[l:N]do” of algorithm 1 reads on “ third group”. The algorithm can be implemented on any ith battery where i= [1, N] number of different batteries and the training data can be generated using any different third group of batteries under different charging, discharging conditions. This is an algorithm design choice) measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets; (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).” Each battery has different time).
receiving second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 4-7: input raw data, step 4 i= [1, N] Page 350, left col. Bottom paragraph, “the raw voltage values after normalization are used as features” NOTE: ”. “Step 4 i=[l:N]do” of algorithm 1 reads on “ fourth group”. where N can vary with any nth no of batteries. each battery group has different time period, different current, different loads, which implies that the discharge, charging cycle and rest process will be different for different batteries. See table 3, each battery has different condition and parameters),
wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). To adapt our classification model to the real scenarios, we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each battery 2, 4,5,6,8,14 are individual “prediction target”. Battery 4, could be 4th group and a second prediction target. see table 3);
generating a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the optimized low-voltage prediction model (Obeid, Figure 6) of the first group of the plurality of the secondary batteries and the second training data of the third group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model); and
outputting a third low-voltage determination prediction result of the third group of the plurality of the secondary batteries (Obeid, Figure 6 (See below), Page 350, Right Col. Algorithm 1, Step 11: Test model on simulated data, Step 12: Pre-process real data, 13: Test/use model on real data), in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target. (Obeid, Page 350, left col. Middle paragraph, “each battery may take a different route to battery failure. The overlapping windows that document the different routes to failure are then labelled as either coming from the safe regions or the failure regions, depending on the SOC information as described earlier. The extracted windows are then split into two groups: a training set (TR) and a testing set (TE). The TR constitutes 70% of all windows. We ensure that windows from the same battery/load combination do not appear simultaneously in training or testing sets. We extract data both from simulated battery and load models, and also from tests on real batteries. Table I explains the different constructed datasets” NOTE: each battery process condition is different from others and each batteries are charged/discharged under different load, current voltage and time period.. for example see (Obeid, Page 353, Table 3, Page 352, 4.2, Right col. Top paragraph, “. Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process. In this procedure, the batteries were drained at 16 different current levels and current profiles”).
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Obeid, Page 350, Algorithm 1.
Obeid teaches a F1-score for precision of the prediction, Obeid is silent on
finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries;
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k.
However, Tan teaches finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries (Tan, Figure 5, page 8723, abstract, “we select the task with the highest FES score to obtain the base model with superior generalization performance”. “A high feature expression scoring (FES)” reads on “optimal value of a weighting factor k”)
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k. (Tan, Figure 5, see below, Block “training base model” with highest FES score” and transfer learning).
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Tan, Figure 5
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Obaid’s transfer learning method for predicting optimized model to incorporate Tan’s transfer learning machine learning method with the “A feature expression scoring (FES) as taught by Tan and obtain an accurate trained model and generate output result with optimal precision. (Tan, conclusion). It would have been obvious to a person of ordinary skill to include the well-known transfer learning machine learning model optimization along with the other machine learning network, in order to yield the predicted results of generating accurate battery performance prediction, yet with higher accuracy (KSR).
Regarding Claim 2, combination of Obeid and Tan teaches the system of claim 1,
Obeid further teaches wherein each of the first training data, the first measurement data, the second training data, and the second measurement data refer to one or more measurement values selected from a voltage measurement value (Obeid, Figure 1-2, Page 348, left col. Middle paragraph,” The battery's terminal
voltage patterns are monitored” also see equation 4, The term y(t) represents the battery terminal voltage”. One of the measurements is voltage value.) a current measurement value, an impedance measurement value, a temperature measurement value, a capacity measurement value, and a power measurement value that are measured in the charging, discharging, and resting processes of the plurality of the secondary batteries independently (Obeid, Page 353, Table 3, (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).”), Page 352, 4.2, Right col. Top paragraph, “. Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process. In this procedure, the batteries were drained at 16 different current levels and current profiles”).
Regarding Claim 3, combination of Obeid and Tan teaches the system of
claim 1,
Obeid further teaches wherein the machine learning independently apply one or more methods selected from decision tree, random Forest, neural network, deep neural network, support vector machine, and gradient boosting machine. (Obeid, Figure 6, Neural Network, page 348, left col. Bottom paragraph “a fully connected artificial neural network is used as a classifier”).
Regarding Claim 4, combination of Obeid and Tan teaches the system of
claim 1,
Obeid is silent on wherein the optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER).
However, Tan teaches wherein the optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER). (Tan, Table VII, Figure 7-8, page 8729, right col. Bottom paragraph, and Page 8730 left col. Top paragraph. “the RMSE (Root mean square error) of the transfer learning is significantly positive correlated with the FES score. It could be observed that for CS35, the FEScs35 = 18 is the highest and same as that of the B7, and the RMSEcs35 = 0.0052 is the lowest. The experimental results demonstrate the validity of the FES rule for CACLE datasets. Compared to other neural network methods (LSTM-FC, DNN, and GMDH), the LSTM-FC-TL achieves optimal stability with the lowest SDE (SD error”). Highest FES score and lowest RMSE or SDE reads on “optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER”)”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Obaid’s transfer learning method for predicting optimized model to incorporate Tan’s transfer learning machine learning method with the “A feature expression scoring (FES) as taught by Tan and obtain an accurate trained model and generate output result with optimal precision. (Tan, conclusion). It would have been obvious to a person of ordinary skill to include the well-known transfer learning machine learning model optimization along with the other machine learning network, in order to yield the predicted results of generating accurate battery performance prediction, yet with higher accuracy (KSR).
Regarding Claim 5, combination of Obeid and Tan teaches the system of
claim 1,
Obeid further teaches further configured to perform outputting the first low-voltage determination prediction result. (Obeid, Figure 6, Figure 7-9, page 351, left col. Bottom paragraph “the performance of the corresponding feature set is displayed in subfigures titled Dataset 1, 2, 3, respectively. The results presented are for a randomly selected test group of windows, with and without noise. The legend in Fig. 9 explains the pattern coding of the figure s, and shows the four different types of outcomes in the NN prediction”) in which the first measurement data is applied to the first low-voltage prediction model of the first group of the plurality of the secondary batteries (Obeid, Figure 6, Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 9-10, “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
Regarding Claim 6, combination of Obeid and Tan teaches the system of
claim 1,
Obeid further teaches, further configured to perform: verifying the second low voltage prediction model by comparing the second low voltage determination prediction result (Obeid, Figure 6 (See below), Page 350, Right Col. Algorithm 1, Step 11: Test model on simulated data, Step 12: Pre-process real data, 13: Test/use model on real data) in which the second measurement data is applied to the second low voltage prediction model generated based on the second training data and the second low voltage determination result based on the second measurement data. (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Steps 11-13). page 352, right col. Bottom paragraph, “To adapt our classification model to the real scenarios, we used transfer learning; i.e. after training the NN on the simulated data, we
trained it an additional time on battery number 1. Then we used the trained NN to test the seven batteries mentioned above”).
Regarding Claim 7, Obeid teaches,
Obeid further teaches, A method of predicting a low-voltage failure (Obeid, Figure 10, Page 350, Right col. Middle paragraph, “the supervised learning-based battery terminal voltage collapse detection methodology Algorithm 1”. Figure 6”) of a secondary battery, the method comprising:
Inputting, by the one or more computing devices, first training data (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 1: “Obtain the simulated training data”) of a first group of a plurality of secondary batteries (Obeid, Figure 6, Page 348, Right Col. top paragraph, “the training is conducted based on data simulated using a mathematical model for a 4 V, 850 mAh Li-ion battery” NOTE: “4 V, 850 mAh Li-ion battery” represents a first group of plurality of battery used to obtain training data. Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries reads on “secondary battery” (Obeid, Page347, left col. Top paragraph introduction:” Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries,” NOTE: It is well known in the art that for Electric Vehicle /(EV) “rechargeable batteries/ Lithium-ion batteries” are used as “secondary batteries”.) measured during a first specific time period of charging, discharging, and resting processes ((Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries, (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).”), wherein the first group of the plurality of the secondary batteries are selected as a first training targets (NOTE: Battery number 1 is first group training target trained by NN. see (Obeid, Page 352, Bottom paragraph, “we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1”)
Generating, by the one or more computing devices, a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data (Obeid, Figure 6, Page 350, Right Col. “Algorithm 1, Step 9-11: “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model)”; Page 348, left col. Bottom paragraph, “raw values, fust-order derivatives, and Fourier transform over sliding time windows of batteries ' terminal voltage values are used as features. Then, a fully connected artificial neural network is used as a classifier”).
Inputting, by the one or more computing devices, first measurement data of a second group of the plurality of the secondary batteries Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 3-5: Step 3: Capture segments of data with overlapping windows: obtain N windows, Step 4: for i= [l: N]do, and Step 5: dataset: - raw data”) selected during a second specific time period of charging, discharging, and resting processes (Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries,)
wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each batteries 2, 4,5,6,8,14 are individual “prediction target” see table 3) ;
comparing, by the one or more computing devices, a second low-voltage determination prediction result of the second group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 11-13:” 11: Test model on simulated data 12: Pre-process real data,13: Test/use model on real data”) in which the first measurement data is applied to a first low-voltage prediction model of the first group of the plurality of the secondary batteries generated using the first training (Algorithm 1, step 1-3) data with first low-voltage determination prediction result of the second group of the plurality of the secondary batteries based on the first measurement data (Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model) (Algorithm 1, Step 13) and
receiving second training data of a third group of the plurality of the secondary batteries (Algorithm 1 step 1-4, Algorithm 1: Supervised learning-based battery terminal voltage collapse detection methodology 1: Obtain the simulated training data by solving (l}--(4) 2: Label the data based on SOC level 3: Capture segments of data with overlapping windows: obtain N windows”. “Step 4 i=[l:N]do” of algorithm 1 reads on “ third group”. The algorithm can be implemented on any ith battery where i= [1, N] number of different batteries and the training data can be generated using any different third group of batteries under different charging, discharging conditions. This is an algorithm design choice) measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets; (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).” Each battery has different time).
Generating, by the one or more computing devices, a second low-voltage prediction model of the third group of the plurality of the secondary batteries by performing machine learning on the optimized low-voltage prediction model (Obeid, Figure 6) of the first group of the plurality of the secondary batteries and the second training data of the third group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
receiving second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes, (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 4-7: input raw data, step 4 i= [1, N] Page 350, left col. Bottom paragraph, “the raw voltage values after normalization are used as features” NOTE: . “Step 4 i=[l:N]do” of algorithm 1 reads on “ fourth group”. where N can vary with any nth no of batteries. each battery group has different time period, discharge charging cycle and ret process. See table 3, each battery has different condition and parameters),
wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). To adapt our classification model to the real scenarios, we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each battery 2, 4,5,6,8,14 are individual “prediction target”. Battery 4, could be 4th group and a second prediction target. see table 3); and
outputting, by the one or more computing devices, a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries (Obeid, Figure 6 (See below), Page 350, Right Col. Algorithm 1, Step 11: Test model on simulated data, Step 12: Pre-process real data, 13: Test/use model on real data), in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target (Obeid, Page 350, left col. Middle paragraph, “each battery may take a different route to battery failure. The overlapping windows that document the different routes to failure are then labelled as either coming from the safe regions or the failure regions, depending on the SOC information as described earlier. The extracted windows are then split into two groups: a training set (TR) and a testing set (TE). The TR constitutes 70% of all windows. We ensure that windows from the same battery/load combination do not appear simultaneously in training or testing sets. We extract data both from simulated battery and load models, and also from tests on real batteries. Table I explains the different constructed datasets” NOTE: each battery process condition is different. for example, see (Obeid, Page 353, Table 3, Page 352, 4.2, Right col. Top paragraph, “. Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process. In this procedure, the batteries were drained at 16 different current levels and current profiles”)
Obeid teaches a F1-score for precision of the prediction, Obeid is silent on
finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model;
transferring, by the one or more computing devices, the optimized first low voltage prediction model and the optimal value of the weighting factor k;
However, Tan teaches finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries (Tan, Figure 5, page 8723, abstract, “we select the task with the highest FES score to obtain the base model with superior generalization performance”. “A high feature expression scoring (FES)” reads on “optimal value of a weighting factor k”)
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k. (Tan, Figure 5, see below, Block “training base model” with highest FES score” and transfer learning).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Obaid’s transfer learning method for predicting optimized model to incorporate Tan’s transfer learning machine learning method with the “A feature expression scoring (FES) as taught by Tan and obtain an accurate trained model and generate output result with optimal precision. (Tan, conclusion). It would have been obvious to a person of ordinary skill to include the well-known transfer learning machine learning model optimization along with the other machine learning network, in order to yield the predicted results of generating accurate battery performance prediction, yet with higher accuracy (KSR).
Regarding Claim 8, combination of Obeid and Tan teaches the method of claim 7,
Obeid further teaches wherein each of the first training data, the first measurement data, the second training data, and the second measurement data refer to one or more measurement values selected from a voltage measurement value (Obeid, Figure 1-2, Page 348, left col. Middle paragraph,” The battery's terminal
voltage patterns are monitored” also see equation 4, The term y(t) represents the battery terminal voltage”. One of the measurements is voltage value.) a current measurement value, an impedance measurement value, a temperature measurement value, a capacity measurement value, and a power measurement value that are measured in the charging, discharging, and resting processes of the plurality of the secondary batteries independently (Obeid, Page 353, Table 3, (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).”), Page 352, 4.2, Right col. Top paragraph, “. Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process. In this procedure, the batteries were drained at 16 different current levels and current profiles”).
Regarding Claim 9, combination of Obeid and Tan teaches the method of claim 7,
Obeid further teaches wherein the machine learning independently apply one or more methods selected from decision tree, random Forest, neural network, deep neural network, support vector machine, and gradient boosting machine. (Obeid, Figure 6, Neural Network, page 348, left col. Bottom paragraph “a fully connected artificial neural network is used as a classifier”).
Regarding Claim 10, combination of Obeid and Tan teaches the method of claim 7,
Obeid is silent on wherein the optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER).
However, Tan teaches wherein the optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER). (Tan, Table VII, Figure 7-8, page 8729, right col. Bottom paragraph, and Page 8730 left col. Top paragraph. “the RMSE (Root mean square error) of the transfer learning is significantly positive correlated with the FES score. It could be observed that for CS35, the FEScs35 = 18 is the highest and same as that of the B7, and the RMSEcs35 = 0.0052 is the lowest. The experimental results demonstrate the validity of the FES rule for CACLE datasets. Compared to other neural network methods (LSTM-FC, DNN, and GMDH), the LSTM-FC-TL achieves optimal stability with the lowest SDE (SD error”). Highest FES score and lowest RMSE or SDE reads on “optimal value of the weighting factor k means a value that minimizes a Misclassification Error Rate (MER”)”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Obaid’s transfer learning method for predicting optimized model to incorporate Tan’s transfer learning machine learning method with the “A feature expression scoring (FES) as taught by Tan and obtain an accurate trained model and generate output result with optimal precision. (Tan, conclusion). It would have been obvious to a person of ordinary skill to include the well-known transfer learning machine learning model optimization along with the other machine learning network, in order to yield the predicted results of generating accurate battery performance prediction, yet with higher accuracy (KSR).
Regarding Claim 11, combination of Obeid and Tan teaches the method of claim 7,
Obeid further teaches further configured to perform outputting the first low-voltage determination prediction result. (Obeid, Figure 6, Figure 7-9, decision, page 351, left col. Bottom paragraph “the performance of the corresponding feature set is displayed in subfigures titled Dataset 1, 2, 3, respectively. The results presented are for a randomly selected test group of windows, with and without noise. The legend in Fig. 9 explains the pattern coding of the figure s, and shows the four different types of outcomes in the NN prediction”) in which the first measurement data is applied to the first low-voltage prediction model of the first group of the plurality of the secondary batteries (Obeid, Figure 6, Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 9-10, “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
Regarding Claim 12, combination of Obeid and Tan teaches the method of claim 7,
Obeid further teaches, further configured to perform: verifying the second low voltage prediction model by comparing the second low voltage determination prediction result (Obeid, Figure 6 (See below), Page 350, Right Col. Algorithm 1, Step 11: Test model on simulated data, Step 12: Pre-process real data, 13: Test/use model on real data) in which the second measurement data is applied to the second low voltage prediction model generated based on the second training data and the second low voltage determination result based on the second measurement data. (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Steps 11-13). page 352, right col. Bottom paragraph, “To adapt our classification model to the real scenarios, we used transfer learning; i.e. after training the NN on the simulated data, we
trained it an additional time on battery number 1. Then we used the trained NN to test the seven batteries mentioned above”).
Regarding Claim 13, combination of Obeid and Tan teaches the apparatus of claim 1,
Obeid further teaches A Battery Management System (BMS) apparatus including the apparatus for predicting the low-voltage failure of the secondary battery of claims 1 (Obeid, Page 352, left column, “actual runtime operation of the proposed algorithm may happen on a BMS”).
Regarding Claim 14, combination of Obeid and Tan teaches the system of claim 13,
Obeid further teaches wherein the BMS apparatus is remotely controlled. (Obeid, page 352, left col. Bottom paragraph “While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer. It is also worth noting that there are several powerful single board computers /microcontrollers available now, which can be suited for such operations”. NOTE: any computer wired or remote BMS system can be used”).
Regarding Claim 15, combination of Obeid and Tan teaches the system of claim 14,
Obeid further teaches a mobile device including the BMS (Obeid, page 352, left col. Bottom paragraph “While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer. It is also worth noting that there are several powerful single board computers /microcontrollers available now, which can be suited for such operations”. NOTE: any computer or a mobile device can perform as a BMS system. It is a design choice).
Regarding Claim 16, combination of Obeid and Tan teaches the system of claim 1,
Obeid further teaches a wherein the BMS apparatus is embedded in the mobile device. (Obeid, page 352, left col. Bottom paragraph “While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer. It is also worth noting that there are several powerful single board computers /microcontrollers available now, which can be suited for such operations”. NOTE: any several powerful single board computers /microcontrollers available can be embedded into a mobile device and perform as a BMS system. It is a design choice).
Regarding Claim 17, Obeid teaches,
A non-transitory machine-readable medium comprising machine-readable instructions encoded thereon for performing (Obeid, page 352, left col. Bottom paragraph “While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer) a method of predicting the low-voltage failure of the second battery(Obeid, Figure 10, Page 350, Right col. Middle paragraph, “the supervised learning-based battery terminal voltage collapse detection methodology Algorithm 1”. Figure 6”), the method comprising:
inputting first training data (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 1: “Obtain the simulated training data”) of a first group of a plurality of secondary batteries (Obeid, Figure 6, Page 348, Right Col. top paragraph, “the training is conducted based on data simulated using a mathematical model for a 4 V, 850 mAh Li-ion battery” NOTE: “4 V, 850 mAh Li-ion battery” represents a first group of plurality of battery used to obtain training data. Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries reads on “secondary battery” (Obeid, Page347, left col. Top paragraph introduction:” Rechargeable batteries, particularly the lithium-ion (Li-ion) batteries,” NOTE: It is well known in the art that for Electric Vehicle /(EV) “rechargeable batteries/ Lithium-ion batteries” are used as “secondary batteries”.) measured during a first specific time period of charging, discharging, and resting processes ((Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries, (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).”), wherein the first group of the plurality of the secondary batteries are selected as a first training targets (NOTE: Battery number 1 is first group training target trained by NN. see (Obeid, Page 352, Bottom paragraph, “we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1”)
generating a first low-voltage prediction model of the first group of the plurality of the secondary batteries by performing machine learning on the first training data and selecting a main factor among the first training data (Obeid, Figure 6, Page 350, Right Col. “Algorithm 1, Step 9-11: “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model)”; Page 348, left col. Bottom paragraph, “raw values, fust-order derivatives, and Fourier transform over sliding time windows of batteries ' terminal voltage values are used as features. Then, a fully connected artificial neural network is used as a classifier”);
inputting first measurement data of a second group of the plurality of the secondary batteries Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 3-5: Step 3: Capture segments of data with overlapping windows: obtain N windows, Step 4: for i= [l: N]do, and Step 5: dataset: - raw data”) selected during a second specific time period of charging, discharging, and resting processes (Obeid, page 353, Table 3, “sampling period (SPS)” reads on “time period of charging, discharging”. Table 3 discloses different time for different batteries,)
wherein the second group of the plurality of the secondary batteries are selected as a first prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each battery 2, 4,5,6,8,14 are individual “prediction target” see table 3);
comparing a second low-voltage determination prediction result of the second group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 11-13:” 11: Test model on simulated data 12: Pre-process real data,13: Test/use model on real data”) in which the first measurement data is applied to a first low-voltage prediction model of the first group of the plurality of the secondary batteries generated using the first training (Algorithm 1, step 1-3) data with first low-voltage determination prediction result of the second group of the plurality of the secondary batteries based on the first measurement data (Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model) (Algorithm 1, Step 13) and
inputting second training data of a third group of the plurality of the secondary batteries measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as second training targets (Algorithm 1 step 1-4, Algorithm 1: Supervised learning-based battery terminal voltage collapse detection methodology 1: Obtain the simulated training data by solving (l}--(4) 2: Label the data based on SOC level 3: Capture segments of data with overlapping windows: obtain N windows”. “Step 4 i=[l:N]do” of algorithm 1 reads on “ third group”. The algorithm can be implemented on any ith battery where i= [1, N] number of different batteries and the training data can be generated using any different third group of batteries under different charging, discharging conditions. This is an algorithm design choice) measured during a third specific time period of charging, discharging, and resting processes, wherein the third group of the plurality of the secondary batteries are selected as a second training targets; (Obeid, Page 352, Right col. Bottom paragraph, “Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14).” Each battery has different time).
generating a second low-voltage prediction model Obeid, Figure 6) of the third group of the plurality of the secondary batteries by performing machine learning on the transferred optimized low-voltage prediction model (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E { 1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
inputting second measurement data of a fourth group of the plurality of the secondary batteries selected during a fourth specific period of charging, discharging, and resting processes Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Step 4-7: input raw data, step 4 i= [1, N] Page 350, left col. Bottom paragraph, “the raw voltage values after normalization are used as features” NOTE: “Step 4 i= [l: N]do” of algorithm 1 reads on “fourth group”. where N can vary with any nth no of batteries. each battery group has different time period, discharge charging cycle and ret process. See table 3, each battery has different condition and parameters),
wherein the fourth group of the plurality of the secondary batteries are selected as a second prediction targets (Obeid, Page 352, 4.2, Right col. bottom paragraph, Table 3 gives a comprehensive summary of the performance of the NN with seven batteries (battery numbers. 2, 4, 5, 6, 8, 9, 14). To adapt our classification model to the real scenarios, we used transfer learning; i.e. after training the NN on the simulated data, we trained it an additional time on battery number 1 (training target). Then we used the trained NN to test the seven batteries mentioned above”. NOTE: each battery 2, 4,5,6,8,14 are individual “prediction target”. Battery 4, could be 4th group and a second prediction target. see table 3);
outputting a second low-voltage determination prediction result of the third group of the plurality of the secondary batteries (Obeid, Figure 6 (See below), Page 350, Right Col. Algorithm 1, Step 11: Test model on simulated data, Step 12: Pre-process real data, 13: Test/use model on real data), in which the second measurement data is applied to the second low-voltage prediction model of the third group of the plurality of the secondary batteries (Obeid, Figure 6, Page 350, Right Col. Algorithm 1, Algorithm 1, Step 9-10, “9: Split each dataset 1, j E {1, 2, 3} into TR (training set) and TE (testing set) 10: Train model);
wherein at least one of charging, discharging, and resting process conditions of the first and second group of the plurality of the secondary batteries selected as the first training target and the first prediction target are different from at least one of charging, discharging, and resting process conditions of the third and fourth group of the plurality of the secondary batteries selected as the second training target and the second prediction target.(Obeid, Page 350, left col. Middle paragraph, “each battery may take a different route to battery failure. The overlapping windows that document the different routes to failure are then labelled as either coming from the safe regions or the failure regions, depending on the SOC information as described earlier. The extracted windows are then split into two groups: a training set (TR) and a testing set (TE). The TR constitutes 70% of all windows. We ensure that windows from the same battery/load combination do not appear simultaneously in training or testing sets. We extract data both from simulated battery and load models, and also from tests on real batteries. Table I explains the different constructed datasets” NOTE: each battery process condition is different. for example, see (Obeid, Page 353, Table 3, Page 352, 4.2, Right col. Top paragraph, “. Sixteen different batteries were discharged through varying loads, and their terminal voltage was observed throughout the process. In this procedure, the batteries were drained at 16 different current levels and current profiles”)
Obeid teaches a F1-score for precision of the prediction, Obeid is silent on
finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model to verify and optimize the first low-voltage prediction model;
transferring the optimized first low voltage prediction model and the optimal value of the weighting factor k;
However, Tan teaches finding an optimal value of a weighting factor k that maximizes a performance of the first low-voltage prediction model of the first group of the plurality of the secondary batteries to verify and optimize the low-voltage prediction model of the first group of the plurality of the secondary batteries (Tan, Figure 5, page 8723, abstract, “we select the task with the highest FES score to obtain the base model with superior generalization performance”. “A high feature expression scoring (FES)” reads on “optimal value of a weighting factor k”)
receiving the optimized low voltage prediction model of the first group of the plurality of the secondary batteries and the optimal value of the weighting factor k. (Tan, Figure 5, see below, Block “training base model” with highest FES score” and transfer learning).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Obaid’s transfer learning method for predicting optimized model to incorporate Tan’s transfer learning machine learning method with the “A feature expression scoring (FES) as taught by Tan and obtain an accurate trained model and generate output result with optimal precision. (Tan, conclusion). It would have been obvious to a person of ordinary skill to include the well-known transfer learning machine learning model optimization along with the other machine learning network, in order to yield the predicted results of generating accurate battery performance prediction, yet with higher accuracy (KSR).
Regarding Claim 18, combination of Obeid and Tan teaches the system of claim 1,
Obeid further teaches A server including the apparatus for predicting the low-voltage failure of the secondary battery of claim 1. (Obeid, page 352, left col. Bottom paragraph “While actual runtime operation of the proposed algorithm may happen on a BMS, training can be done on any computer”. NOTE: A remote computer or server can be used).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
TAKAHASHI; Kenji (US 20190178952 A1) recites “If a charging quantity charged to a battery pack since switching from discharging to charging is more than or equal to a reference charging quantity, an ECU estimates the SOC from the OCV by referring to a charging OCV. If a discharging quantity discharged from the battery pack since switching from charging to discharging is more than or equal to a reference discharging quantity, the ECU estimates the SOC from the OCV by referring to a discharging OCV. If the electric quantity is less than the reference charging quantity or if the electric quantity is more than the reference discharging quantity, the ECU estimates the SOC from the OCV using a straight line for supplementing SOC-OCV characteristics in a region enclosed by the charging OCV and the discharging OCV” (abstract).
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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/DILARA SULTANA/Examiner, Art Unit 2858
07/16/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 7/22/2026
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