DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 11/12/2025 has been entered. Claim(s) 1, 3-19 is/are now pending in the application. Applicant's amendments have addressed all informalities as previously set forth in the non-final action mailed on 08/12/2025.
Claim Rejections - 35 USC § 101
35 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.
Claim(s) 1, 3-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1 recites
building a battery state of health (SOH) estimation model,
applying, by an optimization server, an error of a model voltage, an error of a measured voltage, a capacity, or a resistance of a battery mounted in a vehicle as an objective function;
and
performing error minimization processing to derive optimal parameters of the battery,
wherein
the model voltage is
an output from test data of a battery pack,
which is
built using a Newman, Tiedmann, Gu, and Kim (NTGK) model
by
an NTGK server,
wherein
the measured voltage is
an output from actual vehicle driving/charging battery data of the battery mounted in the vehicle by a vehicle customer relation management (VCRM) server,
and
wherein
the error minimization processing is performed using
a gradient descent algorithm of machine learning by the optimization server;
optimizing, by the optimization server, the NTGK model by:
setting an initial condition for reducing the error;
determining an Nth-order objective function by applving the capacity and the resistance as optimization elements;
and
processing the objective function using the optimization elements by the gradient descent algorithm,
wherein
determining the Nth-order objective function comprises:
setting a calculation result of the objective function as a first-order calculation value of the objective function,
normalizing a slope of the objective function using the gradient descent algorithm,
and
applvng the slope of the objective function to a tolerance value
and
deriving a shift direction of the optimization elements
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
Independent Claim(s) 15 recites
build an NTGK voltage model from test data of a battery pack
and
output a model voltage from the NTGK voltage model;
analyze actual vehicle driving/charging battery data of a battery mounted in a vehicle
and
output a measured voltage;
and
determine an error between the model voltage and the measured voltage,
apply a capacity and a resistance of the battery, as optimization elements, to an objective function,
derive optimal parameters for the capacity and the resistance
in a process of
minimizing the error using a gradient descent algorithm of machine learning for the objective function,
and
optimize the NTGK voltage model based on the optimal parameters,
optimize the NTGK model by:
setting an initial condition for reducing the error;
determining an Nth-order objective function by applying the capacity and the resistance as the optimization elements;
and
processing the objective function using the optimization elements by the gradient descent algorithm,
wherein
the Nth-order objective function is determine by:
setting a calculation result of the objective function as a first-order calculation value of the objective function,
normalizing a slope of the objective function using the gradient descent algorithm,
and
applving the slope of the objective function to a tolerance value and deriving a shift direction of the optimization elements
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim(s) 1, 15, Claim(s) 3-14, 16-19 recite(s)
building the NTGK model by the NTGK server comprises:
collecting the test data in an initial capacity state in which the battery pack is not charged and discharged;
extracting parameters related to a temperature and the resistance from the test data as model fixed parameters; and
performing NTGK processing on the model fixed parameters and
building the NTGK model in which the model voltage is an output value.
the test data comprises
a current, a voltage, and the temperature according to a temperature value and a current value.
extracting battery big data of the battery from the VCRM server,
extracting the battery big data comprises:
synthesizing, by the VCRM server, the actual vehicle driving/charging battery data of the battery transmitted from a VCRM terminal mounted in the vehicle;
performing data pre-processing using the actual vehicle driving/charging battery data as the battery big data through artificial intelligence; and
extracting actual vehicle parameters having the measured voltage as an output value from the data pre-processing as a current, a temperature, and a voltage of the battery.
performing the data pre-processing comprises:
applying a data unit which uses start-up on/off periods of the vehicle as one data set;
using a data set of a rest period after a start-up off period of a previous data set before a start-up on period;
using actual measurement information on the current, and the temperature, and the voltage;
applying two or more of determined initial state of charge (SOC) values of the battery based on the measured voltage as a setting condition; and
analyzing and classifying the actual vehicle driving/charging battery data using the setting condition.
the rest period is set to one hour after the start-up off period.
determining whether a tolerance value is satisfied;
in response to a determination that the tolerance value is not satisfied,
performing calculating the Nth-order objective function again,
repeating the optimizing, and finding the optimal parameters for the optimization elements;
and
in response to a determination that the tolerance value is satisfied,
deriving the optimization elements as the optimal parameters.
calculating the Nth-order objective function further comprises:
setting the calculation result of the objective function as an Nth-order calculation value of the objective function in the shift direction of the optimization elements.
repeating the optimizing comprises:
comparing the Nth-order calculation value of the objective function with the first-order calculation value of the objective function;
in response to the Nth-order calculation value of the objective function being greater than the first-order calculation value of the objective function,
reducing a step size for changing the tolerance value; and
in response to the Nth-order calculation value of the objective function being smaller than the first-order calculation value of the objective function,
increasing a number of repetitions of a total type step.
reducing the step size comprises
setting to 1/2 compared to a previous step size.
after reducing the step size,
returning to deriving the shift direction of the optimization elements during calculating the Nth-order objective function by
applying the slope of the objective function to the tolerance value.
increasing the number of repetitions comprises returning to normalizing the slope of the objective function using the gradient descent algorithm during calculating the Nth-order objective function.
the NTGK model is built as an NTGK voltage model by
applying the optimal parameters; and
the NTGK voltage model
outputs the SOH of the battery as “SOH = xxx%.”
the NTGK server is configured to
use an NTGK model to which the capacity and the resistance of the battery are applied as parameters.
provide the actual vehicle driving/charging battery data to the VCRM server.
the NTGK voltage model is configured to
be applied to an SOH estimation terminal; and
the SOH estimation terminal is mounted in the vehicle and is configured to
output an SOH of the battery.
the SOH estimation terminal is connected to a display; and
the display is configured to
display the SOH as “SOH = xxx%.”
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. an optimization server; a Newman, Tiedmann, Gu, and Kim (NTGK) server; a vehicle customer relation management (VCRM) server configured to; further comprising a VCRM terminal in the vehicle, wherein the VCRM terminal is configured to; an SOH estimation terminal, the SOH estimation terminal is mounted in the vehicle and is configured to; the SOH estimation terminal is connected to a display; and the display is configured to);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output/storage/display); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. of a battery mounted in a vehicle).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure; a battery mounted in a vehicle)).
Allowable Subject Matter (over Prior Art)
See prior OA, mailed 08/12/2025, for the statement of reasons for the indication of allowable subject matter over prior art.
The following is a statement of reasons for the indication of allowable subject matter over prior art:
None of the cited prior art alone or in combination provides motivation to explicitly teach:
building a battery state of health (SOH) estimation model,
applying, by an optimization server, an error of a model voltage, an error of a measured voltage, a capacity, or a resistance of a battery mounted in a vehicle as an objective function;
and
performing error minimization processing to derive optimal parameters of the battery,
wherein
the model voltage is
an output from test data of a battery pack,
which is
built using a Newman, Tiedmann, Gu, and Kim (NTGK) model
by
an NTGK server,
wherein
the measured voltage is
an output from actual vehicle driving/charging battery data of the battery mounted in the vehicle by a vehicle customer relation management (VCRM) server,
and
wherein
the error minimization processing is performed using
a gradient descent algorithm of machine learning by the optimization server;
optimizing, by the optimization server, the NTGK model by:
setting an initial condition for reducing the error;
determining an Nth-order objective function by applving the capacity and the resistance as optimization elements;
and
processing the objective function using the optimization elements by the gradient descent algorithm,
wherein
determining the Nth-order objective function comprises:
setting a calculation result of the objective function as a first-order calculation value of the objective function,
normalizing a slope of the objective function using the gradient descent algorithm,
and
applvng the slope of the objective function to a tolerance value
and
deriving a shift direction of the optimization elements
of claim(s) 1;
a Newman, Tiedmann, Gu, and Kim (NTGK) server
configured to
build an NTGK voltage model from test data of a battery pack
and
output a model voltage from the NTGK voltage model;
a vehicle customer relation management (VCRM) server
configured to
analyze actual vehicle driving/charging battery data of a battery mounted in a vehicle
and
output a measured voltage;
and
an optimization server
configured to
determine an error between the model voltage and the measured voltage,
apply a capacity and a resistance of the battery, as optimization elements, to an objective function,
derive optimal parameters for the capacity and the resistance
in a process of
minimizing the error using a gradient descent algorithm of machine learning for the objective function,
and
optimize the NTGK voltage model based on the optimal parameters,
wherein
the optimization server is further configured to
optimize the NTGK model by:
setting an initial condition for reducing the error;
determining an Nth-order objective function by applying the capacity and the resistance as the optimization elements;
and
processing the objective function using the optimization elements by the gradient descent algorithm,
wherein
the Nth-order objective function is determine by:
setting a calculation result of the objective function as a first-order calculation value of the objective function,
normalizing a slope of the objective function using the gradient descent algorithm,
and
applving the slope of the objective function to a tolerance value and deriving a shift direction of the optimization elements
of claim(s) 15.
Response to Arguments
Applicant’s amendments, filed on 11/12/2025, have been entered and fully considered. In light of the applicant’s amendments changing the scope of the claimed invention, the rejection(s) have been withdrawn or updated. However, upon further consideration, a new or updated ground(s) of rejection(s) have been made, and applicant's argument(s)/remark(s) pertaining to the amended language have been rendered moot.
Applicant's argument(s)/remark(s), see page(s) 8-11, filed 11/12/2025, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“Section 101 Rejections
Claims 1-19 stand rejected under 35 U.S.C. §101. The Office Action states that the claims recite mathematical concepts and/or mental processes. The Office Action further states that the abstract idea is not integrated into a practical application. See Office Action, pages 2-6. The Office Action finally notes that claim 10 (and claims 11-13) include allowable subject matter indicating that at least these features either do not recite a mathematical concept and/or a mental process or integrate the abstract idea into a practical application. See Office Action, pages 1 and 18.
Applicants submit that the following claim is not directed to a judicial exception:
…
Applicants submit that features of the recited claim at least integrate the claim into a practical application. Applicants would like to direct the attention of the Examiner to a recently issued Memorandum from the Deputy Commissioner for Patent, dated August 4, 2025, and titled 'Reminder on evaluating subject matter eligibility of claims under 35 U.S.C. 101." Under "B. Step 2A Prong Two' it states that "... The analysis in Step 2A Prong Two considers the claim as a whole. The way in which the additional elements use or interact with the exception may integrate the judicial exception into a practical application. Accordingly, the additional limitations should not be evaluated in a vacuum, completely separate from the recited judicial exception. Instead, the analysis should take into consideration all the claim limitations and how these limitations interact and impact each other when evaluating whether the exception is integrated into a practical application."
The Memorandum further specifies: “Improvements consideration: In computer-related Examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception. This consideration has also been referred to as the search for a technological solution to a technological problem. An important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as to merely claiming the idea of a solution or outcome. The Examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement.”
The Memorandum finally notes –“Apply it "consideration and overlap with improvements consideration: Another consideration when determining whether a claim integrates a judicial exception into a practical in Step 2A Prong Two is whether the additional elements amount to more than a recitation of the words "apply it"(or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer. Examiners are cautioned not to oversimplify claim limitations and expand the application of the "apply it" consideration. Moreover, Examiners are reminded that the "apply it" consideration often overlaps with the improvement’s consideration. When evaluating these two considerations, examiners may consider the following:
1. Whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome.
2. Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.
3. The particularity or generality of the application of the judicial exception.
For example, the Examiner should consider whether the technological limitations are being used as a tool to improve the recited judicial exception (e.g., automating a manual business process) or whether the claim as a whole provides an improvement to technology or a technical field. Claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception itself.
While this memorandum focuses on the "apply it" and improvements considerations, Examiners are reminded that there are other judicial considerations to evaluate whether additional elements in the claim integrate a judicial exception into a practical application..."
Accordingly, Applicants submit that the recited claim clearly improves the electric vehicle (EV) battery management technology. i) The claim provides a solution to the problem of monitoring the state of health (SOH) of a battery by providing a more accurate real-time SOH estimation and a better battery degradation monitoring, which eventually helps to improve the life of the battery. 2) The claim does not invoke a tool to perform an existing process but rather to improve the capabilities of the battery control system. And 3) The claim is particularly direct to batteries and not to a broad variety of technologies.
Therefore, the claim recites patentable subject matter.
The above consideration apply to all other claims.
Hence, Applicants respectfully request withdrawal of the rejections.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to mathematically estimate parameters corresponding to generic batteries.
When examining step 2A Prong 1, Examiner determines if there is an abstract idea present. One skilled in the art can at least perform the identified abstract idea utilizing Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation. The arguments, in light of the specification, fail to convince the Examiner that utilizing Mathematical Concepts and/or Mental Processes does not fit within the scope of the identified abstract limitations.
When examining step 2A Prong 2, Examiner examines the additional elements to determine if the identified abstract idea has been practically applied in a particular way in a particular technology. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). The additional elements, when viewed individually and in combination with the identified abstract idea, do not add anything beyond mere instructions to implement an abstract idea on a computer, adding generic ‘apply it’ language, and generically linking the identified abstract idea to a technological environment or field of use.
When examining step 2B, Examiner examines the additional elements to determine if they amount to significantly more than the abstract idea. The only additional element(s) is/are the generic computer structure being used as a tool to perform the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
It is important to note, the judicial exception alone cannot provide the improvement. An improved abstract idea is still an abstract idea.
Applicant's argument(s)/remark(s), see page(s) 11-13, filed 11/12/2025, with respect to the art rejection(s) has/have been fully considered.
-Applicant states
“Prior Art Rejections
Claim 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shoa, US 2023/0122362 (hereinafter "Shoa"). Claims 2-9 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Shoa in view of Ha, US 2023/0258727 (hereinafter "Ha"). Applicants respectfully traverse the rejections based on the amendments to the claims.
Claim 1, as amended, specifically recites:
…
Shoa and Ha do not teach or suggest such a server. In particular, Shoa does not show 1) the determination of an Nth-order objective function with capacity and resistance as optimization elements and 2) the specific claimed determination of the Nth-order objective function. Ha has not been cited with respect to these features.
In contrast, Shoa describes a method for compensating voltage drift in electrochemical battery testing using optimization algorithms. See Shoa, paragraphs [0538]-[0560].
The system of Shoa measures voltage signals from batteries during testing and identifies unwanted voltage drift that occurs due to continuous discharge. To remove this drift, Shoa uses a genetic algorithm that creates multiple candidate compensation curves with adjustable parameters. The genetic algorithm works by making copies of candidate curves, randomly adjusting their tuning parameters, and then evaluating how well each curve removes the voltage drift when subtracted from the measured signal.
The system uses an objective function that calculates the squared error between the actual discharge signal and an idealized sine wave, aiming to minimize this error. Through iterative processing, the algorithm selects the best-performing parameters and continues optimizing until it finds the curve that most effectively compensates for the voltage drift. The optimization process involves transforming signals into the frequency domain, identifying drift peaks, and systematically adjusting curve parameters to minimize these peaks. The system can use various optimization techniques including particle swarm, simulated annealing, gradient descent, and stochastic hill climbing as alternatives to the genetic algorithm.
However, Shoa does not show that the Nth-order objective function is determined with capacity and resistance as optimization elements and the Nth-order objective function is determined by setting a calculation result of the objective function as a first-order calculation value of the objective function, normalizing a slope of the objective function using the gradient descent algorithm, and applying the slope of the objective function to a tolerance value and deriving a shift direction of the optimization elements.
Hence, independent claim 1 is allowable.
Independent claim 15 includes similar claim elements as independent claim 1 and is therefore allowable for similar reasons.
The remaining claims depend from and add further features to one of the independent claims. It is respectfully submitted that these dependent claims are allowable by reason of depending from an allowable claim, as well as for adding new features, and that it is not necessary to separately address these dependent claims. This should not be construed as an acquiescence to Examiner's interpretation of those claims, or reading of those claim elements on the prior art, and Applicants expressly reserve the right to establish the patentability of those additional claim limitations, should the need arise.”.
Examiner agrees with the underlined argument(s)/remark(s).
Said rejection(s) has/have been withdrawn.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit