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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/26/2026 has been entered.
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) 1-10, 12-17 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, 14 recites
identify at least one parameter corresponding to at least one of a physical composition and a chemical composition of a first plurality of used batteries during at least one of a charging of the first plurality of used batteries and a discharging of the first plurality of used batteries;
determine a pattern of variations in at least one of a voltage, a current, a temperature, and a resistance during every cycle of the charging of the first plurality of used batteries and every cycle of the discharging of the first plurality of used batteries until an occurrence of a failure for the first plurality of used batteries;
generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition of the first plurality of used batteries and the chemical composition of the first plurality of used batteries;
obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model;
and
evaluate a RUL of the candidate battery using the mathematical model based on the predicted parameter
[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) 10 recites
determine a charging of a battery and a discharging of the battery for a predetermined number of cycles;
measure at least one of voltage, current, a temperature, and a resistance of the battery during the charging of the battery and the discharging of the battery;
train an Artificial intelligence (Al) model
to
estimate battery parameters based on correlation between a pattern of measured voltage, current, and resistance indicative of an occurrence of failure and the at least one of the voltage, the current, the temperature, and the resistance of the battery;
modify the battery model based on the estimated battery parameters;
and
obtain at least one of a physical indicator and a chemical indicator representing a remaining useful life (RUL) of the battery using the modified battery model
[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) 16 recites
determine at least one physical parameter corresponding to at least one of a physical composition of a battery and a chemical composition of the batter during a predetermined number of cycles corresponding to at least one of a charging and a discharging of the battery,
determine a pattern of variations in at least one of a voltage, a current, a temperature, and a resistance of the battery during the predetermined number of cycles;
train an artificial intelligence (Al) model which based on a correlation between the determined pattern of variations and the at least one physical parameter;
obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model;
and
evaluate a remaining useful life (RUL) of the battery using the mathematical model based on the updated predicted parameter
[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, 10, 14, 16, Claim(s) 3-9, 12-13, 17 recite(s)
store the generated Al model in the memory.
identify at least one of a physical composition of a candidate battery and a chemical composition of the candidate battery, and
identify a candidate pattern of variations in at least one of a voltage, a current, a temperature, and a resistance during every cycle of a charging of the candidate battery and a discharging of the candidate battery;
provide the at least one of the physical composition of the candidate battery and the chemical composition of the candidate battery, and the identified candidate pattern of variations to the Al model.
provide the at least one of
the physical composition of the candidate battery and the chemical composition of the candidate battery, and the identified candidate pattern of variations with the Al model which is
trained based on the correlation of the determined pattern of variations and the at least one of the physical composition and the chemical composition of the first plurality of used batteries; and
predict the occurrence of failure of the candidate battery based on a result obtained from the Al model.
the predicting of the occurrence of failure of the candidate battery includes at least one of
determining the RUL of the candidate battery and predicting a cycle number at which a sudden death of the candidate battery will occur.
determine the RUL of the candidate battery based on one or more initial cycles without receiving sudden death data of the first plurality of used batteries, by identifying signs of a non-linear degradation corresponding to battery sudden death in addition to linear degradation in the one or more initial cycles to predict the battery sudden death in at a future time.
the at least one of the physical composition and the chemical composition of the first plurality of used batteries comprises
a resistance growth, a porosity decay rate, a pre-exponential constant defining a Lithium Plating (LiP) current flux, a capacity drop, and a pre-exponential constant defining a solid electrolyte interface current flux.
track the pattern of variations in the at least one of the voltage, the current and the resistance during at least one of a charging of each of the first plurality of used batteries and a discharging of each of the first plurality of used batteries.
track a pattern of variations in the at least one of the voltage, the current, the temperature, and the resistance during at least one of the charging of the battery and the discharging of the battery.
predict an occurrence of failure of a candidate battery used in at least one of an electric vehicle (EV) and a hybrid vehicle based on the Al model.
a correlation a measured pattern of variations and identified physical indicators and chemical indicators corresponding to the RUL of the battery.
determine a pattern of additional variations in the at least one of the voltage, the current, the temperature, and the resistance of the battery during at least one cycle after the predetermined number of cycles;
provide the pattern of additional variations to the Al model;
obtaining an updated predicted parameter for the mathematical model using the AI model;
and
evaluate an updated RUL of the battery using the mathematical model based on the updated predicted parameter.
[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. a memory; a processor; and a remaining useful life (RUL) prediction controller, coupled with the memory and the processor, and configured to:);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. measure at least one of voltage, current, a temperature, and a resistance of the battery during the charging of the battery and the discharging of the battery; store the generated Al model in the memory); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. batteries).
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 the additional elements (e.g., generic computer structure)).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-10, 12-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GORRACHATEGUI et al. (US 2021/0293890) (hereinafter “GORRACHATEGUI”).
With respect to Claim(s) 1, 14, GORRACHATEGUI teaches a battery diagnostic system for an RUL estimation of a battery and the BRI of:
a memory (See, e.g., Fig(s). 1);
a processor (See, e.g., Fig(s). 1); and
a remaining useful life (RUL) prediction controller (See, e.g., Fig(s). 1), coupled with the memory and the processor, and configured to:
identify at least one parameter corresponding to at least one of a physical composition and a chemical composition of a first plurality of used batteries during at least one of a charging of the first plurality of used batteries and a discharging of the first plurality of used batteries (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
determine a pattern of variations in at least one of a voltage, a current, a temperature, and a resistance during every cycle of the charging of the first plurality of used batteries and every cycle of the discharging of the first plurality of used batteries until an occurrence of a failure for the first plurality of used batteries (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition of the first plurality of used batteries and the chemical composition of the first plurality of used batteries (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15); and
obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
and
evaluate a RUL of the candidate battery using the mathematical model based on the predicted parameter (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 10, GORRACHATEGUI teaches a battery diagnostic system for an RUL estimation of a battery and the BRI of:
a memory (See, e.g., Fig(s). 1);
a processor (See, e.g., Fig(s). 1); and
a remaining useful life (RUL) prediction controller (See, e.g., Fig(s). 1), coupled with the memory and the processor, and configured to:
determine a charging of a battery and a discharging of the battery for a predetermined number of cycles (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
measure at least one of voltage, current, a temperature, and a resistance of the battery during the charging of the battery and the discharging of the battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
train an Artificial intelligence (Al) model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15)
to
estimate battery parameters based on correlation between a pattern of measured voltage, current, and resistance indicative of an occurrence of failure and the at least one of the voltage, the current, the temperature, and the resistance of the battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
modify the battery model based on the estimated battery parameters (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
and
obtain at least one of a physical indicator and a chemical indicator representing a remaining useful life (RUL) of the battery using the modified battery model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 16, GORRACHATEGUI teaches a battery diagnostic system for an RUL estimation of a battery and the BRI of:
a memory (See, e.g., Fig(s). 1), and
at least one processor (See, e.g., Fig(s). 1) configured to:
determine at least one physical parameter corresponding to at least one of a physical composition of a battery and a chemical composition of the batter during a predetermined number of cycles corresponding to at least one of a charging and a discharging of the battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15),
determine a pattern of variations in at least one of a voltage, a current, a temperature, and a resistance of the battery during the predetermined number of cycles (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
train an artificial intelligence (Al) model which based on a correlation between the determined pattern of variations and the at least one physical parameter (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15); and
obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
and
evaluate a remaining useful life (RUL) of the battery using the mathematical model based on the updated predicted parameter (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 2, 15, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the RUL prediction controller is further configured to:
store the generated Al model in the memory (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 3, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the RUL prediction controller is further configured to:
identify at least one of a physical composition of a candidate battery and a chemical composition of the candidate battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15), and
identify a candidate pattern of variations in at least one of a voltage, a current, a temperature, and a resistance during every cycle of a charging of the candidate battery and a discharging of the candidate battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15);
provide the at least one of the physical composition of the candidate battery and the chemical composition of the candidate battery, and the identified candidate pattern of variations to the Al model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 4, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
to perform the predicting, the at RUL prediction controller is further configured to:
provide the at least one of
the physical composition of the candidate battery and the chemical composition of the candidate battery, and the identified candidate pattern of variations with the Al model which is trained based on the correlation of the determined pattern of variations and the at least one of the physical composition and the chemical composition of the first plurality of used batteries (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15); and
predict the occurrence of failure of the candidate battery based on a result obtained from the Al model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 5, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the predicting of the occurrence of failure of the candidate battery includes at least one of
determining the RUL of the candidate battery and predicting a cycle number at which a sudden death of the candidate battery will occur (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 6, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the Al model is configured to:
determine the RUL of the candidate battery based on one or more initial cycles without receiving sudden death data of the first plurality of used batteries, by identifying signs of a non-linear degradation corresponding to battery sudden death in addition to linear degradation in the one or more initial cycles to predict the battery sudden death in at a future time (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 7, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the at least one of the physical composition and the chemical composition of the first plurality of used batteries comprises
a resistance growth, a porosity decay rate, a pre-exponential constant defining a Lithium Plating (LiP) current flux, a capacity drop, and a pre-exponential constant defining a solid electrolyte interface current flux (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 8, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the RUL prediction controller is further configured to
track the pattern of variations in the at least one of the voltage, the current and the resistance during at least one of a charging of each of the first plurality of used batteries and a discharging of each of the first plurality of used batteries (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 13, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the RUL prediction controller is further configured to
track a pattern of variations in the at least one of the voltage, the current, the temperature, and the resistance during at least one of the charging of the battery and the discharging of the battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 9, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the RUL prediction controller is further configured to
predict an occurrence of failure of a candidate battery used in at least one of an electric vehicle (EV) and a hybrid vehicle based on the Al model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 12, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the Al model comprises
a correlation a measured pattern of variations and identified physical indicators and chemical indicators corresponding to the RUL of the battery (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
With respect to Claim(s) 17, GORRACHATEGUI teaches the BRI of the parent claim(s).
GORRACHATEGUI further teaches the BRI of:
wherein
the at least one processor is further configured to:
determine a pattern of additional variations in the at least one of the voltage, the current, the temperature, and the resistance of the battery during at least one cycle after the predetermined number of cycles (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15);
provide the pattern of additional variations to the Al model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15);
obtaining an updated predicted parameter for the mathematical model using the AI model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15);
and
evaluate an updated RUL of the battery using the mathematical model based on the updated predicted parameter (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0065, 0067, 0067-0100; See also, e.g., Fig(s). 1-5, 7-15).
Response to Arguments
Applicant’s amendments, filed on 02/26/2026, 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) 10-13, filed 02/26/2026, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“Claim Rejections - 35 USC § 101
Claims 1-10 and 12-17 are rejected under §101 because the Examiner asserts that the claims are directed to an abstract idea without significantly more.
In particular on page 17 of the Office Action, the Examiner asserts:
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.... 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, and generically linking the identified abstract idea to a technological environment or field of use... It is important to note, the judicial exception alone cannot provide the improvement An improved abstract idea is still an abstract idea.
Applicant notes that the USPTO's Appeals Review Panel (ARP) has recently issued a precedential decision in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), a precedential decision in which provided some additional guidance on § 101 issues.
In Ex Parte Desjardins, the ARP reiterated that claims which directed to an improvement to other technology or technical field are patent eligible. See, e.g., Ex Parte Desjardins at 8, citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016) ("the Federal Circuit held that the eligibility determination should turn on whether 'the claims are directed to an improvement to computer functionality versus being directed to an abstract idea."'). Further, the ARP stated that the focus of the examination for patentability should be on §§ 102, 103, and 112 (and not on § 101). See, e.g., Ex Parte Desjardins at 9-10.
In addition, Applicant notes that, on December 5, 2025, the USPTO issued a memorandum titled "Advance notice of change to the MPEP in light of Ex Parte Desjardins", which describes additions which are being made to the MPEP regarding § 101. For example, the December 56 Memorandum indicates that MPEP §2106.05(a) is now revised to state that "'Examiners and panels should not evaluate claims at such a high level of generality' that potentially meaningful technical limitations are dismissed without adequate explanation", and to further state that "[w]hen evaluating a claim as a whole, examiners should not dismiss additional elements as mere 'generic computer components' without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system".
Applicant submits that the Examiner has interpreted claim 1 at a high level of generality, and has improperly dismissed elements as "mere instructions to implement an abstract idea on a computer". Applicant further submits that claim 1, when evaluated as a whole, represents an improvement to a particular technical field, in particular the field of battery RUL estimation and management, as discussed in greater detail below.
As discussed in Applicant's previous remarks, paragraph [0003] of the present Specification discloses that some techniques for evaluating a remaining useful life (RUL) of a battery may use a large amount of data, and may provide a prediction the RUL of the battery at an advanced stage of the battery which is too late for any prudent corrective action. Further, these predictions may rely on specialized battery features, which may result in inconveniencing a user of the battery. However, paragraph [0061] of the Specification discloses that at least one of an artificial intelligence (AI) model and a battery model according to embodiments may be trained based on correlation between variations in the voltage, the current, and the resistance and future battery failure causes, any may therefore be used for detecting early indicators of such failures of the battery in order to detect signs of sudden death from very few initial cycles even without having seen sudden death data. For example, the AI model may be used to estimate parameters of a mathematical model of a battery, and then the mathematical model may be used to predict the capacity/RUL at any cycle number, or predicting the cycle number corresponding to the sudden death of the battery. See, e.g., Specification, paragraphs [0076]-[0077]. Accordingly, Applicant submits that embodiments may be used to automatically generate an improved or fine-tuned mathematical model that may be used to more accurately predict RUL performance of a battery.
Therefore, Applicant respectfully submits that the Specification clearly articulates an improvement to the technologies of battery RUL estimation using artificial intelligence. Further, Applicant submits that this improvement is properly reflected in the claims, for example in the portions of the independent claims relating to the generating of the AI model, estimating the parameters of the mathematical model, and evaluating the RUL of the candidate battery using the mathematical model based on the estimated parameters. Accordingly, even assuming arguendo that any judicial exception is present in the claims, to which Applicant does not acquiesce, Applicant respectfully submits again that claim 1 relates to an improvement to a technical field, and is therefore patent eligible at least at Step 2A, Prong Two (See MPEP 2106.04(d)(I)).
Accordingly, Applicant submits that these rejections have been rendered moot and requests that they be withdrawn.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
It is important to note that the December 5th, 2025 memorandum, Advance notice of change to the MPEP in light of Ex Parte Desjardins, does not change the current 2-Prong 101 eligibility guidance. The memorandum reminds examiners that claimed inventions directed to improving the function of machine learning technology itself, therefore an improvement in computer technology, is patent eligible.
The memorandum concludes that Ex Parte Desjardins is directed towards ‘protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation’.
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to mathematically process generically acquired data, not improving how the machine learning model itself would function in operation.
The examined claims align with Example 47, claim 2, which an abstract idea was identified as being present and was found to be patent ineligible.
Further, Examiner maintains previous response:
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to mathematically process generically acquired data corresponding to used batteries to evaluate parameters corresponding to remaining useful life.
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 Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion). Further, 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, 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) 13-15, filed 02/26/2026, with respect to the art rejection(s) has/have been fully considered.
-Applicant states
“Claim Rejections - 35 USC § 102
Claims 1-10, 12-17 are rejected under §102(a)(1) as being anticipated by Gorrachategui (US 2021/0293890).
Applicant respectfully traverses these rejections and requests reconsideration.
Independent claim 1 recites (emphasis added):
…
Applicant respectfully submits that claim 1 is patentable because Gorrachategui fails to disclose or suggest each and every element of the claim. For example, Applicant respectfully submits that Gorrachategui fails to disclose "generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition of the first plurality of used batteries and the chemical composition of the first plurality of used batteries; obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model; and evaluate a RUL of the candidate battery using the mathematical model based on the predicted parameter," as claimed.
As discussed in Applicant's previous remarks, Gorrachategui discloses a battery diagnostic system which may be used to determine a remaining useful life (RUL) of a test battery. See Gorrachategui, paragraph [0042]. In a training stage, a training dataset may be used to train machine learning algorithms to classify the battery into short RUL and long RUL classes, and predict the RUL cycles for the battery. See Gorrachategui, paragraph [0042]. For example, paragraph [0054] discloses an example in which a neural network may include a RUL classifier that classifies the battery into a short RUL class or a long RUL class, and then the neural network may estimate the RUL of the battery based on the classification. In addition, paragraph [0072] discloses another example in which the classification is used to select either a short RUL expert 708 or a long RUL expert 710, and then the selected expert model is used to estimate the RUL of the battery.
However, to the extent that Gorrachategui discloses using machine learning algorithms while estimating an RUL of a test battery, Gorrachategui does not appear to disclose using the machine learning algorithms to generate a predicted parameter for a mathematical model associated with the battery, and then estimating the RUL using the mathematical model according to the predicted parameter. Instead, it appears that Gorrachategui directly estimates the RUL based on the output of the machine learning algorithms (e.g., the neural network 106 or the expert models 708 and 710).
Therefore, Gorrachategui fails to disclose or suggest at least "generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition of the first plurality of used batteries and the chemical composition of the first plurality of used batteries; obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model; and evaluate a RUL of the candidate battery using the mathematical model based on the predicted parameter," as claimed in claim 1.
Accordingly, Applicant respectfully submits that claim 1 is patentable as each and every element of the claim is not disclosed or suggested by the cited reference.
Regarding independent claims 10, 14, and 16, Applicant respectfully submits that claims 10, 14, and 16 are patentable for at least similar reasons as those provided above with reference to claim 1.
Regarding dependent claims 2-9, 12-13, 15, and 17, Applicant respectfully submits that these claims are patentable for at least the reasons set forth above due to their respective dependencies.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Claim 1 states:
“…
generate an artificial intelligence (AI) model which is trained based on a correlation between the determined pattern of variations and the at least one of the physical composition of the first plurality of used batteries and the chemical composition of the first plurality of used batteries;
obtain a predicted parameter for a mathematical model associated with a candidate battery using the AI model;
and
evaluate a RUL of the candidate battery using the mathematical model based on the predicted parameter”.
First, an AI model is interpreted as being a type of mathematical model. POSITA would understand that an AI model consists a plurality of mathematical equations to achieve a particular task. The claim language fails to provide any particular mathematical and/or AI model to achieve the task of evaluating a remaining life of a battery.
GORRACHATEGUI teaches:
[Eq. 1-6] a plurality of mathematical equations.
Further, Examiner maintains previous response:
Examiner’s BRI of the above limitation(s) is/are to determine a mathematical pattern/algorithm/model/equation for variation data/information for at least one generic battery parameter (e.g,, voltage, a current, a temperature, or a resistance) for the entire charging/discharging life of generic batteries. Then generically training and utilizing an AI model with the determined mathematical pattern/algorithm/model/equation data/information and composition (e.g., physical or chemical) data/information of the generic batteries to evaluate remaining useful life. The claimed invention fails to claim any particular algorithm that would be considered novel over the cited prior art.
GORRACHATEGUI teaches:
[Fig(s). 1, 2, 15] utilizing neural network models based on data/information acquired from test batteries during every charging/discharging cycle to extract features and estimate remaining useful life data/information;
[Para 0042] a training dataset of occasionally taken measurements…For instance, the training dataset may comprise batteries or cells, which undergo a few number of test cycles until their end-of-life;
[Para 0066] The charging system may determine the variations of the voltage based on an incremental capacity (IC) analysis. In the IC analysis, properties of the battery 114 (e.g., properties related to chemistry of the battery 114, such as oxidation potential, reduction potential, or the like) are tracked.
Examiner is not convinced the applicant invented utilizing an AI model that is trained with battery parameters (e.g., voltage data, current data, temperature data, resistance data, variation data, battery composition (e.g., physical, chemical) data) to calculate/analyze/evaluate remaining useful life of batteries. Examiner maintains the cited reference(s). See updated rejection(s) necessitated by amendment(s).
Conclusion
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
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