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 07/08/2026 has been entered. Claim(s) 1-6, 8-10, 12-17 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 04/08/2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-6, 8-10, 12-17 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to Claim(s) 1, 10, 16, the limitation states
“…
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;
generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the AI model;
wherein
the mathematical model is different from the AI model;
and
evaluate a RUL of the candidate battery using the mathematical model based on the one or more predicted parameters,
wherein
the one or more predicted parameters comprise one or more of
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”.
Examiner’s BRI of an ‘artificial intelligence (AI) model’ is a mathematical model structure trained on large datasets to recognize patterns, make decisions, and generate a result without human intervention. Examiner’s BRI of a ‘mathematical model’ is a model structure utilizing mathatical concepts, equations and logic. The term ‘artificial intelligence (AI) model’ fits within the broader scope of what a ‘mathematical model’ is. Examiner interprets the entire claimed invention to be generic computer structure performing a mathematical algorithm/model as a whole. Each step can be viewed as it’s own mathematical model/equation within a one big mathematical model/equation. Categorizing or grouping each individual step within the entire mathematical model/algorithm as individual models/algorithms does not appear to add a functional significance. It is unclear to one skilled in the art how the terms ‘artificial intelligence (AI) model’ and ‘mathematical model’ are patently distinct from each other beyond the former being a narrow interpretation of the latter.
For examination purposes, Examiner will interpret an ‘artificial intelligence (AI) model’ to be a ‘mathematical model’. With that being said, Examiner interprets every step to of the mathematical algorithm to correspond to a mathematical model/equation. The only step that requires the more narrower scope of an ‘artificial intelligence (AI) model’ is the training of the AI model and the generating of one or more predicted parameters corresponding to one or more of 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. Any prior art that utilizes AI to at least perform these steps or performs all of the steps for the same result of evaluating a RUL of a candidate battery meets the BRI of the claimed invention.
Claim(s) 2-6, 8-9, 12-15, 17 is/are rejected as for being dependent on the above rejected parent claim(s).
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) 1-6, 8-10, 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
a remaining useful life (RUL) prediction,
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;
generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the AI model;
wherein
the mathematical model is different from the AI model;
and
evaluate a RUL of the candidate battery using the mathematical model based on the one or more predicted parameters,
wherein
the one or more predicted parameters comprise one or more of
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
[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
a remaining useful life (RUL) prediction,
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;
estimate the battery parameters using the AI model;
modify a battery model based on the estimated battery parameters,
wherein
the battery model is different form the AI model;
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,
wherein
the estimated battery parameters comprise one or more of
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
[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;
generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the AI model,
wherein
the mathematical model is different from the AI model;
and
evaluate a remaining useful life (RUL) of the battery using the mathematical model based on the one or more predicted parameters,
wherein
the one or more predicted parameters comprise one or more of
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
[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.
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 § 103
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 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.
Claim(s) 1-6, 8-10, 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over 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) / mathematical 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);
generate one or more predicted parameters for an artificial intelligence (AI) / mathematical model associated with a candidate battery based on an output of the artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical model based on the one or more predicted parameters (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15),
wherein
the one or more predicted parameters comprise one or more of
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, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15 | at least one of the physical composition ("capacity","capacitance peak") and the chemical composition (implicit in "capacity fade","internal resistance","voltage at capacitance peak","time interval of equal discharging voltage difference (TIEDVD)") of the first plurality of used batteries ("used rechargeable batteries") comprises a resistance growth ("IR feature 320 increases abruptly when the battery 114 is near its Eal"), a porosity decay rate, a pre-exponential constant defining a Lithium Plating current flux (implicit in "capacity fade"), a capacity drop ("capacity fade","rapid decay in capacity"), and a pre-exponential constant defining a solid electrolyte interface current flux (implicit in "capacity fade"),).
However, GORRACHATEGUI is lacking the explicit language of:
the mathematical model is different from the AI model.
It would be obvious to one skilled in the art to utilize AI modelling and/or mathematical modelling in combination or individually to simply substitute one known element for another to obtain predictable results.
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 (AI) / mathematical 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 (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
estimate the battery parameters using the artificial intelligence (AI) / mathematical model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15);
modify a artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical model (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15),
wherein
the estimated battery parameters comprise one or more of
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, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15 | at least one of the physical composition ("capacity","capacitance peak") and the chemical composition (implicit in "capacity fade","internal resistance","voltage at capacitance peak","time interval of equal discharging voltage difference (TIEDVD)") of the first plurality of used batteries ("used rechargeable batteries") comprises a resistance growth ("IR feature 320 increases abruptly when the battery 114 is near its Eal"), a porosity decay rate, a pre-exponential constant defining a Lithium Plating current flux (implicit in "capacity fade"), a capacity drop ("capacity fade","rapid decay in capacity"), and a pre-exponential constant defining a solid electrolyte interface current flux (implicit in "capacity fade"),).
However, GORRACHATEGUI is lacking the explicit language of:
the battery model is different from the AI model.
It would be obvious to one skilled in the art to utilize AI modelling and/or mathematical modelling in combination or individually to simply substitute one known element for another to obtain predictable results.
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 (AI) / mathematical 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);
generate one or more predicted parameters for artificial intelligence (AI) / mathematical model associated with a candidate battery based on an output of the artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical model based on the one or more predicted parameters (See, e.g., ¶ 0003-0004, 0040-0054, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15),
wherein
the one or more predicted parameters comprise one or more of
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, 0058-0100; See also, e.g., Fig(s). 1-5, 7-15 | at least one of the physical composition ("capacity","capacitance peak") and the chemical composition (implicit in "capacity fade","internal resistance","voltage at capacitance peak","time interval of equal discharging voltage difference (TIEDVD)") of the first plurality of used batteries ("used rechargeable batteries") comprises a resistance growth ("IR feature 320 increases abruptly when the battery 114 is near its Eal"), a porosity decay rate, a pre-exponential constant defining a Lithium Plating current flux (implicit in "capacity fade"), a capacity drop ("capacity fade","rapid decay in capacity"), and a pre-exponential constant defining a solid electrolyte interface current flux (implicit in "capacity fade"),).
However, GORRACHATEGUI is lacking the explicit language of:
the mathematical model is different from the AI model.
It would be obvious to one skilled in the art to utilize AI modelling and/or mathematical modelling in combination or individually to simply substitute one known element for another to obtain predictable results.
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 artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical 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 artificial intelligence (AI) / mathematical 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) 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 07/08/2026, 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) 10-12, filed 07/08/2026, 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 18 of the Office Action, the Examiner asserts:
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.
Without prejudice or disclaimer, and solely in the interest of advancing prosecution, Applicant has amended claim 1 to specify "generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the Al model, wherein the mathematical model is different from the Al model". In addition, Applicant has amended claim 1 to specify that "the one or more predicted parameters comprise one or more of 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", with corresponding changes to the other independent claims.
In addition, Applicant 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 (Al) 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 Al model may be used to generate estimated parameters of a separate mathematical model of a battery, and then the mathematical model may be used to predict the capacity/RUL at any cycle number, or predict the cycle number corresponding to the sudden death of the battery. See, e.g., Specification, paragraphs [0076]-[0077]. Accordingly, Applicant submits that embodiments may use an Al model to automatically generate an improved or fine-tuned mathematical model (which is different from that Al model) that may be used to more accurately predict RUL performance of a battery.
In particular, according to embodiments, an Al model may not directly calculate the RUL. Rather, based on an output of the Al model, embodiments may generate a predicted parameter for a mathematical model that is different from the Al model, and evaluate the RUL of a candidate battery using the mathematical model based on the predicted parameter. Furthermore, the predicted parameter may be a specific physical or chemical parameter that defines the degradation dynamics of the battery, such as resistance growth, porosity decay rate, a pre-exponential constant defining Lithium Plating (LiP) and Solid Electrolyte Interphase (SEI) current flux, and capacity drop. See, e.g., Specification, paragraph [0040]. Accordingly, embodiments may not merely perform mathematical processing of general data using a general-purpose computer. Rather, embodiments may evaluate the RUL by predicting specific parameters representing the battery degradation mechanism and reflecting the predicted parameters on the mathematical model.
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 Al 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).
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.
See updated rejection(s) necessitated by amendment(s).
Applicant's argument(s)/remark(s), see page(s) 13-17, filed 07/08/2026, 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; generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the AI model, wherein the mathematical model is different from the AI model; and evaluate a RUL of the candidate battery using the mathematical model based on the one or more predicted parameters, wherein the one or more predicted parameters comprise one or more of 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," 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 which is different from the machine learning algorithms, and then estimating the RUL using the mathematical model according to the predicted parameter. Instead, it appears that Gorrachategui extracts feature values and inputs the extracted feature values into machine learning algorithms (e.g., the neural network 106 or the expert models 708 and 710), and then directly estimates the RUL based on the output of the machine learning algorithms.
On page 21 of the Office Action, in response to these arguments, the Examiner asserts:
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.
The Examiner then goes on to cite Equations 1-6 of Gorrachategui as corresponding to the claimed mathematical model.
However, Applicant submits that none of Equations 1-6 of Gorrachategui may properly correspond to the claimed mathematical model. For example, Equation 1 represents a logistic regression model that corresponds to the machine learning algorithm of Gorrechategui, and Equations 2-3 are cost functions used to train the logistic regression model. Equation 4 is used to calculate a number of test cycles. In addition, Equation 5 represents a multivariable linear regression (MLR) algorithm that corresponds to the machine learning algorithm of Gorrechategui, and Equation 6 is a cost function used to train the MLR algorithm.
In addition, on pages 22-23 of the Office Action, the Examiner asserts:
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.
Applicant submits that the claimed AI model is not used to evaluate the RUL. Instead, the claimed AI model is used to generate a predicted parameter for a separate mathematical model, and then the mathematical model is used to evaluate the RUL. Applicant further submits, that, as discussed above, Gorrachategui fails to disclose such a mathematical model.
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; generate one or more predicted parameters for a mathematical model associated with a candidate battery based on an output of the AI model, wherein the mathematical model is different from the AI model; and evaluate a RUL of the candidate battery using the mathematical model based on the one or more predicted parameters, wherein the one or more predicted parameters comprise one or more of 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," 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).
Examiner maintains previous response:
Previously rejected Claim 1 stated:
“…
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.
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).
Examiner has elected to update the rejection(s) from a 102 to a 103 necessitated by amendment.
Also see 112(b) rejection(s) necessitated by amendment.
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
Prosecution Insights