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 03/18/2026 has been entered. Claim(s) 1-20 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 12/18/2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1 recites
receive information about a battery in real time
and to
generate state-of-health data;
store the state-of-health data generated by the state data generator,
and to
store past state data which is generated based on the state-of-health data; and
generate a health state model comprising
a formula which indicates an aging trend of the battery based on the past state data,
generate state prediction data based on the state-of-health data generated by the state data generator using the health state model,
determine a confidence interval associated with the state prediction data,
determine whether the state-of-health is outside of the confidence interval associated with a state prediction data,
determine to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data,
and
calculate a remaining life of the battery,
wherein
to generate the health state model,
generate an initial model comprising a non-linear function which includes
a model coefficient,
determine an initial model coefficient by
correcting the model coefficient using a least squares approximation based on the initial model and the past state data, and
generate the health state model based on the initial model coefficient and the initial 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) 10 recites
collecting information about a battery in real time to generate state-of-health data;
generating past state data by storing the state-of-health data;
generating a health state model comprising
a formula which indicates an aging trend of the battery based on the past state data;
generating state prediction data based on the state-of-health data using the health state model;
determining a confidence interval associated with the state prediction data,
determining whether the state-of-health is outside of the confidence interval associated with a state prediction data,
determining to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data;
and
calculating a remaining life of the battery based on the state prediction data
[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) 18 recites
measure information about the battery,
and to
generate sensing data including
output voltage data
and
output current data;
and
generate state prediction data based on the sensing data,
and to
calculate a remaining life of the battery,
receive the sensing data in real time
and to
generate state-of-health data;
a state data store unit configured to
store the state-of-health data generated by the state data generator
and to
generate past state data;
and
generate a health state model indicating an aging trend of the battery in a formula type based on the past state data,
generate the state prediction data based on the state-of-health data generated by the state data generator by using the health state model,
determine a confidence interval associated with the state prediction data,
determine whether the state-of-health is outside of the confidence interval associated with a state prediction data,
determine to regenerate the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data,
and
calculate the remaining life of the battery,
wherein
to generate the health state model,
generate an initial model comprising
a non-linear function
which includes
a model coefficient; and
determine an initial model coefficient by
correcting the model coefficient using a least squares approximation based on the initial model and the past state data; and
generate the health state model based on the initial model coefficient and the initial 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)].
In combination with Independent Claim(s) 1, 10, 18, Claim(s) 2-9, 11-17, 19-20 recite(s)
generate the state-of-health data based on output voltage data and output current data of the battery using an electrical equivalent circuit model of the battery.
generate the state-of-health data based on the output voltage data and the output current data using a dual extended Kalman filter.
generate the state-of-health data and state-of- charge data using the dual extended Kalman filter; and
generate battery cycle data based on the state-of-charge data, and
wherein
the battery cycle data and the state-of-health data is stored in the state data storage as the past state data.
wherein
the electrical equivalent circuit model comprises
a Thevenin equivalent circuit model of the battery.
generate the health state model using the least squares approximation based on the past state data;
apply a particle filter to the health state model
and to
generate the state prediction data according to a likelihood function based on the state-of-health data;
generate a correction signal based on the state prediction data and the state-of-health data; and
calculate the remaining life of the battery based on the state prediction data,
generate a confidence interval based on a likelihood corresponding to the state prediction data, and
generate the correction signal based on the state-of-health data being outside of the confidence interval.
applying a particle filter to the health state model to
generate the state prediction data according to a likelihood function based on the state-of-health data.
wherein
the determining whether to modify the health state model includes:
generating a confidence interval based on a likelihood corresponding to the state prediction data;
based on the state-of-health data being outside of the confidence interval,
generating a correction signal; and
modifying the health state model based on the past state data stored before a time point at which the correction signal was generated.
wherein
based on the health state model being regenerated,
modifying the state prediction data using the modified health state model; and
calculating the remaining life of the battery based on the modified state prediction data.
receive the correction signal, and
based on the correction signal being received,
modify the health state model by correcting the initial model coefficient based on the past state data stored in the state data storage before a time point at which the correction signal was received.
modify the health state model by correcting the initial model coefficient using the least squares approximation based on the stored past state data.
wherein
based on the state-of-health data being within the confidence interval,
the battery life calculator is further configured to
calculate the remaining life of the battery based on the state prediction data.
wherein
the generating of the health state model comprises:
generating an initial model comprising
a non-linear function which includes
a model coefficient;
determining an initial model coefficient by
correcting the model coefficient using
a least squares approximation based on the initial model and the past state data; and
generating the health state model based on the initial model coefficient and the initial 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)].
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 state data generator configured to; a state data storage configured to; a battery life calculator configured to:; a data measurement unit configured to; a battery life prediction device configured to; wherein the battery life prediction device comprises: a state data generator configured to; a state data store unit configured to; a battery life calculator configured to:; wherein the state data generator comprises: a state data calculator configured to; a battery cycle calculator configured to; wherein the battery life calculator comprises:a health state model generator configured to; a state prediction data generator configured to; a correction signal generator configured to; a remaining life prediction unit configured to);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. a data measurement unit configured to); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. a battery including a battery pack; a data measurement unit configured to).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure; generic data acquisition/measurement structure; generic battery)).
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, 2, 6-11, 14-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GARCIA ET AL. (US 2019/0187212) (hereinafter “GARCIA”).
With respect to Claim(s) 1, GARCIA teaches systems and computer-implemented methods are used for analyzing battery information and the BRI of:
a state data generator (See, e.g., Fig(s). 10) configured to
receive information about a battery in real time (See, e.g., Fig(s). 1)
and to
generate state-of-health data (See, e.g., Fig(s). 1);
a state data storage (See, e.g., Fig(s). 10) configured to
store the state-of-health data generated by the state data generator (See, e.g., Fig(s). 1),
and to
store past state data which is generated based on the state-of-health data (See, e.g., Fig(s). 1); and
a battery life calculator (See, e.g., Fig(s). 10) configured to:
generate a health state model (See, e.g., Fig(s). 1) comprising
a formula which indicates an aging trend of the battery based on the past state data (See, e.g., Fig(s). 1),
generate state prediction data based on the state-of-health data generated by the state data generator using the health state model (See, e.g., Fig(s). 1),
determine a confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determine whether the state-of-health is outside of the confidence interval associated with a state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determine to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
and
calculate a remaining life of the battery (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3),
wherein
to generate the health state model (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3),
the battery life calculator is further configured to:
generate an initial model (See, e.g., Fig(s). 1-4, 6-9) comprising
a non-linear function which includes a model coefficient (See, e.g., ¶ 0092-0093, 0103),
determine an initial model coefficient by correcting the model coefficient using a least squares approximation based on the initial model and the past state data (See, e.g., ¶ 0115), and
generate the health state model based on the initial model coefficient and the initial model (See, e.g., Fig(s). 1-4, 6-9).
With respect to Claim(s) 10, GARCIA teaches systems and computer-implemented methods are used for analyzing battery information and the BRI of:
collecting information about a battery in real time to generate state-of-health data (See, e.g., Fig(s). 1);
generating past state data by storing the state-of-health data (See, e.g., Fig(s). 1);
generating a health state model (See, e.g., Fig(s). 1) comprising
a formula which indicates an aging trend of the battery based on the past state data (See, e.g., Fig(s). 1);
generating state prediction data based on the state-of-health data using the health state model (See, e.g., Fig(s). 1);
determining a confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determining whether the state-of-health is outside of the confidence interval associated with a state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determining to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9);
and
calculating a remaining life of the battery based on the state prediction data (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3).
With respect to Claim(s) 18, GARCIA teaches systems and computer-implemented methods are used for analyzing battery information and the BRI of:
a battery (See, e.g., Fig(s). 10) including
a battery pack (See, e.g., Fig(s). 10);
a data measurement unit (See, e.g., Fig(s). 10) configured to
measure information about the battery (See, e.g., Fig(s). 1),
and to
generate sensing data (See, e.g., Fig(s). 1) including
output voltage data (See, e.g., Fig(s). 1)
and
output current data (See, e.g., Fig(s). 1); and
a battery life prediction device (See, e.g., Fig(s). 10) configured to
generate state prediction data based on the sensing data (See, e.g., Fig(s). 1),
and to
calculate a remaining life of the battery (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3),
wherein
the battery life prediction device comprises:
a state data generator (See, e.g., Fig(s). 10) configured to
receive the sensing data in real time (See, e.g., Fig(s). 1)
and to
generate state-of-health data (See, e.g., Fig(s). 1);
a state data store unit (See, e.g., Fig(s). 10) configured to
store the state-of-health data generated by the state data generator (See, e.g., Fig(s). 1)
and to
generate past state data (See, e.g., Fig(s). 1); and
a battery life calculator (See, e.g., Fig(s). 10) configured to:
generate a health state model indicating an aging trend of the battery in a formula type based on the past state data (See, e.g., Fig(s). 1),
generate the state prediction data based on the state-of-health data generated by the state data generator by using the health state model (See, e.g., Fig(s). 1),
determine a confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determine whether the state-of-health is outside of the confidence interval associated with a state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
determine to regenerate the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data (See, e.g., ¶ 0089, 0124; See also, e.g., Fig(s). 1-4, 6-9),
and
calculate the remaining life of the battery (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3),
wherein
to generate the health state model,
the battery life calculator is further configured to:
generate an initial model (See, e.g., Fig(s). 1-4, 6-9) comprising
a non-linear function which includes a model coefficient (See, e.g., ¶ 0092-0093, 0103); and
determine an initial model coefficient by correcting the model coefficient using a least squares approximation based on the initial model and the past state data (See, e.g., ¶ 0115); and
generate the health state model based on the initial model coefficient and the initial model (See, e.g., Fig(s). 1-4, 6-9).
With respect to Claim(s) 2, 11, 19, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the state data generator is further configured to
generate the state-of-health data based on output voltage data and output current data of the battery using an electrical equivalent circuit model of the battery (See, e.g., Fig(s). 1).
With respect to Claim(s) 6, 20, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the battery life calculator comprises:
a health state model generator (See, e.g., Fig(s). 10) configured to
generate the health state model using the least squares approximation based on the past state data (See, e.g., ¶ 0115);
a state prediction data generator (See, e.g., Fig(s). 10) configured to
apply a particle filter to the health state model (See, e.g., ¶ 0009, 0010)
and to
generate the state prediction data according to a likelihood function based on the state-of-health data (See, e.g., ¶ 0126);
a correction signal generator (See, e.g., Fig(s). 10) configured to
generate a correction signal based on the state prediction data and the state-of-health data (See, e.g., ¶ 0105, 0116, 0132); and
a remaining life prediction unit (See, e.g., Fig(s). 10) configured to
calculate the remaining life of the battery based on the state prediction data (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3),
wherein
the state prediction data generator is configured to
generate a confidence interval based on a likelihood corresponding to the state prediction data (See, e.g., ¶ 0104, 0105, 0108, 0116, 0128, 0132), and
wherein
the correction signal generator is further configured to
generate the correction signal based on the state-of-health data being outside of the confidence interval (See, e.g., ¶ 0105, 0116, 0132).
With respect to Claim(s) 15, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the generating of the state prediction data comprises
applying a particle filter to the health state model (See, e.g., ¶ 0009, 0010)
to
generate the state prediction data according to a likelihood function based on the state-of-health data (See, e.g., ¶ 0126).
With respect to Claim(s) 16, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the determining whether to modify the health state model includes:
generating a confidence interval based on a likelihood corresponding to the state prediction data (See, e.g., ¶ 0104, 0105, 0108, 0116, 0128, 0132);
based on the state-of-health data being outside of the confidence interval,
generating a correction signal (See, e.g., ¶ 0105, 0116, 0132); and
modifying the health state model based on the past state data stored before a time point at which the correction signal was generated (See, e.g., ¶ 0105, 0116, 0132).
With respect to Claim(s) 17, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
based on the health state model being regenerated,
the method further comprises:
modifying the state prediction data using the modified health state model (See, e.g., ¶ 0105, 0116, 0132); and
calculating the remaining life of the battery based on the modified state prediction data (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3).
With respect to Claim(s) 7, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the health state model generator is further configured to:
receive the correction signal (See, e.g., ¶ 0105, 0116, 0132), and
based on the correction signal being received,
modify the health state model
by
correcting the initial model coefficient based on the past state data stored in the state data storage before a time point at which the correction signal was received (See, e.g., ¶ 0105, 0116, 0132).
With respect to Claim(s) 8, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the health state model generator is further configured to
modify the health state model
by
correcting the initial model coefficient using the least squares approximation based on the stored past state data (See, e.g., ¶ 0105, 0115, 0116, 0132).
With respect to Claim(s) 9, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
based on the state-of-health data being within the confidence interval,
the battery life calculator is further configured to
calculate the remaining life of the battery based on the state prediction data (See, e.g., ¶ 0011; See also, e.g., Fig(s). 3).
With respect to Claim(s) 14, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the generating of the health state model comprises:
generating an initial model (See, e.g., Fig(s). 1-4, 6-9) comprising
a non-linear function which includes a model coefficient (See, e.g., ¶ 0092-0093, 0103);
determining an initial model coefficient by correcting the model coefficient using a least squares approximation based on the initial model and the past state data (See, e.g., ¶ 0115); and
generating the health state model based on the initial model coefficient and the initial model (See, e.g., Fig(s). 1-4, 6-9).
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) 3, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited references of the parent claim(s) in view of CHOI (US 2015/0377974).
With respect to Claim(s) 3, 12, GARCIA teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the state data generator is further configured to
generate the state-of-health data based on the output voltage data and the output current data (See, e.g., Fig(s). 1).
However, GARCIA is lacking the explicit language of:
using a dual extended Kalman filter.
CHOI teaches a battery state estimation method and system and the BRI of:
using a dual extended Kalman filter (See, e.g., ¶ 0052).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARCIA to include using a dual extended Kalman filter.
One of ordinary skill in the art would have been motivated to modify GARCIA because it would be beneficial to improve SOC/SOH estimation. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Claim(s) 4, 5, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited references of the parent claim(s) in view of CHOI (US 2015/0377974), JO ET AL. (US 2023/0076118) (hereinafter “JO”).
With respect to Claim(s) 4, GARCIA, CHOI teaches the BRI of the parent claim(s).
GARCIA further teaches the BRI of:
wherein
the state data generator comprises:
a state data calculator (See, e.g., Fig(s). 10) configured to
generate the state-of-health data and state-of- charge data (See, e.g., Fig(s). 1).
CHOI further teaches the BRI of:
using a dual extended Kalman filter (See, e.g., ¶ 0052).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARCIA to include using a dual extended Kalman filter.
One of ordinary skill in the art would have been motivated to modify GARCIA because it would be beneficial to improve SOC/SOH estimation. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
However, GARCIA is lacking the explicit language of:
a battery cycle calculator configured to generate battery cycle data based on the state-of-charge data, and wherein the battery cycle data and the state-of-health data is stored in the state data storage as the past state data.
JO teaches a battery state prediction device and the BRI of:
a battery cycle calculator configured to generate battery cycle data based on the state-of-charge data, and wherein the battery cycle data and the state-of-health data is stored in the state data storage as the past state data (See, e.g., ¶ 0045).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARCIA to include a battery cycle calculator configured to generate battery cycle data based on the state-of-charge data, and wherein the battery cycle data and the state-of-health data is stored in the state data storage as the past state data.
One of ordinary skill in the art would have been motivated to modify GARCIA because it would be beneficial to improve predicant state of a battery. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 5, 13, GARCIA, CHOI, JO teaches the BRI of the parent claim(s).
JO further teaches the BRI of:
wherein
the electrical equivalent circuit model comprises
a Thevenin equivalent circuit model of the battery (See, e.g., ¶ 0049, 0050).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARCIA to include wherein the electrical equivalent circuit model comprises a Thevenin equivalent circuit model of the battery.
One of ordinary skill in the art would have been motivated to modify GARCIA because it would be beneficial to improve predicant state of a battery. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Response to Arguments
Applicant’s amendments, filed on 03/18/2026, 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) 11-15, filed 03/18/2026, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under § 101 because the Examiner asserts that the claims are directed to an abstract idea without significantly more.
In particular, the Examiner asserts that the steps of claim 1 relate to "[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 addition, the Examiner asserts that many of the elements of claim 1 (e.g., the battery life calculator, the health state model generator, the state prediction data generator, etc.) represent no more than a generic computer used to perform generic computer functions. See, e.g., Office Action, pages 6-7.
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 5i 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 such as the claimed battery life calculator, health state model generator, and state prediction data generator as "generic computer elements". 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 monitoring and management, as discussed in greater detail below.
In addition, without conceding to the merits of the Examiner's assertions, and solely in the interest of advancing prosecution, Applicant has amended claim 1 to specify "determine a confidence interval associated with the state prediction data, determine whether the state-of- health data is outside of the confidence interval associated with the state prediction data, [and] determine to modify the health state model based on the state-of-health data being outside of a confidence interval associated with the state prediction data". Accordingly, Applicant submits that claim 1 is patentable at least at Step 2A, Prong Two, as discussed below.
MPEP §2106.04(d) states (emphasis added):
A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field.
This section also provides guidance for determining whether a judicial exception is integrated into a practical application, stating "first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement", and further stating "if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement". Further, unlike the Step 2B analysis, the analysis under Step 2A Prong Two is performed "without reference to what is well-understood, routine, conventional activity".
According to non-limiting exemplary embodiments of the present Specification consistent with claim 1, in some methods for remaining battery life prediction, a battery manufacturer may conduct an aging test in advance, and provide the user with the aging data accumulated during the experiment, and the user may predict the remaining life state in the future based on the battery usage time. However, because the aging trend of the battery may vary depending on the tolerance occurring at the time of manufacture, the operating pattern such as charging and discharging of the battery, and the external environment condition, these methods may have a limitation in reflecting the above external factors of the battery, which may allow an error to occur between the predicted life and the actual lifespan, thereby causing a decrease in the reliability of the system and the economic loss due to a power supply problem of the UPS.. See Specification, paragraphs [0004], [0121]. However, non-limiting exemplary embodiments of the present Specification may calculate state prediction data and a corresponding confidence interval, and may determine whether state-of-health data generated by a health state model is within the confidence interval. If the state-of-health data is outside of the confidence interval, embodiments may regenerate the health state model to provide greater accuracy. See, e.g., Specification, paragraph [0122].
Therefore, Applicant submits that the Specification clearly articulates an improvement to the technologies of battery monitoring and management. Further, Applicant submits that the this improvement is properly reflected in the claims, for example in the portions of the claims relating to determining to modify the health state model based on the state-of-health data being outside of a confidence interval associated with the state prediction data.
Therefore, Applicant submits that, even assuming arguendo that the claims recite any judicial exception, to which Applicant does not acquiesce, Applicant submits that any potential judicial exception is integrated into a practical application which includes an improvement to computer technology. Therefore, Applicant submits that the claims are not directed to a judicial exception, and are patent eligible at least at Step 2A Prong Two of the patent eligibility analysis.
Accordingly, Applicant submits that the claims satisfy all of the requirements of § 101, and respectfully requests that these rejections 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.
Examiner’s BRI of the claimed inventions is generic computer structure being used as a tool to generically acquire data corresponding to a battery and mathematically processing said data to update/calibrate a mathematical model and eventually output a result from the updated/calibrated mathematical model.
When examining step 2A Prong 1, Examiner determines if there is an abstract idea present. One skilled in the art can at least perform the identified abstract idea utilizing Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation. 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). The arguments, in light of the specification, fail to convince the Examiner that utilizing Mathematical Concepts and/or Mental Processes does not fit within the scope of the identified abstract limitations.
When examining step 2A Prong 2, Examiner examines the additional elements to determine if the identified abstract idea has been practically applied in a particular way in a particular technology. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). The additional elements, when viewed individually and in combination with the identified abstract idea, do not add anything beyond mere instructions to implement an abstract idea on a computer, adding generic ‘apply it’ language, and generically linking the identified abstract idea to a technological environment or field of use.
When examining step 2B, Examiner examines the additional elements to determine if they amount to significantly more than the abstract idea. The only additional element(s) is/are the generic computer structure being used as a tool to perform the abstract idea and the battery where the generic computer structure is generically acquiring the data from. 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) 15-19, filed 03/18/2026, with respect to the art rejection(s) has/have been fully considered.
-Applicant states
“Claim Rejections - 35 USC § 102
Claims 1-2, 6-11, and 14-20 are rejected under § 102(a)(1) as being anticipated by Garcia (US 2019/0187212).
Applicant respectfully traverses these rejections and requests reconsideration.
…
Applicant respectfully submits that claim 1 is patentable because Garcia fails to disclose or suggest each and every element of the claim. For example, Applicant respectfully submits that Garcia fails to disclose "a state data generator configured to receive information about a battery in real time and to generate state-of-health data;... and a battery life calculator configured to: generate a health state model comprising a formula which indicates an aging trend of the battery based on the past state data, generate state prediction data based on the state-of- health data generated by the state data generator using the health state model, determine a confidence interval associated with the state prediction data, determine whether the state- of-health data is outside of the confidence interval associated with the state prediction data, [and] determine to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data," as claimed.
Garcia discloses a battery condition monitoring (BCM) system that may include a training and learning module for tuning embedded models based on training data, a feature extraction module for computing internal battery parameters, a state estimation module for estimating current battery health conditions (e.g., SOC, SOH), and a state prediction module for predicting the future condition state of the battery. See Garcia, paragraph [0072]. To perform the SOH estimation, a plurality of health metrics may be estimated, and the health metrics may be combined or fused to obtain an aggregate SOH estimation according to weights determined based on a confidence measure. See, e.g., Garcia, paragraph [0104], [0108]. Then, a future condition state of the battery may be predicted by calculating a plurality of health metrics at a future time instance, and then combining or fusing this plurality of future health metrics based on a confidence measure, similar to the aggregate SOH estimation discussed above. See Garcia, paragraphs [0117], [0132].
As a result, to the extent that Garcia discusses obtaining a confidence measure, this confidence measure is only used to combine or fuse multiple estimated health metrics or predicted future health metrics into a single value. Therefore, nothing in Garcia discloses calculating a confidence measure associated with a predicted future health metric, and then comparing a measured health metric to the confidence measure associated with the predicted future health metric. Accordingly, it is impossible for Garcia to disclose updating any of the models included in the BCM based on such a comparison, because no such comparison occurs in Garcia.
Therefore, Garcia fails to disclose or suggest "a state data generator configured to receive information about a battery in real time and to generate state-of-health data;... and a battery life calculator configured to: generate a health state model comprising a formula which indicates an aging trend of the battery based on the past state data, generate state prediction data based on the state-of-health data generated by the state data generator using the health state model, determine a confidence interval associated with the state prediction data, determine whether the state-of-health data is outside of the confidence interval associated with the state prediction data, [and] determine to modify the health state model based on the state- of-health data being outside of the confidence interval associated with the state prediction data," 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 and 18, Applicant respectfully submits that claims 10 and 18 are patentable for at least similar reasons as those provided above with reference to claim 1.
Regarding dependent claims 2, 6-9, 11, 14-17, and 19-20 , 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).
The above arguments are directed towards the claimed limitations of:
“a state data generator configured to
receive information about a battery in real time and to generate state-of-health data;
…
a battery life calculator configured to:
generate a health state model comprising a formula which indicates an aging trend of the battery based on the past state data, generate state prediction data based on the state-of-health data generated by the state data generator using the health state model, determine a confidence interval associated with the state prediction data, determine whether the state-of-health is outside of the confidence interval associated with a state prediction data, determine to modify the health state model based on the state-of-health data being outside of the confidence interval associated with the state prediction data,
…”
Examiner’s BRI of the structures of the ‘state data generator’ and ‘battery life calculator’ is generic computer structure.
Examiner’s BRI of said function(s) of the ‘state data generator’ is/are to generically receive/generate data corresponding to the state of health of a battery.
Examiner’s BRI of said function(s) of the ‘battery life calculator’ is/are to generate and utilize a mathematical model corresponding to state of health of a battery to predict the state of the battery, utilizing a confidence/validation metric to validate the result of the mathematical model, and update/calibrate/modify said mathematical model if the result does not meet the confidence/validation metric.
GARCIA teaches:
[Fig. 1] “input data 102 include impedance spectra, temperatures, offload/underload conditions, voltages, and currents collected at sampling rates that are accordingly selected for the applications at hand”, “learned battery models 108 including electrical equivalent circuits, electrochemical aging models, analytical/kinetic models, as well as other battery models”, “Three primary operations, generally executed in sequence, are: 1) state estimation and prediction 120 of internal battery parameters based on observations and embedded knowledge, 2) state interpretation 150 of these calculated parameters, and 3) computation of health estimation and prediction 170”, “output data 195 include SOC, SOH, RUL, and EOL”.
[Fig. 2] “training, learning, feature extraction, state update, and state propagation methods. This diagram illustrates basic modules of the present disclosure. Four main modules are: 1) a training and learning module 210 used for tuning embedded models based on training data; 2) a feature extraction module 220 used for computing internal battery parameters; 3) a state estimation module 230 used for estimating the current condition state of the battery based on current and historical information, which is then used to estimate current battery health conditions (e.g., SOC, SOH); and 4) a state prediction module 240 used for predicting the future condition state of the battery based on forecasted or assumed future operational conditions of interest for what-if analysis, which is then used to predict future battery health conditions (e.g., RUL, EOL)”
[0089] “temperature data are used to accordingly modify baseline aging mappings and databases learned and stored when necessary”
[0124] “this module first selects a given operational profile, which then translates to a specific aging model used to predict future values of battery parameters, battery states, or a combination thereof. These estimations are then used to compute RUL[k]. If this estimate does not meet given needs, the model then picks/modifies the assumed operating profile and repeats this cycle, which is terminated when RUL[k] meets the specification sRUL[k] desired at k. Thus, the LES module 892 computes output 894 as LES(k)”
In view of GARCIA, Examiner is not persuaded that the inventor invented function of generically receiving/generating data corresponding to the state of health of a battery, generating and utilizing a mathematical model corresponding to state of health of a battery to predict the state of the battery, utilizing a confidence/validation metric to validate the result of the mathematical model, and updating/calibrating/modifying said mathematical model if the result does not meet the confidence/validation metric.
-Applicant states
“Claim Rejections - 35 USC § 103
Claims 3 and 12 are rejected under § 103 as being unpatentable over Garcia in view of Choi (US 2015/0377974).
Claims 4-5 and 13 are rejected under § 103 as being unpatentable Garcia in view of Choi and Jo (US 2023/0076118).
Applicant submits that Choi and Jo fail to remedy the deficiencies of Garcia discussed above, and are not relied upon by the Examiner to do so. Therefore, Applicant submits that these claims are patentable for at least the reasons discussed above due to their respective dependencies.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
See response above.
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
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