DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/8/2026 has been entered.
Response to Amendment
Claims 1-4, 6-12, 14 remain pending in the application along with added new claims 16-18, and claims 5, 13, and 15 have been canceled. Applicant’s amendments and arguments have overcome every 103 rejection previously set forth in the Final Office Action mailed 4/1/2026.
Response to Arguments
Applicant's arguments filed 5/8/2026 have been fully considered and they are persuasive.
The applicant submits on page 8 that Juang does not describe the stochastic gradient descent as determining gradients at predefined maximum and minimum outer limit values and at a mean value of a loss function as recited in claim 1. Therefore, Juang cannot disclose using such evaluations of gradients to narrow a search domain. In fact, Juang discloses the stochastic gradient descent operates in a high-dimensional, unbounded neural-network weight space. The stochastic gradient descent of Juang is not described as selecting or refining a bounded scalar parameter interval. However, the amended claims require that gradient evaluations at predefined boundary and mean values are used to select a reduced parameter interval for the parameter (d). Furthermore, Juang merely uses gradient values solely to compute incremental weight updates during back-propagation. In the amended claims, the sign change of the gradient is used, as expressly recited in amended claim 1, to select a reduced parameter interval for the parameter (d) within which further optimization is performed. The cited portions of Juang are silent regarding selecting or refining a bounded scalar parameter interval. Therefore, Juang fails to teach or suggest the claimed "gradient-based sign change interval parameter selection" of the deterministic gradient descent method recited in claim 1 as amended. Accordingly, Juang fails to cure the deficiencies of Mohajer.
The applicant submits on page 8 that Brownlee describes standard gradient descent in which parameters are updated iteratively. However, Brownlee does not disclose defining, selecting, or constraining a parameter interval, let alone reducing the parameter interval based on gradient sign changes as required by claim 1 as amended. While Brownlee discloses gradients may change sign during optimization, Brownlee does not teach using a sign change to select a bounded parameter interval. Furthermore, any sign changes in Brownlee are incidental outcomes of iteration, not decision inputs for bracketing. Brownlee does not teach or suggest selecting a reduced parameter interval for parameter (d) using sign changes of determined gradients determined at predefined boundary and mean values as generally recited in claim 1 as amended. Brownlee also does not disclose constraining subsequent optimization to the selected interval, nor does Brownlee describe any mechanism by which an interval selection determines the domain of further optimization, as expressly required by amended claim 1. Therefore, Brownlee cannot cure the deficiencies of Mohajer and Juang. Thus, claim 1 is allowable for at least these reasons stated herein.
The examiner submits that because the mean value is not clearly specified, the mean value can essentially be any value as it may be continuously updated by added data to the loss function f(d). As evidenced by Brownlee, additional added data points allows further educated parameter inputs to determine if there is a minimum of the loss function. A person of ordinary skill in the art would recognize a gradient sign change of the loss function would indicate the passing of an inflection point and would narrow the parameter interview to more finely locate the inflection point.
Claim Objections
Claim 12 objected to because of the following informalities:
Claim 12 recites “a present state of charge and/or a present state of health and/or the second side reaction current are determined.” The use of two and/or conjugations make it unclear what phrases are being conjugated. For the purposes of compact prosecution, the examiner interprets the phrase as “a present state of charge, a present state of health, and/or the second side reaction current are determined.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-12, 14, and 16-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 recite “deterministic gradient method.” However, the closest concept the examiner can find of a “deterministic gradient method” are in paragraphs [14, 25, 52, 65-66] with phrases such as “a minimum of a loss function f(d) of a parameter d of a polynomial charging profile Ip(t) is numerically determined”] and “the gradient of the loss function f(d) at the outer limit values dmin and dmax of the loss function f(d) and at a mean value dm of the loss function f(d) halfway between the outer limit values dmin and dmax is first of all determined.” The above descriptions do not appear to match the term “deterministic” in regards to producing predictable outcomes.
Claims 2-4, 6-10 and 12, 14, and 16-18 inherit the deficiencies of parent claims 1 and 11, are also rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement.
Claim 16 recites “parameter (d) is constrained to a predefined bounded parameter interval throughout optimization.” This does not appear to be present in the Specification.
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.
Claims 1-4, 6-12, 14, and 16-18 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.
Claims 1 and 11 recite “using the deterministic gradient method includes determining a gradient of the loss function at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d) and further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed.”
It is not clear how the following clauses are related to each other:
“determining a gradient of the loss function”
“at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d)”
“selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed.”
The examiner interprets the clause as the following:
wherein using the deterministic gradient method includes determining a gradient of the loss function at:
(a) a maximum outer limit value of the loss function f(d),
(b) a minimum outer limit value of the loss function f(d), and
(c) a mean value of the loss function f(d);
wherein using the deterministic gradient method further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed.
Claims 1 and 11 recite “a mean value of the loss function f(d).” It is not clear what is the mean value. Fig. 4 appears to show mean value dm as a mean for the x-axis values, parameter d, and not the mean value for the y-axis values, loss function f(d).
Claims 2-4, 6-10 and 12, 14, and 16-18 inherit the deficiencies of parent claims 1 and 11, are also rejected under 35 U.S.C. 112(b) as being indefinite.
NOTE: There does not appear to be a transitional phrase in claim 1. The examiner interprets the preamble as “A charger for an electrical energy store” and the body as the wherein clauses that follow. If transitional phrases were meant to be used, please see MPEP 2111.03 for transitional phrases.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-12, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the ScienceDirect Publication of Mohajer et al. (“Design of a Model-based Fractional-Order Controller for Optimal Charging of Batteries,” IFAC Conference PapersOnLine, ScienceDirect, presented Sep. 4-6, 2018) in view of Juang et al. (US 20180086222 A1) as evidenced by Brownlee (“Linear Regression Tutorial Using Gradient Descent for Machine Learning” Machine Learning Mastery, < https://machinelearningmastery.com/linear-regression-tutorial-using-gradient-descent-for-machine-learning/ > Published online 3/29/2016), hereinafter respectively referred to as Mohajer, Juang, and Brownlee.
Regarding independent claim 1, Mohajer teaches a charger (Fig. 6 and Section 4: robust controller) for an electrical energy store (Fig. 6: battery),
wherein the charger has an open-loop control unit (plant model current trajectories, Ich trajectories and Jsr trajectories) and a closed-loop control unit (controller C(s)),
wherein the charger is configured to charge the electrical energy store to a defined state of charge within a preset charging time (Fig. 2: fixed charging time of 20 minutes) and to set a charging current (Fig. 6: ICh,traj) and a side reaction current (Jsr,traj or Jsr,obs) of the electrical energy store (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature)
wherein an affine or polynomial charging profile is optimized by numerically determining a minimum of a loss function of a parameter of the charging profile, wherein a first charging current and a first side reaction current are subjected to open-loop control according to an optimized charging profile (p. 98, col. 1, par. 1-2, Sec. 2; p. 100, col. 2, par. 1; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature. The examiner interprets the loss function as a loss of optimization function where the parameters of SOC, aging, and temperature are adjusted to minimize optimization loss ),
wherein the charging current is one of a first charging current (ICh,traj), a second charging current (ICh,FF), a third charging current (ICh,FB), or a fourth charging current (Icharge),
wherein the side reaction current is one of a first side reaction current (Jsr,traj) or a second side reaction current (Jsr,obs),
Mohajer does not explicitly teach the use of a deterministic gradient method to determine the minimum of a loss function of a parameter of the charging profile,
wherein using the gradient method includes determining the deterministic gradient of the loss function at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d) and further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed.
Juang teaches the use of a deterministic gradient method to determine the minimum of a loss function of a parameter (¶’s [31-32]: Recurrent Neural Networks (RNN) for battery modeling uses stochastic gradient descent method for appropriate mapping of input to output based on temperature dependency and does not require foreknowledge of battery physical parameters. The examiner interprets the loss function as a loss of optimization function where the parameters are adjusted to minimize optimization loss. The method is deterministic in that any parameter inputted to the algorithm would generate a repeatable data point for the loss function),
wherein using the deterministic gradient method includes determining the gradient of the loss function at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d) and further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed (As evidenced by Brownlee page 5/18, see plot of error versus iteration below, the stochastic gradient descent method determines coefficients for the optimization function to make a best fit for a trendline against data. Through the process, maximum and minimum outer limits of the errors are determined and errors are reduced to be closer to zero over more iterations. The examiner interprets a sign change of determined gradients as the slope change between positive and negative in the error plot below which would correspond to positive and negative slopes in the loss of optimization function where iterations made to approach the function minimum results in a positive slope (growing loss with parameters increasing) if tried parameters cause an overshoot of the function minimum and results in a negative slope (decreasing loss with parameters increasing) if tried parameters cause an undershoot of the function minimum. A person of ordinary skill in the art would recognize a gradient sign change of the loss function would indicate the passing of an inflection point and would narrow the parameter interview to more finely locate the inflection point).
PNG
media_image1.png
436
635
media_image1.png
Greyscale
Both Mohajer and Juang teach systems for optimization. Mohajer optimizes charging profiles based on battery SOC, aging, and temperature. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to incorporate the stochastic gradient descent optimization method in the system of Juang into the system of Mohajer. Doing so would aid Mohajer in the minimization of ageing with optimization of charging based on a given data points in a successive iteration of instances, a simpler way for optimization as compared to depending on a long sequence of data (Juang - ¶0031: long sequence of output currents).
Regarding claim 2, Mohajer teaches the charger as claimed in claim 1, wherein the charger has an evaluation unit (Fig. 6: cell observer), which has at least one terminal for a sensor (sensors that produce temperature signal T and cell voltage signal Ucell) of the electrical energy store (battery), wherein the evaluation unit is configured to determine at least aging (aging observer) of the electrical energy store by means of a simplified linear electrothermal aging model of the electrical energy store (p. 98, col. 1, par. 1-2, Sec. 2; p. 100, col. 2, par. 1; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature. Cell observer measures the cell voltage and temperature which is compared with the measured trajectories of side reaction current).
Regarding claim 3, Mohajer teaches the charger as claimed in claim 2,
wherein the evaluation unit is connected in signal-conducting fashion to the open-loop control unit and/or to the closed-loop control unit (Fig. 6: currents, Jsr,traj of the cell observer and Jsr,obs of Jsr trajectories, are compared and sent to controller C(s)).
Regarding claim 4, Mohajer teaches the charger as claimed in claim 1,
wherein the open-loop control unit (Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories) is configured to subject the first charging current (ICh,traj) and the first side reaction current (Jsr,traj) to open-loop control so that the electrical energy store (battery) is charged to the defined state of charge within the preset charging time (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature).
Regarding claim 6, Mohajer teaches the charger as claimed in claim 1, wherein the open-loop control unit has a charge open-loop control means configured to subject the first charging current to open-loop control according to the optimized charging profile (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature).
Regarding claim 7, Mohajer teaches the charger as claimed in claim 1, wherein the closed-loop control unit (Fig. 6: controller C(s)) is configured to subject a third charging current (ICh,FB) to closed-loop control so that the second side reaction current of the electrical energy store is minimized (p. 98, col. 2, par. 2, trajectories set to minimize capacity loss).
Regarding claim 8, Mohajer teaches the charger as claimed in claim 1, wherein the charger has a summation means (Fig. 6: the circle by two plus signs and receiving charging currents ICh,FF and IChFB), which is arranged between the open-loop control unit (Ich trajectories and low pass filter LPF) and the closed-loop control unit (controller C(s)), on one side, and an output terminal of the charger (the output line where charge current Icharge meets the battery), on another side, wherein the summation means is configured to add the first charging current or the second charging current (ICh,FF) from the open-loop control unit (Ich trajectories and low pass filter LPF) and the third charging current (ICh,FB) from the closed-loop control unit (controller C(s)) and to generate the fourth charging current (Icharge).
Regarding claim 9, Mohajer teaches the charger as claimed in claim 8, wherein the charger has a low-pass filter (Fig. 6: LPF), which is arranged between the open-loop control unit and the summation means.
Regarding claim 10, Mohajer teaches the charger as claimed in claim 8, wherein the charger has a comparison means (Fig. 6: the circle by a plus sign and minus sign and receiving side reaction currents Jsr,traj and Jsr,obs), which is arranged between the open-loop control unit (JST trajectories) and an ageing evaluation means (aging observer in the cell observer), on one side, and the summation means (the circle by two plus signs and receiving ICh,FB and ICh,FF and outputting ICharge), on another side, wherein the comparison means is configured to compare the first side reaction current and the second side reaction current (Fig. 6: the circle by a plus sign and minus sign and receiving side reaction currents Jsr,traj and Jsr,obs).
Regarding independent claim 11, Mohajer teaches a method for charging an electrical energy store (Fig. 6: battery) by means of a charger having an open-loop control unit (Ich trajectories and Jsr trajectories) and a closed-loop control unit (controller C(s)),
wherein the charger is configured to charge the electrical energy store to a defined state of charge within a preset charging time and to set a charging current (ICh,traj) and a side reaction current (Jsr,traj) of the electrical energy store (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature),
wherein the method comprises an open-loop control step and a closed-loop control step, which run simultaneously (Fig. 6: charging current Ich,FF from Ich trajectories and low pass filter LPF is produced independently from charging current Ich,FB from controller (C(s)),
wherein the electrical energy store is charged to a defined state of charge within a preset charging time and a charging current (ICh,traj) and a side reaction current (Jsr,traj) of the electrical energy store are set (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature)
wherein an affine or polynomial charging profile is optimized by numerically determining a minimum of a loss function of a parameter of the charging profile, wherein a first charging current and a first side reaction current are subjected to open-loop control according to an optimized charging profile (p. 98, col. 1, par. 1-2, Sec. 2; p. 100, col. 2, par. 1; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature. The examiner interprets the loss function as a loss of optimization function where the parameters of SOC, aging, and temperature are adjusted to minimize optimization loss),
wherein the charging current is one of a first charging current (ICh,traj), a second charging current (ICh,FF), a third charging current (ICh,FB), or a fourth charging current (Icharge),
wherein the side reaction current is one of a first side reaction current (Jsr,traj) or a second side reaction current (Jsr,obs),
Mohajer does not explicitly teach the use of a deterministic gradient method to determine the minimum of a loss function of a parameter of the charging profile,
wherein using the deterministic gradient method includes determining the gradient of the loss function at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d) and further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed.
Juang teaches the use of a deterministic gradient method to determine the minimum of a loss function of a parameter (¶’s [31-32]: Recurrent Neural Networks (RNN) for battery modeling uses stochastic gradient descent method for appropriate mapping of input to output based on temperature dependency and does not require foreknowledge of battery physical parameters. The examiner interprets the loss function as a loss of optimization function where the parameters are adjusted to minimize optimization loss. The method is deterministic in that any parameter inputted to the algorithm would generate a repeatable data point for the loss function),
wherein using the deterministic gradient method includes determining the gradient of the loss function at maximum and minimum outer limit values of the loss function f(d), and at a mean value of the loss function f(d) and further includes selecting, using a sign change of the determined gradients, a reduced parameter interval for the parameter (d) within which further optimization is performed (As evidenced by Brownlee page 5/18, see plot of error versus iteration below, the stochastic gradient descent method determines coefficients for the optimization function to make a best fit for a trendline against data. Through the process, maximum and minimum outer limits of the errors are determined and errors are reduced to be closer to zero over more iterations. The examiner interprets a sign change of determined gradients as the slope change between positive and negative in the error plot below which would correspond to positive and negative slopes in the loss of optimization function where iterations made to approach the function minimum results in a positive slope (growing loss with parameters increasing) if tried parameters cause an overshoot of the function minimum and results in a negative slope (decreasing loss with parameters increasing) if tried parameters cause an undershoot of the function minimum. A person of ordinary skill in the art would recognize a gradient sign change of the loss function would indicate the passing of an inflection point and would narrow the parameter interview to more finely locate the inflection point).
PNG
media_image1.png
436
635
media_image1.png
Greyscale
Both Mohajer and Juang teach systems for optimization. Mohajer optimizes charging profiles based on battery SOC, aging, and temperature. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to incorporate the stochastic gradient descent optimization method in the system of Juang into the system of Mohajer. Doing so would aid Mohajer in the minimization of ageing with optimization of charging based on a given data points in a successive iteration of instances, a simpler way for optimization as compared to depending on a long sequence of data (Juang - ¶0031: long sequence of output currents).
Regarding claim 12, Mohajer teaches the method as claimed in claim 11,
wherein a present state of charge and/or a present state of health and/or the second side reaction current are determined from sensor data of the electrical energy store by means of a simplified linear electrothermal aging model of the electrical energy store (p. 98, col. 1, par. 1-2, Sec. 2; p. 100, col. 2, par. 1; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature. Cell observer measures the cell voltage and temperature which is compared with the measured trajectories of side reaction current).
Regarding claim 14, Mohajer teaches the method as claimed in claim 13,
wherein the first side reaction current is compared with the second side reaction current (Fig. 6: the circle by a plus sign and minus sign and receiving side reaction currents Jsr,traj and Jsr,obs), and a third charging current is generated (Ich,FB),
wherein the third charging current is equal to zero when the first side reaction current has the same value as the second side reaction current and/or wherein, when the first side reaction current and the second side reaction current have different values (it is predictable to a person of ordinary skill in the art that the difference between two same currents would yield zero current and the difference between two difference currents would yield a nonzero current value), the third charging current (Ich,FB) is determined so an ageing of the electrical energy store is minimized (p. 98, col. 2, par. 2, trajectories set to minimize capacity loss),
wherein the third charging current (Ich,FB) and the second charging current are added, and a fourth charging current is generated (the circle by two plus signs and receiving charging currents, ICh,FB and ICh,FF , and outputting ICharge), and
wherein the electrical energy store is charged with the fourth charging current (ICharge is sent to the battery).
Regarding claim 15, Mohajer teaches the charger as claimed in claim 1, wherein using the deterministic gradient method further includes:
determining a range, within the maximum and minimum outer limit values, for the parameter (d) based on the mean value of the loss function f(d) and a sign change of the gradient of the loss function (As evidence by Brownlee, pages 3/18 to 4/18, a range is constructed in a gradient method by starting with plugging in a first try value to get the first error value and then proceeding to plug in more values to get a sufficient set of errors to identify the loss function behavior. Lowest and highest input values would correspond to a minimum and maximum of the range. If the try values result in the crossing of the minimum of the loss function (minimum of resulting errors), a sign change for the gradient will occur because the function is changing between a decreasing slope and an increasing slope).
Regarding claim 16, Mohajer in view of Juang teaches the charger of claim 1, wherein the parameter (d) is constrained to a predefined bounded parameter interval throughout the optimization (The nature of optimization through stochastic gradient descent as taught by Juang and evidence by Brownlee involves starting with a first try value where subsequent try values are in effect bounded by the optimization process to be within a parameter interval since parameters resulting in greater loss reduce optimization).
Furthermore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the instant application to constrain any first try value for the parameter (d) to a predefined bounded parameter interval throughout the optimization, since it has been held that choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success is obvious. KSR International Co. v Teleflex Inc., 550 U.S. 398, 127 S. Ct. 1727, 82 USPQ2d 1385, 1395-97 (2007).)
Regarding independent claim 11, Mohajer teaches a method for charging an electrical energy store (Fig. 6: battery) by means of a charger having an open-loop control unit (Ich trajectories and Jsr trajectories) and a closed-loop control unit (controller C(s)),
wherein the charger is configured to charge the electrical energy store to a defined state of charge within a preset charging time and to set a charging current (ICh,traj) and a side reaction current (Jsr,traj) of the electrical energy store (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature),
Regarding claim 18, Mohajer in view of Juang teaches the charger of claim 16, wherein the optimization deterministically yields the same value of the parameter (d) for identical values of input parameters used to define the charging profile (p. 98, col. 1, par. 1-2, Sec. 2; Fig. 2; and Fig. 6: plant model current trajectories, Ich trajectories and Jsr trajectories, are based on optimized charging profiles over a set period of time that include side reaction current data based on battery SOC, aging, and temperature. The examiner interprets that any comparison with the model and values measured in real-time would deterministically yield the same parameter for use of a parameter (d) for a loss function).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Mohajer in view of Juang as evidenced by Brownlee and by Price (“Guide: Your EV’s Battery vs. Heat” Price Family Dealerships https://www.pricefamilydealerships.com/guide-your-evs-battery-vs-heat/ > Retrieved from Online 5/13/26).
Regarding claim 17, Mohajer in view of Juang teaches the charger of claim 16.
Mohajer does not explicitly teach wherein the loss function f(d) has a unimodal shape over the bounded parameter interval.
However, it is a common occurrence through the optimization of parameters in a system for a loss function to have a parabolic shape with a global minimum, depending on how the parameters are prioritized. For example, if charging were optimized based on measured heat of the battery, it is known that battery efficiency is lost when either it is too hot or cold, and therefore has a minimum loss of efficiency bounded by two values of heat, as evidenced by Price.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GeeksforGeeks (“ML – Stochastic Gradient Descent (SGD)” < https://www.geeksforgeeks.org/machine-learning/ml-stochastic-gradient-descent-sgd/ > Published online 9/30/2025)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryu-Sung Peer Weinmann whose telephone number is (703)756-5964. The examiner can normally be reached Monday-Friday 9am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman, can be reached at (571) 272-2147. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or (571) 272-1000.
/Ryu-Sung P. Weinmann/Examiner, Art Unit 2859 May 14, 2026
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859
Prosecution Insights