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 .
Examiner Note
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Objections
Claim 10 is objected to because of the following informalities: typo “one or mor” (change to “one or more”). 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 19-20 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. The limitations including “permutation sets” do not have support in the specification with details to understand how they are being utilized. The term “permutation sets” only appear in [0022] and [0023], which merely recite the language of claims 19-20.
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 19-20 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.
In claim 19, the structure of “permutation sets” and the relationship to “parameter values set” is indefinite. The “generating” of permutation sets is indefinite. The kind of permutation (full, partial, with repetition, multiset permutation etc.), generation method and quantity (complete, incomplete, etc.), and the treatment of the source data (values of the “parameter values set” being utilized) all change the underlying algorithm in significant ways. Additionally, it is unclear whether “permutation sets” is referring to reordering of values, as discussed above, or Monte Carlo permutation (random sampling from a subset of parameter arrangements), which one of ordinary skill in the art of parameter optimization techniques would consider relevant.
For the purpose of compact prosecution, claims 19-20 will be interpreted as implementing Monte Carlo permutation, with “permutation sets” being randomly sampled from a subset of possible parameter value configurations.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claim(s) recite(s) mental processes and mathematical concepts.
Step 1: Statutory Category:
Claims 1-9 are directed to a process. Claims 10-18 are directed to a machine. Claims 19-20 are directed to a process.
Step 2A Prong I: Recited Judicial Exception:
Regarding claim 1, the following limitations which contain abstract ideas are bolded:
A method of determining an optimized value set of parameters used for a target operation of a battery, (mental process)
the method comprising: setting, as a default value set, a first value set comprising first values of the respective parameters; (mental process)
acquiring a first performance indicator by performing a simulation of the target operation according to the first value set; (mathematical concept & mental process)
generating candidate second value sets, each candidate second value set having at least one value in the first value set changed based on the first value set; (mental process)
acquiring second performance indicators respectively corresponding to the candidate second value sets by performing simulations of the target operation according to the respective candidate second value sets; (mathematical concept & mental process)
selecting one of the candidate second value sets as a second value set based on the second performance indicators; (mental process)
determining whether the second performance indicator corresponding to the second value set satisfies a target performance condition of the battery; (mental process)
and in response to determining that the second performance indicator satisfies the target performance condition of the battery, using the second value set as the optimized value set of the parameters. (mental process)
A person can perform the mental process of “setting” a set of values as a mental judgement. A person can perform the mental process of varying the values in order to create a second set of values. A person can perform the mental process of comparing indicators and selecting a set of values based on the indicators as an observation and judgement.
The process of performing a simulation of a target operation of a battery, given broadest reasonable interpretation, constitutes mathematical concepts and mental process, as equations can be used to model internal parameters of the battery, which a person can, given a pen and paper, calculate and track as a mental process. Given broadest reasonable interpretation, “performing simulations” can include execution of models containing a single mathematical equation (abstract mathematical concept), or include a model discretized over time and/or space, which includes mathematical equations (abstract mathematical concept) and the mental process of tracking (abstract mental observation) and calculating (abstract mental evaluation) values to perform the simulation iteratively.
Step 2A Prong II: Integration into Practical Application:
This judicial exception is not integrated into a practical application because Claim 1 does not contain any additional elements beyond the judicial exception.
Step 2B: Significantly More:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim recites no elements beyond the judicial exception. Therefore, claim 1 is ineligible under 35 U.S.C 101.
Regarding Claim 2, the claim recites the following limitations:
The method of claim 1, wherein the acquiring of the first performance indicator comprises performing the simulation of the target operation by applying the first value set to a battery model representing an internal state of the battery.
“acquiring of the first performance indicator” constitutes the mental process of observing the result of the performed simulation.
The process of performing a simulation of a target operation of a battery, given broadest reasonable interpretation, constitutes mathematical concepts and mental process, as equations can be used to model internal parameters of the battery, which a person can, given a pen and paper, calculate and track as a mental process. Given broadest reasonable interpretation, “performing simulations” can include execution of models containing a single mathematical equation (abstract mathematical concept), or include a model discretized over time and/or space, which includes mathematical equations (abstract mathematical concept) and the mental process of tracking (abstract mental observation) and calculating (abstract mental evaluation) values to perform the simulation iteratively.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The claim does not recite additional elements beyond the judicial exception. Therefore, claim 2 is ineligible under 35 U.S.C 101.
Regarding Claim 3, the claim recites the following limitation:
The method of claim 1, wherein the target operation is an operation of charging the battery.
The additional limitation constitutes generally linking to a field of use, see MPEP § 2106.05(h). The claim limitation merely links the target operation of a simulation to the field of use of simulating “charging the battery”.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 3 is ineligible under 35 U.S.C 101.
Claim 11 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 4, the claim recites the following limitation:
The method of claim 3, wherein the target performance condition comprises any one or any combination of a charging capacity of the battery or a charging speed of the battery.
The additional limitation constitutes generally linking to a field of use, see MPEP § 2106.05(h). The claim limitation merely links the target parameter optimization of a simulation to the field of use of measuring “charging capacity” or “charging speed”.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 4 is ineligible under 35 U.S.C 101.
Claim 12 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 5, the claim recites the following limitation:
The method of claim 1, wherein the generating of the candidate second value sets comprises: generating a first second candidate value set by changing a first value of a first of the parameters to a second value based on a preset variation for the first parameter; and generating a second second candidate value set by changing a first value of a second of the parameters to a second value based on a preset variation for the second parameter.
The additional limitations constitute additional metal processes. A person can perform the mental process of changing parameters according to preset variations.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 5 is ineligible under 35 U.S.C 101.
Claim 13 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 6, the claim recites the following limitations:
The method of claim 1, wherein the generating of the candidate second value sets comprises: determining a variation of the first value of the first parameter based on a first lookup table (LUT) preset for the first parameter; generating a first second candidate value set by changing the first value of the first parameter to a second value based on the variation of the first value of the first parameter; determining a variation of the first value of the second parameter based on a second LUT preset for the second parameter among the parameters; and generating a second second candidate value set by changing the first value of the second parameter to a second value based on the variation of the first value of the second parameter.
The additional limitations constitute additional metal processes. A person can perform the mental process of changing parameters according to preset variations. A person can perform the mental process of observing and utilizing a lookup table.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 6 is ineligible under 35 U.S.C 101.
Claim 15 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 7, the claim recites the following limitations:
The method of claim 1, wherein the generating of the candidate second value sets comprises: determining a variation of the first value of the first parameter based on a first function preset for the first parameter; generating a first second candidate value set by changing the first value of the first parameter to a second value based on the variation of the first value of the first parameter; determining a variation of the first value of the second parameter based on a second function preset for the second parameter among the parameters; and generating a second second candidate value set by changing the first value of the second parameter to a second value based on the variation of the first value of the second parameter.
The additional limitations constitute additional metal processes. A person can perform the mental process of changing parameters according to preset variations. A person can perform the mental process of computing a function to determine changes of parameters.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 7 is ineligible under 35 U.S.C 101.
Claim 16 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 8, the claim recites the following limitations:
The method of claim 1, wherein the acquiring of the first performance indicator comprises acquiring the first performance indicator based on a result value of a first factor of the simulation, a first weight of the first factor, a result value of a second factor of the simulation, and a second weight of the second factor.
The additional limitation is directed to “acquiring” the performance indicator, which amounts to the abstract mental process of observing the results of the simulation.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 8 is ineligible under 35 U.S.C 101.
Claim 17 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding Claim 9, the claim recites the following limitations:
The method of claim 1, further comprising, in response to the second performance indicator not satisfying the target performance condition, setting the second value set as a new default set of the parameters.
A person can perform the mental process of responding to the state of a performance indicator and set the values of parameters.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 9 is ineligible under 35 U.S.C 101.
Regarding Claim 10, the claim recites the following limitations:
An electronic device for determining an optimized value set of parameters used for a target operation of a battery,
comprising: one or more processors;
and a memory storing instructions configured to cause the one or mor processors to: access simulation data for preset charging currents
that is based on a battery model representing an internal state of the battery;
set a first value set as a default value set of the parameters;
acquire a first performance indicator by performing a simulation of the target operation of the battery based on the first value set and the simulation data;
generate a candidate second value sets in which at least one value in the first value set is changed based on the first value set;
acquire candidate second performance indicators by performing simulations of the target operation of the battery based on the respective candidate second value sets and the simulation data;
determine one of the candidate second value sets as a second value set of the parameters based on the candidate second performance indicators;
determine whether the second performance indicator for the second value set of the parameters corresponds to a preset target performance indicator;
and in response to the second performance indicator corresponding to the target performance indicator, determine the second value set to be the optimized value set of the parameters.
A person can perform the mental process of “setting” a set of values as a mental judgement. A person can perform the mental process of varying the values in order to create a second set of values. A person can perform the mental process of comparing indicators and selecting a set of values based on the indicators as an observation and judgement.
The process of performing a simulation of a target operation of a battery, given broadest reasonable interpretation, constitutes mathematical concepts and mental process, as equations can be used to model internal parameters of the battery, which a person can, given a pen and paper, calculate and track as a mental process. Given broadest reasonable interpretation, “performing simulations” can include execution of models containing a single mathematical equation (abstract mathematical concept), or include a model discretized over time and/or space, which includes mathematical equations (abstract mathematical concept) and the mental process of tracking (abstract mental observation) and calculating (abstract mental evaluation) values to perform the simulation iteratively.
Claim 10 contains the additional limitations beyond the judicial exception:
An electronic device (mere instructions to apply an exception with generic computing components, see MPEP § 2106.05(f))
for determining an optimized value set of parameters used for a target operation of a battery, (general field of use, see MPEP § 2106.05(h))
comprising: one or more processors; (mere instructions to apply an exception with generic computing components, see MPEP § 2106.05(f))
and a memory storing instructions configured to cause the one or mor processors to: (mere instructions to apply an exception with generic computing components, see MPEP § 2106.05(f))
access simulation data (insignificant extra-solution activity, mere data gathering, see MPEP § 2106.05(g))
for preset charging currents that is based on a battery model representing an internal state of the battery; (general field of use, see MPEP § 2106.05(h))
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 10 is ineligible under 35 U.S.C 101.
Regarding Claim 14, the claim recites the following limitation which amounts to merely linking to a field of use: The electronic device of claim 13, wherein the preset variation for the first parameter and the preset variation for the second parameter comprise different respective values. (general field of use, see MPEP § 2106.05(h))
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 10 is ineligible under 35 U.S.C 101.
Regarding Claim 18, the claim recites the following limitations:
The electronic device of claim 10, wherein the instructions are further configured to cause the one or more processors to: in response to the second performance indicator not corresponding to the target performance indicator, set the second value set as the default set of the parameters, and generate new candidate second value sets in which at least one value in the first value set is changed based on the first value set.
A person can perform the mental process of responding to the state of a performance indicator and set and/or change the values of parameters.
Claim 18 contains the following additional limitation:
wherein the instructions are further configured to cause the one or more processors to: (mere instructions to apply an exception with generic computing components, see MPEP § 2106.05(f))
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 18 is ineligible under 35 U.S.C 101.
Regarding Claim 19, the claim recites the following limitations:
A method comprising: forming a first battery parameter values set;
generating first permutation sets comprising respective permutations of the first battery parameter values set;
obtaining first performance indications generated by respective simulations of an internal state of a battery based on the respective first permutation sets;
based on the first performance indications, determining that none of the first permutation sets satisfy a performance condition of a charging operation of the battery, and based thereon, generating second permutations sets comprising respective permutations of one of the first permutation sets;
obtaining second performance indications generated by respective simulations of the internal state of the battery based on the respective second permutation sets;
and selecting one of the second permutation sets based on its corresponding second performance indication, and
using the selected second permutation set to control a charging operation of the battery.
A person can perform the mental process of choosing parameter values, observe and make judgements about the results of a simulation, and update and change parameter values.
Claim 19 contains the following additional limitation:
using the selected second permutation set to control a charging operation of the battery.
This limitation constitutes insignificant extra-solution activity, insignificant application. MPEP § 2106.05(g) outlines 3 considerations:
(1) Whether the extra-solution limitation is well known.
Applying the results of a simulation to the real target of the model is well known.
(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).
The invention is directed to optimizing a set of parameters in simulation, and applying the results of the parameters is tangential to this application.
(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).
None of the recited judicial exceptions require the outputting of the “permutation set”.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 19 is ineligible under 35 U.S.C 101.
Regarding Claim 20, the claim recites the following limitations:
The method of claim 19, wherein the one of the second permutation sets is selected based on having the highest corresponding second performance indication.
A person can perform the mental process of evaluating/comparing performance indications. A person can perform the mental process of selecting a permutation set.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 20 is ineligible under 35 U.S.C 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7, 9-12, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (J. Electrochem. Soc. 167, 2020).
Regarding claim 1, Gao teaches the following limitations:
A method of determining an optimized value set of parameters used for a target operation of a battery, (Gao Abstract, “We introduce a bio-inspired electrolyte channel design into thick electrodes to improve the cell performance, especially under fast charging conditions … Integrating machine learning with the Markov chain Monte Carlo gradient descent optimization, we demonstrate that the complicated multivariable channel geometry optimization problem can be efficiently solved.”)
the method comprising: setting, as a default value set, a first value set comprising first values of the respective parameters; (Gao Fig. 16, “Randomly generate N groups of input parameters”)
acquiring a first performance indicator (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i, … Inn,k) by trained DNN function”)
generating candidate second value sets, each candidate second value set having at least one value in the first value set changed based on the first value set; (Gao Fig. 16, “Adjust input parameter…”)
acquiring second performance indicators respectively corresponding to the candidate second value sets (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i (1+ε) … Inn,k) by trained DNN function”)
selecting one of the candidate second value sets as a second value set based on the second performance indicators; (Gao Fig. 16, “Calculate the SDR for the SE values among the N groups”)
determining whether the second performance indicator corresponding to the second value set satisfies a target performance condition of the battery; (Gao Fig. 16, “if SDR < 10-2”) (Gao “Deep Artificial Neural Network Aided Design”, “If the SDR value is smaller than 10-2, the maximum SE value within these N groups of SE values is regarded as the global optimized SE value“)
and in response to determining that the second performance indicator satisfies the target performance condition of the battery, using the second value set as the optimized value set of the parameters. (Gao Fig. 16, “Find the largest SE value and the corresponding input parameters”)
The embodiment of Gao does not explicitly teach every aspect of: “by performing a simulation of the target operation according to the first value set;” and “by performing simulations of the target operation according to the respective candidate second value sets;” within the context of parameter optimization.
However, Gao does teach:
by performing a simulation of the target operation according to the first value set (Gao pg. 6, “To investigate the effect of channel length on battery performance, two groups of simulations were performed.”)
by performing simulations of the target operation (Gao pg. 6, “To investigate the effect of channel length on battery performance, two groups of simulations were performed.”)
according to the respective candidate second value sets; (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”) Gao trains a DNN (Deep Neural Network) based on a FEM simulation (Gao “Deep Artificial Neural Network Aided Design”, “For the DNN training process, we generated 20000 groups of dataset based on FEM of the equations in the section “Modelling” using COMSOL.”). The FEM Model of Gao teaches the limitations of “performing a simulation”, and the “Markov chain Monte Carlo method” (Gao Introduction, “A Markov chain Monte Carlo method is used together with DNN to find the Ragone planes and the optimized channel designs.”) teaches “candidate second value sets” as it is a stochastic method (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Regarding claim 2, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, wherein the acquiring of the first performance indicator (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i (1+ε) … Inn,k) by trained DNN function) SE (Specific Energy) is being interpreted as the performance indicator.
comprises performing the simulation of the target operation (Gao pg. 6, “To investigate the effect of channel length on battery performance, two groups of simulations were performed.”)
by applying the first value set to a battery model representing an internal state of the battery. (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i (1+ε) … Inn,k) by trained DNN function) Broadest reasonable interpretation of “a battery model representing an internal state of the battery” includes the DNN of Gao.
The embodiment of Gao does not explicitly teach every aspect of: comprises performing the simulation of the target operation
However, Gao does teach:
comprises performing the simulation of the target operation (Gao pg. 6, “To investigate the effect of channel length on battery performance, two groups of simulations were performed.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Regarding claim 3, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, wherein the target operation is an operation of charging the battery. (Gao pg. 5, “Simulation conditions.—In our simulations the battery was first fast charged by the constant current, constant voltage (CCCV) strategy, and then discharged until the terminal voltage decreased to the cut-off voltage.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Claim 11 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding claim 4, in addition to the limitations of claim 3, Gao teaches:
The method of claim 3, wherein the target performance condition comprises any one or any combination of a charging capacity of the battery or a charging speed of the battery. (Gao pg. 6, “Quantification of electrochemical performance and mechanical stress.—The specific energy, specific power and specific capacity during discharging are chosen to quantify the battery performance.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Claim 12 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding claim 7, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, wherein the generating of the candidate second value sets comprises: determining a variation of the first value of the first parameter based on a first function preset for the first parameter; (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”) Broadest reasonable interpretation of “function preset” includes computer functions, and Markov Chain Monte Carlo (MCMC), which is the sampling optimization technique used by Gao.
generating a first second candidate value set by changing the first value of the first parameter to a second value based on the variation of the first value of the first parameter; (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”) The figure teaches looping in order to iterate through input parameters in a set.
determining a variation of the first value of the second parameter based on a second function preset for the second parameter among the parameters; (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”) Broadest reasonable interpretation of “function preset” includes computer functions, and Markov Chain Monte Carlo (MCMC), which is the sampling optimization technique used by Gao.
and generating a second second candidate value set by changing the first value of the second parameter to a second value based on the variation of the first value of the second parameter. (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”) The figure teaches looping in order to iterate through input parameters in a set.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Claim 16 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding claim 9, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, further comprising, in response to the second performance indicator not satisfying the target performance condition, setting the second value set as a new default set of the parameters. (Gao Fig. 16, “if Gradn < 10-4”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Regarding claim 10, Gao teaches:
An electronic device for determining an optimized value set of parameters used for a target operation of a battery, (Gao Abstract, “We introduce a bio-inspired electrolyte channel design into thick electrodes to improve the cell performance, especially under fast charging conditions … Integrating machine learning with the Markov chain Monte Carlo gradient descent optimization, we demonstrate that the complicated multivariable channel geometry optimization problem can be efficiently solved.”)
comprising: one or more processors; and a memory storing instructions configured to cause the one or mor processors to: (Gao pg. 4, “using COMSOL Multiphysics 5.4”) (Gao pg. 13, “machine learning toolbox in MATLAB”) Software “COMSOL” and “MATLAB” necessitate hardware structure.
set a first value set as a default value set of the parameters; (Gao Fig. 16, “Randomly generate N groups of input parameters”)
acquire a first performance indicator (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i, … Inn,k) by trained DNN function”)
generate a candidate second value sets in which at least one value in the first value set is changed based on the first value set; (Gao Fig. 16, “Adjust input parameter…”)
acquire candidate second performance indicators (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i (1+ε) … Inn,k) by trained DNN function”)
determine one of the candidate second value sets as a second value set of the parameters based on the candidate second performance indicators; (Gao Fig. 16, “Calculate SE(Inn,1, … , Inn,i (1+ε) … Inn,k) by trained DNN function”)
determine whether the second performance indicator for the second value set of the parameters corresponds to a preset target performance indicator; (Gao Fig. 16, “if SDR < 10-2”) (Gao “Deep Artificial Neural Network Aided Design”, “If the SDR value is smaller than 10-2, the maximum SE value within these N groups of SE values is regarded as the global optimized SE value“)
and in response to the second performance indicator corresponding to the target performance indicator, determine the second value set to be the optimized value set of the parameters. (Gao Fig. 16, “Find the largest SE value and the corresponding input parameters”)
The embodiment of Gao does not explicitly teach every aspect of: “access simulation data for preset charging currents that is based on a battery model representing an internal state of the battery;”, “by performing a simulation of the target operation of the battery based on the first value set and the simulation data;”, “by performing simulations of the target operation of the battery based on the respective candidate second value sets and the simulation data;”
However, Gao does teach:
access simulation data for preset charging currents that is based on a battery model representing an internal state of the battery; (Gao pg. 13, “For the DNN training process, we generated 20000 groups of dataset based on FEM of the equations…”)
by performing a simulation of the target operation of the battery (Gao, Modelling Section)
based on the first value set and the simulation data; (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”) Gao trains a DNN (Deep Neural Network) based on a FEM simulation (Gao “Deep Artificial Neural Network Aided Design”, “For the DNN training process, we generated 20000 groups of dataset based on FEM of the equations in the section “Modelling” using COMSOL.”). The FEM Model of Gao teaches the limitations of “performing a simulation”, and the “Markov chain Monte Carlo method” (Gao Introduction, “A Markov chain Monte Carlo method is used together with DNN to find the Ragone planes and the optimized channel designs.”) teaches “candidate second value sets” as it is a stochastic method (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”). The DNN is trained on simulation data and thus teaches “based on … simulation data”.
by performing simulations of the target operation of the battery (Gao, Modelling Section)
based on the respective candidate second value sets and the simulation data; (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”) Gao trains a DNN (Deep Neural Network) based on a FEM simulation (Gao “Deep Artificial Neural Network Aided Design”, “For the DNN training process, we generated 20000 groups of dataset based on FEM of the equations in the section “Modelling” using COMSOL.”). The FEM Model of Gao teaches the limitations of “performing a simulation”, and the “Markov chain Monte Carlo method” (Gao Introduction, “A Markov chain Monte Carlo method is used together with DNN to find the Ragone planes and the optimized channel designs.”) teaches “candidate second value sets” as it is a stochastic method (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within a range of the candidate pool”). The DNN is trained on simulation data and thus teaches “based on … simulation data”.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Regarding claim 18, in addition to the limitations of claim 1, Gao teaches:
The electronic device of claim 10, wherein the instructions are further configured to cause the one or more processors to: in response to the second performance indicator not corresponding to the target performance indicator, set the second value set as the default set of the parameters, (Gao Fig. 16, “if SDR < 10-2”)
and generate new candidate second value sets in which at least one value in the first value set is changed based on the first value set. (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within the range of the candidate pool”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the parameter optimization process of Gao with the simulation model of Gao. The motivation/suggestion is that Gao acknowledges the method of using the FEM simulation for parameter optimization, but uses the DNN instead because it is more efficient (Gao Introduction, “Electrochemical models21–24 can be used to calculate the battery characteristics for any given sets of channel geometry by numerical approaches such as the finite element method (FEM). However, conducting parametric optimization using FEM is challenging due to the huge computational cost associated with the highly non-linear electrochemical model.25 It is critical to find a method to effectively connect the geometrical parameters of an electrolyte channel to the battery cell performance, to be able to effectively conduct a parametric optimal design.”). The DNN is designed such that it is used as an efficient replacement for a simulation model, with the same inputs and outputs, thus it would have been obvious to use the simulation model within the parameter optimization process instead.
Claim(s) 5-6, 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. as applied to claims 1-4, 7, 9-12, 16, 18 above, and further in view of Sobol (Mathematics and Computers in Simulation Vol. 47, 1998).
Regarding claim 5, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, wherein the generating of the candidate second value sets comprises: generating a first second candidate value set by changing a first value of a first of the parameters to a second value (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”, “Calculate SE(Inn,1, … , Inn,i(1+ε), … Inn,k)”)
and generating a second second candidate value set by changing a first value of a second of the parameters to a second value (Gao Fig. 16, “Adjust input parameter to Inn,i(1+ε)”, “Calculate SE(Inn,1, … , Inn,i(1+ε), … Inn,k)”) The parameter adjustment step is repeated (Gao Fig. 16, “if Gradn < 10-4”) and a “second of the parameters” would be a different “i” value for “Inn,i”.
The limitations not taught by Gao are taught by Sobol:
based on a preset variation for the first parameter; (Sobol 4.1, “…in the quasi-monte Carlo algorithm all the numbers for a trial are produced simultaneously.”) The “preset variation” given broadest reasonable interpretation includes a quasi-Monte Carlo algorithm, which produces sample points before a trial based on a known distribution, such as for a specific set of battery configurations.
based on a preset variation for the second parameter. (Sobol 4.1, “…in the quasi-monte Carlo algorithm all the numbers for a trial are produced simultaneously.”)
Gao et al. and Sobol are analogous art because they are in the same field of endeavor: Computer Simulation using Monte-Carlo methods for optimization. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the battery parameter optimization system of Gao with the quasi-Monte Carlo algorithm of Sobol. The motivation/suggestion would be to improve the efficiency of the algorithm (Sobol 4.4, “The main advantage of the quasi-Monte Carlo approach is a possible speed-up of the convergence.”).
Claim 13 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding claim 6, in addition to the limitations of claim 1, Gao teaches:
The method of claim 1, wherein the generating of the candidate second value sets comprises: determining a variation of the first value of the first parameter based on a first lookup table (LUT) preset for the first parameter;
generating a first second candidate value set by changing the first value of the first parameter to a second value based on the variation of the first value of the first parameter;
determining a variation of the first value of the second parameter based on a second LUT preset for the second parameter among the parameters;
and generating a second second candidate value set by changing the first value of the second parameter to a second value based on the variation of the first value of the second parameter.
Claim 15 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Regarding claim 14, in addition to the limitations of claim 13 taught by Gao and Sobol, Sobol teaches: The electronic device of claim 13, wherein the preset variation for the first parameter and the preset variation for the second parameter comprise different respective values. (Sobol Section 3, “uniformly distributed…”) Sobol describes quasi-Monte Carlo algorithms as using uniformly distributed point sampling, which necessarily comprise “different respective values”.
Claim(s) 8, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. as applied to claims 1-4, 7-20 above, and further in view of Cannella et al (2021).
Regarding claim 8, Gao teaches:
The method of claim 1, wherein the acquiring of the first performance indicator comprises acquiring the first performance indicator based on a result value of a first factor of the simulation, (Gao pg. 15, “The trained DNN function was utilized to calculate SE, SP, and SC … The maximum SE, SP, and SC value was found by the MCMC gradient descent method”) Gao defines three performance indicators that can be optimized over.
a result value of a second factor of the simulation, (Gao pg. 15, “The trained DNN function was utilized to calculate SE, SP, and SC … The maximum SE, SP, and SC value was found by the MCMC gradient descent method”)
and a second weight of the second factor.
The limitations not taught by Gao are taught by Cannella et al.:
a first weight of the first factor, (Cannella Section 5, “the positive weighted combination of proper and representation invariant objective functions is itself a proper and representation invariant objective function. This allows us to construct potential approximations of L* by the combination of simpler objective functions as weighted by hyperparameter coefficients.) The “first factor” and “second factor” are taught by the “objective functions” of Cannella. The “first weight” and “second weight” are taught by the “hyperparameter coefficients”.
and a second weight of the second factor. (Cannella Section 5, “the positive weighted combination of proper and representation invariant objective functions is itself a proper and representation invariant objective function. This allows us to construct potential approximations of L* by the combination of simpler objective functions as weighted by hyperparameter coefficients.)
Gao et al. and Cannella are analogous art because they are in the same field of endeavor: MCMC parameter optimization algorithms. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the battery parameter optimization system of Gao with the objective function weighted mixing of Cannella. The motivation/suggestion would be to improve the efficiency of MCMC performance (Cannella Section 1, “Ideally, we would like to develop a practical method for optimizing MCMC performance over an arbitrarily parameterized class of proposal distributions.”).
Claim 17 contains significantly similar limitations to claim 3 and is rejected for the same reasons.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al., and further in view of Berry et al (Permutation Statistical Methods, 2018) and Xia et al. (PGPUB US2002/0120906 A1).
Regarding claim 19, Gao teaches:
A method comprising: forming a first battery parameter values set; (Gao Fig. 16, “Find the top 10% SE values and corresponding optimized input parameters”, “Regenerate N groups of random input parameters with each input parameter within the range of the candidate pool”) The “parameter value set” is taught by the “candidate pool” of Gao.
obtaining first performance indications generated by respective simulations of an internal state of a battery (Gao Fig. 16, “Calculate SE…”)
based on the first performance indications, determining that none of the first permutation sets satisfy a performance condition of a charging operation of the battery, (Gao Fig. 16, “if SDR < 10-2”) This limitation is taught by the “No” path. The “permutation sets” are taught by the random permutation sampling of Berry.
and based thereon, generating second permutations sets comprising respective permutations of one of the first permutation sets; (Gao Fig. 16, “Regenerate N groups of random input parameters with each input parameter within the range of the candidate pool”) The “permutation sets” are taught by the random permutation sampling of Berry.
obtaining second performance indications generated by respective simulations of the internal state of the battery based on the respective second permutation sets; (Gao Fig. 16, “Calculate SE…”) The “permutation sets” are taught by the random permutation sampling of Berry.
and selecting one of the second permutation sets based on its corresponding second performance indication, (Gao Fig. 16, “if SDR < 10-2”) This limitation is taught by the “Yes” path. The “permutation sets” are taught by the random permutation sampling of Berry.
The limitations not taught by Gao are taught by Berry:
generating first permutation sets comprising respective permutations of the first battery parameter values set; (Berry 2.2.2, “Resampling-approximation (hereafter, resampling) permutation tests generate and examine a Monte Carlo random subset of all possible, equally-likely arrangements of the observed response measurements. For each randomly selected arrangement of the observed data, the desired test statistic is calculated.”) The random permutation sampling of Berry is being combined with the parameter optimization of Gao such that “Regenerating random input parameters” of Gao (based on the “candidate pool”) is substituted with the random permutation sampling of Berry.
based on the respective first permutation sets (Berry 2.2.2, “Resampling-approximation (hereafter, resampling) permutation tests generate and examine a Monte Carlo random subset of all possible, equally-likely arrangements of the observed response measurements. For each randomly selected arrangement of the observed data, the desired test statistic is calculated.”)
The limitations not taught by Gao in view of Berry are taught by Xia:
and using the selected second permutation set to control a charging operation of the battery. (Xia [0432], “Knowledge about the dynamic behavior obtained from the small-signal model analysis of the fuel cell can be used in the control system design.”)
Gao et al., Berry et al., and Xia et al. are analogous art because they are in the same field of endeavor: Simulation and Parameter Optimization. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the battery parameter optimization process of Gao with the permutation sampling of Berry and the battery control (based on battery modeling and design optimization) of Xia. The motivation would be to improve the operation/application of a battery based on the optimized battery model (Xia [0007], “The second research area is in the application of a device, where the goal is to understand the performance characteristics of the device and design a better solution for an application. The quality of the solution can be defined in many different ways depending on the requirements of specific applications. For example, faster charging time, higher instant power output, longer device life, and accurate estimate of the state of charge of a battery, are all considered to be desirable features of an application.”), and improve the performance of the Monte Carlo parameter optimization (Berry 2.2.2, “Presently, Monte Carlo resampling permutation tests are the method of choice for most researchers, with exact permutation tests reserved for smaller data sets. There are three notable advantages to resampling permutation tests. First, resampling permutation tests are highly efficient given the ready availability of high-speed computers and the recent development of rapid pseudorandom number generators such as the Mersenne Twister, on which resampling permutation tests are highly dependent.7 Second, in some applications a resampling permutation test is much more efficient than an exact permutation test, even for small samples. For example, in the permutation analysis of contingency tables an exact permutation test must necessarily calculate a hypergeometric point probability value for each of, potentially, thousands of cell frequency arrangements, while a resampling permutation test need only count the number of cell arrangements as extreme or more extreme than the observed cell arrangement. Third, algorithms for exact permutation tests are non-existent or completely impractical for analyzing certain problems, such as multi-way contingency tables, while an efficient resampling algorithm is presently available for multi-way tables”).
Regarding claim 20, in addition to the limitations taught by Gao in view of Berry and Xia, Berry teaches:
The method of claim 19, wherein the one of the second permutation sets is selected (Berry 2.2.2, “Resampling-approximation (hereafter, resampling) permutation tests generate and examine a Monte Carlo random subset of all possible, equally-likely arrangements of the observed response measurements. For each randomly selected arrangement of the observed data, the desired test statistic is calculated.”)
Limitations not taught by Berry are taught by Gao:
based on having the highest corresponding second performance indication. (Gao Fig. 16, “Find the largest SE value and the corresponding input parameters”)
Gao et al., Berry et al., and Xia et al. are analogous art because they are in the same field of endeavor: Simulation and Parameter Optimization. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the battery parameter optimization process of Gao with the permutation sampling of Berry and the battery control (based on battery modeling and design optimization) of Xia. The motivation would be to improve the operation/application of a battery based on the optimized battery model (Xia [0007], “The second research area is in the application of a device, where the goal is to understand the performance characteristics of the device and design a better solution for an application. The quality of the solution can be defined in many different ways depending on the requirements of specific applications. For example, faster charging time, higher instant power output, longer device life, and accurate estimate of the state of charge of a battery, are all considered to be desirable features of an application.”), and improve the performance of the Monte Carlo parameter optimization (Berry 2.2.2, “Presently, Monte Carlo resampling permutation tests are the method of choice for most researchers, with exact permutation tests reserved for smaller data sets. There are three notable advantages to resampling permutation tests. First, resampling permutation tests are highly efficient given the ready availability of high-speed computers and the recent development of rapid pseudorandom number generators such as the Mersenne Twister, on which resampling permutation tests are highly dependent.7 Second, in some applications a resampling permutation test is much more efficient than an exact permutation test, even for small samples. For example, in the permutation analysis of contingency tables an exact permutation test must necessarily calculate a hypergeometric point probability value for each of, potentially, thousands of cell frequency arrangements, while a resampling permutation test need only count the number of cell arrangements as extreme or more extreme than the observed cell arrangement. Third, algorithms for exact permutation tests are non-existent or completely impractical for analyzing certain problems, such as multi-way contingency tables, while an efficient resampling algorithm is presently available for multi-way tables”).
Conclusion
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/HENRY JOYNER GOLD/ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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