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 .
Response to Amendment
This Office Action has been issued in response to amendment filed 07/28/2025. Applicant's arguments have been carefully and fully considered; and they are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made. Accordingly, this action has been made FINAL.
Claim Status
Claims 12 and 20-21 have been amended. Claims 12, 18, and 20-21 remain pending and are ready for examination.
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 12, 18, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03.
The claim is to a method, i.e. one of the statutory categories.
Step 2A prong one: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(11) and the October 2019 Update, a claim "recites" a judicial exception when the judicial exception is "set forth" or "described" in the claim.
The claim recites:
“…determining a state of health of an electrical energy store,
detecting… an operating state of the electrical energy store;
selecting… as a function of the detected operating state, at least two models and/or measuring processes for determining the state of health of the electrical energy store from a plurality of models and/or measuring processes, the selecting including …to evaluate (i) the detected operating state, (ii) the plurality of models and/or measuring processes, and (iii) states of health of a plurality of electrical energy stores in order… to associate with the detected operating state those of the models and/or measuring processes having a greatest accuracy for determining the state of health of the state of health of the electrical store for the detected operating state relative to an accuracy of others of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state,
detecting… data for determining the state of health of the electrical energy store using a first model and/or measuring process of the selected at least two models and/or measuring processes;
determining… the state of health of the electrical energy store using the first model and/or measuring process;
correcting, using the state of health determined using the first model and/or measuring process, a parameter of a further model and/or measuring process of the selected at least two models and/or measuring processes;
detecting… data for determining the state of health of the electrical energy store using the further model and/or measuring process with the corrected parameter;
determining… the state of health of the electrical energy store using the further model and/or measuring process with the corrected parameter;
evaluating… values for the state of health of the electrical energy store, determined using the first model and/or measuring process and the further model and/or measuring process, taking into account the detected operating state with the corrected parameter;
predicting, using a model whose parameters have been corrected using the values for the state of health determined using the first model and/or measuring process and the further model and/or measuring process with the corrected parameter, a future course of the state of health of the electrical energy store;
using an electrical equivalent circuit diagram for the electrical energy store with current integration for determining charge quantity…”
These limitations recite concepts that can be practically performed in the human mind but for the recitation of generic computer components. Thus, the limitations fall into the “Mental Processes” grouping of abstract ideas. (Step 2A prong one: YES).
Step 2A prong two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section lll{A){2), 84 Fed. Reg. at 54-55.
This judicial exception is not integrated into a practical application because: Besides the abstract idea, the claim recites the additional limitations of:
…via the processor…
… using machine learning…
…for the machine learning…
…as evaluated the machine learning;
outputting… a value of the values of the state of health having a precise determination of the state of health accuracy by reducing a scattering of the values of the state of health;
wherein the selected at least two models and/or measuring processes being those of the models and/or measuring processes having the greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to the accuracy of the other of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state,
and optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store;
“wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include at least one physical model, for stationary operation… and an electrochemical model for dynamic operation.”
The processor, the machine learning, the two models, the measuring processes, the physical model, and the electrochemical model are a recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Thus, these limitations represent no more than mere instructions to apply the judicial exceptions on a computer.
The limitation “outputting… a value of the values of the state of health having a precise determination of the state of health accuracy by reducing a scattering of the values of the state of health;” merely adds insignificant extra-solution activity to the judicial exception because it claims mere data outputting.
The limitation “optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store;” merely adds insignificant extra-solution activity to the judicial exception because generally links the abstract idea to a particular technological environment because it claims field of use.
Even when viewed in combination, the additional element does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception (Step 2A prong two: NO).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05
Regarding the additional element:
The processor, the machine learning, the two models, the measuring processes, the physical model, and the electrochemical model are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Thus, these limitations represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP 2106.05(f) Implementing an abstract idea on generic electronic components as a tool to perform an abstract idea does not amount to significantly more. See Elec. Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”)
The limitation “outputting a value of the values of the state of health having a greatest accuracy” represents merely outputting data. Taniguchi (US7106047B2) discloses outputting the most recently calculated value of residual capacity. Kiesel (US9209494B2) discloses generating a most-recent state-of-charge value. Further, Jobson (US20220234469A1) discloses choosing an application giving the highest State of Health value.
The limitation “optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store;” represents merely outputting data. Saint-Marcoux (US10180464B2) discloses proceeding from a reliable estimation of the SOHE of the battery, it may be possible to optimize the charge-discharge cycling of the battery so as to increase the service life of the battery compared with charge-discharge cycling that would still be performed within the same limits of no-load voltages of the battery. Severin (US10877101B2) discloses provides important results for model-based planning and optimization of a battery during its service life. Further, Fink (US11193984B2) discloses using an electrochemical energy storage device within a specifiable usage plan time period so as to optimize the service life.
In view of the foregoing, in accord with MPEP 2106.05(d), simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception does not qualify the claim as reciting “significantly more”. Even when considered in combination, the additional element represents mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept (Step 2B: NO). The claim is not patent eligible.
Regarding claims 18 and 20, under their broadest reasonable interpretation, the limitations of claim 18 further defines the operating state and claim 20 further define the determining, which have been established to include abstract ideas. There are no additional limitations in the claims to apply, rely on, or use the judicial exception in a manner that would impose a meaningful limit on the judicial exception. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, claims 18 and 20 are not patent eligible.
Regarding claim 21, the claim has similar limitations as claim 12; moreover, claim 21 recites a non-transitory machine-readable memory medium on which is stored a computer program, which are generic computer components and do not practically integrate the invention nor amount to significantly more. The claim 21 is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 12 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN 110308396 A -hereinafter Wang) in view of Garcia et al. (US20190187212A1 -hereinafter Garcia) in view of Bohne et al. (WO2019057871A1 -hereinafter Bohne) in view of Guan et al. (CN108695570B -hereinafter Guan -Note: As the machine translation attached).
Regarding Claim 12, Wang teaches a method for determining a state of health of an electrical energy store, the method comprising:
detecting, via a processor, an operating state of the electrical energy store; (page 2, last paragraph; Wang: “S11, receiving the real-time operation data of the battery collected by the battery management system”)
detecting, via the processor, data for determining the state of health of the electrical energy store using a first model and/or measuring process of the selected at least two models and/or measuring processes; (see page 5, last paragraph; Wang: “the parameters of the battery model comprise direct current resistance, short-time resistance, long-time resistance, short-time capacitance and long-time capacitance;” See page 6, first paragraph: “establishing a first equivalent circuit model according to the parameters of the battery model”.)
determining, via the processor, the state of health of the electrical energy store using the first model and/or measuring process; (see page 3; second paragraph; Wang: “the battery state data includes state of charge data and state of health data (SOH) of the battery; then the process of the first step is carried out,”)
correcting, using the state of health determined using the first model and/or measuring process, a parameter of a further model and/or measuring process of the at least two models and/or measuring processes; (see page 3, third paragraph; Wang: “And S12, correcting the parameters of the battery model according to the real-time operation data and the battery state data to obtain the corrected parameters.”)
detecting, via the processor, data for determining the state of health of the electrical energy store using the further model and/or measuring process with the corrected parameter; (see page 6, paragraph 9; Wang: “the battery management system establishes a second equivalent circuit model according to the corrected parameters;”)
determining, via the processor, the state of health of the electrical energy store using the further model and/or measuring process with the corrected parameter; (see page 6, paragraph 10; Wang: “calculating the state of charge data by adopting an ampere-hour integration method according to the second equivalent circuit model, the current voltage of the battery cell, the current of the battery and the current temperature of the battery cell;”)
evaluating, via the processor, values for the state of health of the electrical energy store, determined using the first model and/or measuring process and the further model and/or measuring process with the corrected parameter, taking into account the detected operating state; and (see page 6, paragraph 11; Wang: “judging whether the calculated charge state data is equal to the pre-configured standard charge state data or not; if so, judging that the revised parameters pass the verification again; if not, judging that the revised parameters fail to be verified again.” See page 11, third paragraph: “specifically, when the calculated cell voltage is equal to the current voltage of the cell, it is determined that the corrected parameter passes verification, that is, the edge processor determines that the corrected parameter is valid, and the corrected parameter may be returned to the battery management system; when the calculated cell voltage is not equal to the current voltage of the cell, it is determined that the corrected parameter verification fails, that is, the edge processor determines that the corrected parameter is invalid, and the corrected parameter can be discarded.”)
outputting, via the processor, a value of the values of the state of health having a precise determination of the state of health accuracy by reducing a scattering of the values of the state of health; (see page 13, paragraph 4; Wang: “the corrected parameters are mutually verified through the edge processor and the battery management system, so that the validity of the corrected parameters is ensured, the precision of the updated battery model is ensured, the state of the battery can be accurately monitored by applying the battery model, and the problem of overlarge calculation error of the state of charge data caused by the reduction of the precision of the battery model under the condition of battery aging or extreme environment is effectively avoided, so that the maximum capacity output of the battery is ensured, the safe operation of the battery is ensured, and the user experience is improved. In addition, the embodiment of the invention corrects and checks the parameters of the battery model by the edge processor, thereby solving the problems that the calculation resources of the battery management system are limited and the parameters of the battery model are difficult to correct by using the real-time running data of the battery. Moreover, real-time operation data of the battery do not need to be sent to a remote cloud server, so that the battery model parameters are corrected by means of the cloud server, and the remote data transmission quantity is reduced. In addition, parameters of the battery model are corrected through the real-time operation data and the battery state data of the battery, so that the parameters of the battery model are adjusted according to the specific battery, and the problem of large errors caused by individual differences of the battery is solved.”)
using a model whose parameters have been corrected using the values for the state of health determined using the first model and/or measuring process and the further model and/or measuring process with the corrected parameter, (see page 16, paragraph 4; Wang: “And S25, after the verification is passed again, updating the corrected parameters into the battery model. Specifically, after the battery management system determines that the revised parameters pass the re-verification, parameters of the battery model are replaced with the revised parameters, so that the battery model is updated, and the updated battery model is obtained. And S26, applying the updated battery model to monitor the state of the battery.”)
However, Wang does not explicitly teach:
selecting, via the processor, as a function of the detected operating state, at least two models and/or measuring processes for determining the state of health of the electrical energy store from a plurality of models and/or measuring processes, the selecting including using machine learning to evaluate;(i) the detected operating state, (ii) the plurality of models and/or measuring processes, and (iii) states of health of a plurality of electrical energy stores in order for the machine learning to associate with the detected operating state those of the models and/or measuring processes having a greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to an accuracy of others of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state, wherein the selected at least two models and/or measuring processes being those of the models and/or measuring processes having the greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to the accuracy of the other of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state, as evaluated by the machine learning;
predicting… a future course of the state of health of the electrical energy store; and
optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store;
wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include at least one physical model, for stationary operation, using an electrical equivalent circuit diagram for the electrical energy store with current integration for determining charge quantity, and an electrochemical model for dynamic operation.
Garcia from the same or similar field of endeavor teaches:
selecting, via the processor, as a function of the detected operating state, at least two models and/or measuring processes (see Fig. 1 and [0066]; Garcia: “The internal parameters pi are computed assuming observed, what-if, and forecast conditions defined in module 110 and learned battery models 108 including electrical equivalent circuits, electrochemical aging models, analytical/kinetic models, as well as other battery models.”) for determining the state of health of the electrical energy store from a plurality of models and/or measuring processes (see Fig. 1 and [0064]; Garcia: “output data 195 include SOC, SOH, RUL, and EOL. As value examples: …SOH (state of health) may be presented as a number estimate between 0 and 1 with 0 indicating a failed battery and 1 indicating a healthy battery”), the selecting including using machine learning to evaluate: (i) the detected operating state (see [0075]; Garcia: “To accomplish the tuning, the training and learning module 210 may use inputs from existing internal state data 212 as well as current and historical external data 214, such as, for example, temperature and load conditions.”), (ii) the plurality of models and/or measuring processes (see Fig. 1 and [0066]; Garcia: “The internal parameters pi are computed assuming observed, what-if, and forecast conditions defined in module 110 and learned battery models 108 including electrical equivalent circuits, electrochemical aging models, analytical/kinetic models, as well as other battery models.”), and (iii) states of health of a plurality of electrical energy stores (see [0107]; Garcia: “Thus, an SOHc calculation 632 is performed for capacity, an SOHp calculation 634 is performed for available power, and an SOHr calculation 636 is performed for pulse resistance.”) in order for the machine learning to associate with the detected operating state those of the models and/or measuring processes having a greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to an accuracy of others of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state (see [0075]; Garcia: “The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data.” See [0108]: “In order to compute the aggregated SOH[k] 696 of the battery as output data 195, different decision fusion algorithms can be used to aggregate these individual assessments.”), wherein the selected at least two models and/or measuring processes being those of the models and/or measuring processes having the greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to the accuracy of the other of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state, as evaluated by the machine learning; (see [0075]; Garcia: “The training and learning module 210 thus constructs models (e.g., aging models) that best fit estimations with the training data.”)
wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include …using an electrical equivalent circuit diagram for the electrical energy store with current integration for determining charge quantity, and an electrochemical model for dynamic operation. (see [0066]; Garcia: “The internal parameters pi are computed assuming observed, what-if, and forecast conditions defined in module 110 and learned battery models 108 including electrical equivalent circuits, electrochemical aging models, analytical/kinetic models, as well as other battery models.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Wang to include Garcia’s features of selecting, via the processor, as a function of the detected operating state, at least two models and/or measuring processes for determining the state of health of the electrical energy store from a plurality of models and/or measuring processes, the selecting including using machine learning to evaluate;(i) the detected operating state, (ii) the plurality of models and/or measuring processes, and (iii) states of health of a plurality of electrical energy stores in order for the machine learning to associate with the detected operating state those of the models and/or measuring processes having a greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to an accuracy of others of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state, wherein the selected at least two models and/or measuring processes being those of the models and/or measuring processes having the greatest accuracy for determining the state of health of the electrical store for the detected operating state relative to the accuracy of the other of the plurality of models and/or measuring processes for determining the state of health of the electrical store for the detected operating state, as evaluated by the machine learning; the at least two models and/or measuring processes for determining the state of health of the electrical energy store include …using an electrical equivalent circuit diagram for the electrical energy store with current integration for determining charge quantity, and an electrochemical model for dynamic operation. Doing so would estimate and predict battery health and battery performance in order to provide a user, or other systems, with information on the present state and possible future states of a battery. (Garcia, [0006])
However, it does not explicitly teach:
predicting… a future course of the state of health of the electrical energy store; and
optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store;
wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include at least one physical model, for stationary operation…
Bohne from the same or similar field of endeavor teaches:
predicting… a future course of the state of health of the electrical energy store; and (see page 14, paragraph 9, Bohne: “a predictive diagnostic model predicts the future course of this health condition to predict the failure.”)
optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store; (see page 9, paragraph 10; Bogne: “The calculation of the failure prediction can be carried out in the form of a mean expected remaining service life. As a measure of the particular remaining life, the maintenance intervals may be variably controlled, also referred to as predictive maintenance, and / or controlling the future loading of the components, referred to as Predictive Health Management, to increase the remaining lifetimes of the components.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Wang and Garcia to include Bohne’s features of predicting a future course of the state of health of the electrical energy store; and optimizing a service life of the electrical energy store using the predicted further course of the state of health of the electrical energy store. Doing so would predict the failure of components and improve product safety. (Bohne, page 3, last paragraph)
However, it does not explicitly teach: wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include at least one physical model, for stationary operation, using an electrical equivalent circuit diagram for the electrical energy store with current integration for determining charge quantity, and an electrochemical model for dynamic operation.
Guan from the same or similar field of endeavor teaches: wherein the at least two models and/or measuring processes for determining the state of health of the electrical energy store include at least one physical model, for stationary operation… (see page 2, fourth paragraph; Guan: “A physical model of a lithium battery based on self-healing characteristics, as shown in FIG. 2… The model can be divided into two parts, one part describes the discharge phase voltage of the lithium battery and the other part describes the self-healing voltage of the lithium battery during the stationary stage.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Wang, Garcia, and Bohne to include Guan’s features of including at least one physical model, for stationary operation and an electrochemical model for dynamic operation. Doing so would improve accuracy and extend the life of the battery. (Guan, third paragraph)
Regarding Claim 21, the limitations in this claim is taught by the combination of Wang, Garcia, Bohne, and Guan as discussed connection with claim 12.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Garcia in view of Bohne in view of Guan in view of Gokulakrishnan et al. (WO 2022/058416 A1 -hereinafter Gokulakrishnan).
Regarding Claim 18, the combination of Wang, Garcia, Bohne, and Guan teaches all the limitations of claim 12 above; however, it does not explicitly teach: wherein the operating state is: (i) a dynamic operation or a stationary operation of the electrical energy store, and/or (ii) a charging or discharging of the electrical energy store at an associated charge rate or discharge rate, and/or (iii) a balancing state of the electrical energy store, and/or (iv) a maintenance state in a repair shop.
Gokulakrishnan further teaches wherein the operating state is: (i) a dynamic operation or a stationary operation of the electrical energy store (see page 17, first paragraph; Gokulakrishnan: “discharge capacity in the dynamic discharge phase and charge capacity in the recuperation phase,”), and/or (ii) a charging or discharging of the electrical energy store at an associated charge rate or discharge rate (see page 5, fifth paragraph; Gokulakrishnan: “constant voltage charging phase, discharge capacity in a constant current discharge phase and discharge capacity in a constant voltage discharge phase,”), and/or (iii) a balancing state of the electrical energy store (see page 14, fourth paragraph; “One or more variables (operating parameters) influencing the charge, such as operation or rest before charging, polarization of the battery before charging, as well as additional battery knowledge (battery parameters), such as cell chemistry, electrode balancing, or previous main aging mechanism, can also be output as parameters and serve as model input.”), and/or (iv) a maintenance state in a repair shop.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Wang, Garcia, and Bohne to include Gokulakrishnan’s features of or discharging of the electrical energy store at an associated charge rate or discharge rate, and/or (iii) a balancing state of the electrical energy store, and/or (iv) a maintenance state in a repair shop. Doing so would allow a precise determination of the aging state of the energy store. (Gokulakrishnan, page 6, third paragraph)
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Garcia in view of Bohne in view of Guan in view of Boehm et al. (WO2010020532A1 -hereinafter Boehm -Note: as the machine translation attached).
Regarding Claim 20, the combination of Wang, Garcia, Bohne, and Guan teaches all the limitations of claim 12 above, Garcia further teaches wherein the selected at least two models and/or measuring methods for determining the state of health of the electrical energy store… ((see Fig. 1 and [0066]; Garcia: “The internal parameters pi are computed assuming observed, what-if, and forecast conditions defined in module 110 and learned battery models 108 including electrical equivalent circuits, electrochemical aging models, analytical/kinetic models, as well as other battery models.” See Fig. 1 and [0064]; Garcia: “output data 195 include SOC, SOH, RUL, and EOL. As value examples: …SOH (state of health) may be presented as a number estimate between 0 and 1 with 0 indicating a failed battery and 1 indicating a healthy battery”)
However, it does not explicitly teach: …include a measuring method with charge quantity determination at defined voltage levels after a balancing operation, and/or a measuring method with determination of an open circuit voltage upon calling up of the balancing function after a defined switch-off time of the electrical energy store, and/or repair shop measurements.
Boehm from the same or similar field of endeavor teaches: …include a measuring method with charge quantity determination at defined voltage levels after a balancing operation (see page 5, third paragraph; Boehm: “The feedback which the integrator already has for the detected current in order to integrate it into the charged charge is thus changed according to the invention such that now (1) the measurement state of charge is returned weighted and (2) with a weighted estimate state of charge ( which is based on an open circuit voltage) is combined. The signal returned to the integrator (which is used to add up current values to detect the charge balance) thus corresponds to a combination of weighted estimate state of charge measurement state of charge. The combination of the weighted states of charge can in principle lead directly to the actual state of charge or, in principle, can also indirectly lead to the actual state of charge, for example if the combination of the weighted state of charge is fed into a feedback of an integrator (ie if the combined weighted states of charge are integrated). and the integrator outputs the actual state of charge as an integral of the combined weighted states of charge (ie, the integrated combined weighted state of charge provides the actual state of charge)”), and/or a measuring method with determination of an open circuit voltage upon calling up of the balancing function after a defined switch-off time of the electrical energy store, and/or repair shop measurements. (see page 5, last paragraph; Boehm: “, the open circuit voltage may be provided by measuring a terminal voltage that occurs after a rest period, wherein the terminal voltage may be further modified according to the length of the rest period, for example, the terminal voltage at too short rest periods, in which the balancing processes have not subsided, be extrapolated to a final open circuit voltage. Instead of measurements of the terminal voltage after a rest period, a model may also be used, for example a battery impedance model which continuously or regularly monitors the terminal voltage and the accumulator current under load. and during load breaks to constantly update the accumulator model.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of the combination of Wang, Garcia, Bohne, and Guan to include Boehm’s features of measuring method with charge quantity determination at defined voltage levels after a balancing operation, and/or a measuring method with determination of an open circuit voltage upon calling up of the balancing function after a defined switch-off time of the electrical energy store, and/or repair shop measurements. Doing so would significantly reduce measurement noise and improve the accuracy. (Boehm, page 4, fifth paragraph)
Response to Arguments
Applicant's arguments with respect to the claim rejection(s) under 35 U.S.C. 101 have been fully considered but they are not persuasive.
With respect to applicant’s argument located of the page 7 which recites:
“In the "selecting" of the claims, machine learning is used to select the most accurate models/measuring processes for a detected operating state of the energy store. By selecting the particular models/measuring processes based on the detected operating state of the energy store, the models/measuring processes that produce the most accurate values of the state of health are determined. (See, e.g., Applicant's published application US 2022/0170993 at [0005].) As discussed in, e.g., [0039], physical models determined the state of health in stationary operation than in dynamic operation of the energy store. Electrochemical models are more accurate in dynamic operation of the energy store.
Additionally, the "correcting" step of the claims allows the various models/measuring methos to be mutually optimized, thus improving the accuracy even further. (See, e.g., [0012].) Additionally, the "predicting" and "optimizing" steps of the claims may achieve a vehicle-individual optimization of the service life of the individual energy stored. (See, e.g., [0014]).”
The Applicant’s argument has been considered but is not deemed persuasive. Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraph [0025] describing the selection function, the selection of the at least two models could be performed as a mental process that can be performed through observation, evaluation and judgement. A person could define or designate the most accurate value for the state of health for the present operating state are selected. Moreover, using a machine learning algorithm for this purpose is akin to using a processor. Machine learning algorithm is recited at a high level of generality and they are generic computing components that do not integrate the invention into a practical application nor do they amount to significantly more than the abstract idea. Besides, under their broadest reasonable interpretation and based on the description provided in the Specification, such as page 9 lines 12-15 describing the predicting step, predicting a future course of the state of health could be performed as a mental process that can be performed through observation, evaluation and judgement. Moreover, the limitation “optimizing...” does not integrate the invention into a practical application because it’s just “applying” the abstract idea.
The limitations “selecting” and “correcting” recite abstract ideas. “An improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology” (see MPEP 2106.05(a)). The limitation “optimizing” merely adds insignificant extra-solution activity to the judicial exception because generally links the abstract idea to a particular technological environment because it claims field of use.
Applicant’s arguments with respect to the claim rejection(s) under 35 U.S.C. 103 have been fully considered and are persuasive because of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hamann et al. (US20150347922A1) discloses the multi-model blending described herein specifically relates to using one or more models (at least one of which is a physical model) and then using machine learning for the blended model.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.”
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/V.N.T./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117