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 . 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 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/439,450 filed on 02/12/2024. This communication is the first action on the merits.
Status of Claims
Claims 1-10 are currently pending and have been rejected as follows.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
IDS
The information disclosure statements filed on 02/12/2024 and 03/22/2024 comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and are considered.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "302" and "303" have both been used to designate the first mathematical model. Based on [0045], the result of block 305 is the input to the first mathematical model, which is block 302 according to [0042]. But based on FIG. 3, the output of block 305 is fed to block 303. Similarly, both reference characters "302" and "303" have both been used to designate the second mathematical model. According to [0043], block [308] should be connected to block 303, but it is fed to block 302 in FIG. 3.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In addition to Replacement Sheets containing the corrected drawing figure(s), applicant is required to submit a marked-up copy of each Replacement Sheet including annotations indicating the changes made to the previous version. The marked-up copy must be clearly labeled as “Annotated Sheets” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. See 37 CFR 1.121(d)(1). Failure to timely submit the proposed drawing and marked-up copy will result in the abandonment of the application.
Specification
The following is a quotation of 37 CFR 1.71(a):
The specification must include a written description of the invention or discovery and of the manner and processes of making and using the same, and is required to be in such full, clear, concise, and exact terms as to enable any person skilled in the art or science to which the invention or discovery appertains, or with which it is most nearly connected, to make and use the same.
The specification is objected to because of the following informalities:
In paragraph [0015], line 5 “whereby the scale value…” should read “whereby the scalar value…”
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Line 6, “… unit;” should read “… unit; and”
Claim 4 is objected to because of the following informalities:
Line 5, “… model;” should read “… model; and”
Claim 6 is objected to because of the following informalities:
Line 3, “… model;” should read “… model; and”
Claim 8 is objected to because of the following informalities:
Line 4, “… means;” should read “… means; and”
Appropriate correction is required.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and do not include additional elements that amount to significantly more than the judicial exception. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method for predicting an aging state of an electrical energy storage unit, the method comprising the following steps:
providing a first mathematical model having first input variables in order to evaluate factors influencing the aging of the electric energy storage unit;
providing a second mathematical model having second input variables in order to determine the aging state of the electrical energy storage unit;
combining the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method (process) claim. Likewise, both claims 9 and 10 are considered system claims (a device and a non-transitory, computer-readable storage medium)
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and/or a Mathematical Concept. This can be seen in the claim limitations of providing a first model to “evaluate” factors influencing the aging of the electric energy storage unit, providing a second model to “determine” the aging state of the electrical energy storage unit, and using a third model to “predict” the aging state of the electrical energy storage unit which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to predict the aging state of the electrical energy storage unit and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. a first mathematical model … to evaluate, a second mathematical model … to determine, and a third mathematical model … to predict.
Similar limitations comprise the abstract ideas of Claims 9 and 10.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) 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.
In addition to the abstract ideas recited in claim 1, the claimed method further recites the additional element(s) of using generic AI/ML technology, i.e. “by means of a neural network into a third mathematical model”, to perform estimations or predictions. The claim does not recite any details regarding how the neural network functions or is trained. Instead, the claims are found to utilize the neural network as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer. See MPEP 2106.05(f). Additionally, the use of the “neural network” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of the “neural network” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
System claims 9 and 10 additionally recite “A device for … comprising at least one electronic computing unit” and “A non-transitory, computer-readable storage medium containing instructions that when executed on a computer cause the computer to…” respectively. However, they are found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general-purpose computer do not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
The generic data processing is recited at such a high level of generality (e.g. “A device for predicting…” and “A non-transitory, computer-readable storage medium containing instructions …”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is recited with the result of predicting the aging state of the electrical energy storage unit.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general-purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 9 and 10). Such insignificant extra-solution activity when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claims 9 and 10, amount to significantly more than the abstract idea.
With respect to dependent claim 2, the claim further recites using the third mathematical model to predict the aging state of the electrical energy storage unit. This is the same abstract idea recited in claim 1.
With respect to dependent claim 3, the claim further recites “the first input variables of the first mathematical model are in the form of multi-dimensional histograms”. This limitation merely specifies the particular form or characterization of the data that is used as input to the first mathematical model. Characterizing or arranging data in a particular format is an abstract idea. See MPEP 2106.04(a)(2)(III) (mental processes) and MPEP 2106.04(a)(2)(I) (mathematical concepts)
With respect to dependent claim 4, the claim further recites “extracting histogram information while taking into account domain knowledge about the causes of aging of an electrical energy storage unit as first input variables for the first mathematical model; combining histogram information into scalar statistical variables as first input variables quantities for the first mathematical model.” Understood from the specification [0015] and [0050], this limitation is nothing more than multiplying the histogram with an evaluation function to yield a scalar or a set of statistical variables; thus, this limitation recites an abstract idea which is a Mental Process and/or a Mathematical Concept.
With respect to dependent claim 5, the claim further recites “wherein the second mathematical model comprises a further neural network featuring a memory function.” The mere recitation that the second mathematical model comprises a neural network featuring a memory function does not integrate the exception into a practical application or provide an inventive concept as it does not recite any details regarding how the neural network functions or is trained. Instead, the claim is found to utilize the neural network as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer. See MPEP 2106.05(f). Additionally, the use of a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of a neural network featuring a memory function to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
With respect to dependent claim 6, the claim further recites “providing data of the first input variables of the first mathematical model and data of the second input variables of the second mathematical model; training the third mathematical model comprising the first and the second mathematical models in order to optimize the prediction of the aging state using the data of the first input variables and the second ones”. Providing data to the first and the second models amount to insignificant data gathering activity and therefore, fails to integrate the recited abstract idea into a practical application. Training the third model recited at a high level of generality without detailed steps is merely an attempt at limiting the abstract idea to a particular technological environment and therefore fails to integrate the recited abstract idea into a practical application or amounts to significantly more than the judicial exception.
With respect to dependent claim 7, the claim further recites “the first input variables of the first mathematical model include an electrical voltage of the electrical energy storage unit, an electrical current of the electrical energy storage unit, a temperature of the electrical energy storage unit, and/or a state of charge of the electrical energy storage unit, and/or wherein the second input variables of the second mathematical model include a state of health of the capacity and/or the internal resistance”.
This limitation merely specifies the particular form or characterization of the data that is used as input to the first mathematical model. Characterizing data or arranging data in a particular format is an abstract idea. See MPEP 2106.04(a)(2)(III) (mental processes) and MPEP 2106.04(a)(2)(I) (mathematical concepts).
With respect to dependent claim 8, the claim further recites “saving data of the first input variables of the first mathematical model and/or data of the second input variables of the second mathematical model in a first data storage means; transmitting the stored data to a second data storage means physically located at another location”. This claim recites insignificant extra-solution activity, e.g., data storage and transmission, and is found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(ii) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 6-7, 9-10 are rejected under 35 U.S.C 103 as being unpatentable over TWAICE (US20250172616A1) in view of DING (Ding, Y., Lu, C., & Ma, J. (2017, December). Li-ion battery health estimation based on multi-layer characteristic fusion and deep learning. In 2017 IEEE vehicle power and propulsion conference (VPPC) (pp. 1-5). IEEE.).
Regarding claim 1, TWAICE teaches on the following limitations of the claim:
A method for predicting an aging state of an electrical energy storage unit, the method comprising the following steps: ([0011]-[0013]: “[0011] Embodiments of the invention are generally based on a combined battery aging model, obtained by combining [0012] a battery aging parametric model, based on laboratory data, with [0013] a battery aging machine learning model, based on real-world operation data.”)
providing a first mathematical model having first input variables in order to evaluate factors influencing the aging of the electric energy storage unit: (Fig. 1 and [0020]: “…, the system comprising a parametric battery model, configured to receive one or more inputs and to provide a first output based thereon, …”)
providing a second mathematical model having second input variables in order to determine the aging state of the electrical energy storage unit; (Fig. 1 and [0020]: “… a machine learning model, which has been trained to correct an output of the parametric battery model based on battery operation comprising aging, …”)
TWAICE fails to teach, DING, however, does teach the following limitation of claim 1
combining the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit. (Fig. 5, which shows the use of a neural network for combining two different sets of parameters X and Y for battery health estimation)
Since both TWAICE and DING are in the same technical field, i.e., battery health estimation, and in [0072] TWAICE teaches that any function f can be implemented for obtaining OUTCBAM, which is the output of the combination of two models BAM and MLM, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE’s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include combining the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit in view of DING with the motivation to improve the accuracy and reliability for battery health estimation (see DING, Section V. Conclusion->main contributions) (see MPEP 2143-I-(G)).
Regarding claim 2, TWAICE in view of DING teaches the elements of the parent claim(s).
TWAICE further teaches the following limitations of the claim:
The method according to claim 1, further comprising: predicting the aging state of the electrical energy storage unit using the third mathematical model ([0029]: “the third output can be an improved estimation of the state of health of the battery, based on real-world battery operation”, which clearly indicates that the third model is used to predict the aging state of the battery).
Regarding claim 6, TWAICE in view of DING teaches the elements of the parent claim(s).
TWAICE further teaches the following limitations of the claim:
The method according to claim 1, further comprising: providing data of the first input variables of the first mathematical model and data of the second input variables of the second mathematical model; ([0028]: “…to provide inputs to the parametric model and to the machine learning model …”)
TWAICE fails to teach, Ding, however, does teach the following limitation of the claim
training the third mathematical model comprising the first and the second mathematical models in order to optimize the prediction of the aging state using the data of the first input variables and the second ones (Figure 6 and Section III-B: “Finally, the logistic regression is trained with the obtained features to estimate the health state of Li-ion battery. The detailed structure of the model is shown in figure 6.”)
Since both TWAICE and DING are in the same technical field, i.e., battery health estimation, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have replaced TWAICE’s third model with a neural network, as previously discussed, and to clearly include training the third mathematical model comprising the first and the second mathematical models in order to optimize the prediction of the aging state using the data of the first input variables and the second ones in view of DING with the motivation to adjust the parameters of the third model to optimally predict the health state of the battery (see MPEP 2143-I-(G).
Regarding claim 7, TWAICE in view of DING teaches the elements of the parent claim(s).
TWAICE further teaches the following limitations of the claim:
The method according to claim 1, wherein the first input variables of the first mathematical model include an electrical voltage of the electrical energy storage unit, an electrical current of the electrical energy storage unit, a temperature of the electrical energy storage unit, and/or a state of charge of the electrical energy storage unit, and/or wherein the second input variables of the second mathematical model include a state of health of the capacity and/or the internal resistance ([0022]-[0027]: In some embodiments, the one or more inputs can comprise one or more among: temperature, depth of discharge, state of charge, voltage, current, of the battery).
Regarding claim 9, TWAICE teaches the following elements of the claim(s).
A device for predicting an aging state of an electrical energy storage unit comprising at least one electronic computing unit ([0039]: “… A further embodiment can relate to a computer implemented model, for modelling at least one state of a battery, the computer-implemented model comprising a processor …” )
configured to provide a first mathematical model having first input variables in order to evaluate factors influencing the aging of the electric energy storage unit; (Fig. 1 and [0020]: “…, the system comprising a parametric battery model, configured to receive one or more inputs and to provide a first output based thereon, …”)
provide a second mathematical model having second input variables in order to determine the aging state of the electrical energy storage unit; (Fig. 1 and [0020]: “… a machine learning model, which has been trained to correct an output of the parametric battery model based on battery operation comprising aging, …”)
TWAICE fails to teach, however, DING, does teach the following limitation of claim 9
and combine the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit. (Fig. 5, which shows the use of a neural network for combining two different sets of parameters X and Y for battery health estimation)
Since both TWAICE and DING are in the same technical field, i.e., battery health estimation, and in [0072] TWAICE teaches that any function f can be implemented for obtaining OUTCBAM, which is the output of the combination of two models BAM and MLM, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE’s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include to combine the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit in view of DING with the motivation to improve the accuracy and reliability for battery health estimation (see DING, Section V. Conclusion->main contributions) (see MPEP 2143-I-(G)).
Regarding claim 10, TWAICE teaches the following elements of the claim(s).
A non-transitory, computer-readable storage medium containing instructions that when executed on a computer cause the computer to … ([0039]: “A further embodiment can relate to a computer implemented model, for modelling at least one state of a battery, the computer-implemented model comprising a processor and a memory, the memory comprising instructions being configured to, when executed by the processor, cause the processor to implement any of the methods described above or throughout the description, or any of the systems” ).
provide a first mathematical model having first input variables in order to evaluate factors influencing the aging of the electric energy storage unit; (Fig. 1 and [0020]: “…, the system comprising a parametric battery model, configured to receive one or more inputs and to provide a first output based thereon, …”)
provide a second mathematical model having second input variables in order to determine the aging state of the electrical energy storage unit; (Fig. 1 and [0020]: “… a machine learning model, which has been trained to correct an output of the parametric battery model based on battery operation comprising aging, …”)
TWAICE fails to teach, however, DING, does teach the following limitation of claim 10:
and combine the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit. (Fig. 5, which shows the use of a neural network for combining two different sets of parameters X and Y for battery health estimation)
Since both TWAICE and DING are in the same technical field, i.e., battery health estimation, and in [0072] TWAICE teaches that any function f can be implemented for obtaining OUTCBAM, which is the output of the combination of two models BAM and MLM, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE’s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include and combine the first mathematical model and the second mathematical model by means of a neural network into a third mathematical model in order to predict the aging state of the electrical energy storage unit in view of DING with the motivation to improve the accuracy and reliability for battery health estimation (see DING, Section V. Conclusion->main contributions) (see MPEP 2143-I-(G)).
Claim 3 is rejected under 35 U.S.C 103 as being unpatentable over TWAICE (US20250172616A1) in view of DING (Ding, Y., Lu, C., & Ma, J. (2017, December). Li-ion battery health estimation based on multi-layer characteristic fusion and deep learning. In 2017 IEEE vehicle power and propulsion conference (VPPC) (pp. 1-5). IEEE.), and further in view of BOEHM (US20130241567A1).
The combination of TWAICE and DING teaches the elements of the parent claim(s).
The combination of TWICE and DING fails to teach, BOEHM, however, does teach the following limitations of claim 3:
The method according to claim 1, wherein the first input variables of the first mathematical model are in the form of multidimensional histograms. ([0030]: “it is advantageous if the frequency of occurrence of certain values of the physical variables is represented as a function of one another and/or the frequency with which a certain number of processes are carried out is represented as a function of one another, in a visually perceptible fashion in at least one three dimensional histogram.”, [0030]: “The state of health and the service life can be calculated from the histogram by means of suitable algorithms”; [0031]: “… the values which can be derived from the histogram can also be used immediately in the battery management system as open-loop and/or closed-loop control signals for the operation of the battery cell or of an entire battery, in order to prevent premature ageing phenomena or wear phenomena.”)
Since the combination of TWAICE and DING and BOEHM are in the same technical field, i.e., battery health/remaining life estimation, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE’s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include wherein the first input variables of the first mathematical model are in the form of multidimensional histograms in view of BOEHM with the motivation to effectively process time-series data and efficiently save data storage resource at the same time (see BOEHM, [0023] and [0029]) (see MPEP 2143-I-(G)).
Claim 4 is rejected under 35 U.S.C 103 as being unpatentable over TWAICE (US20250172616A1) in view of DING (Ding, Y., Lu, C., & Ma, J. (2017, December). Li-ion battery health estimation based on multi-layer characteristic fusion and deep learning. In 2017 IEEE vehicle power and propulsion conference (VPPC) (pp. 1-5). IEEE.), further in view of BOEHM (US20130241567A1), and further in view of SCHMIDT (US20210237580A1).
The combination of TWAICE, DING, and BOEHM teaches the elements of the parent claim(s).
The combination of TWAICE, DING, and BOEHM fails to teach, SCHMIDT, however, does teach the following limitations of the claim:
The method according to claim 3, further comprising at least one of the following steps: extracting histogram information while taking into account domain knowledge about the causes of aging of an electrical energy storage unit as first input variables for the first mathematical model; combining histogram information into scalar statistical variables as first input variables quantities for the first mathematical model (SCHMIDT [0034]: “Characteristic variables of the energy store which are stored maybe , for example , various histograms of past operating states of the respective voltages , charging and / or discharging currents , temperatures and states of charge ( SOC ) , in each case in the form of maximum , minimum and average values.”);
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to have modified Twaice/Ding/Boehm’s estimation of battery health/aging to include combining histogram information into scalar statistical variables as first input variables quantities for the first mathematical model in view of Schmidt by combining the use of a model to evaluate factors influencing battery ageing based on multi-dimensional histograms of input variables taught by Twaice in view of Boehm, as described above, with the determination of maximum, minimum, and average values for histograms of characteristic variables taught by Schmidt in the same field of battery health estimation. In the combination each element merely would have performed the same function as it did separately and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Twaice (e.g. [0056] describing that the inputs to the model correspond to battery characteristics that can be computed and [0083] describing mean values) and Boehm (e.g. [0020] describing identifying maximum and minimum values for the physical variables, [0037] looking at frequency of variables within a range, and [0030]: “The state of health and the service life can be calculated from the histogram by means of suitable algorithms”) as well as the references’ discussion of using standard computer implementation to perform the aforementioned functions, the results of the combination were predictable, i.e. the input variables to the model could be of any suitable form for evaluating factors which influence battery aging including the multi-dimensional histograms or statistical scalar values such as minimum, maximum, or averages determined from the histograms as are known in the art (MPEP 2143 A).
Claim 5 is rejected under 35 U.S.C 103 as being unpatentable over TWAICE (US20250172616A1) in view of DING (Ding, Y., Lu, C., & Ma, J. (2017, December). Li-ion battery health estimation based on multi-layer characteristic fusion and deep learning. In 2017 IEEE vehicle power and propulsion conference (VPPC) (pp. 1-5). IEEE.), and further in view of TAO (CN114636932A).
Specifically, TWAICE in view of DING teaches the elements of the parent claim(s). TWICE in view of DING fails to teach, TAO, however, does teach the following limitations of the claim:
The method according to claim 1, wherein the second mathematical model comprises a further neural network featuring a memory function ([0011]: “… the prediction model includes a convolutional neural network (CNN) and a long short-term memory neural network (LSTM), where CNN is used to extract spatial correlation features of the data, and LSTM is used to extract the temporal features of the spatial correlation features to predict the remaining lifespan of the battery;…”).
Since TWAICE and TAO are in the same technical field, i.e., battery health/remaining life estimation, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE’s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include wherein the second mathematical model comprises a further neural network featuring a memory function in view of TAO with the motivation to improve battery health prediction with more data, i.e., with the time-series input instead of instantaneous or current values of the listed parameters (see TAO, Abstract) (see MPEP 2143-I-(G)).
Claim 8 is rejected under 35 U.S.C 103 as being unpatentable over TWAICE (US20250172616A1) in view of DING (Ding, Y., Lu, C., & Ma, J. (2017, December). Li-ion battery health estimation based on multi-layer characteristic fusion and deep learning. In 2017 IEEE vehicle power and propulsion conference (VPPC) (pp. 1-5). IEEE.), and further in view of HUESSON, (DE102020108365A1).
Specifically, the combination of TWAICE and DING teaches the elements of the parent claim(s). The combination of TWAICE and DING fails to teach, HUESSON, however, does teach the following limitations of claim 8:
The method according to claim 7, comprising: saving data of the first input variables of the first mathematical model and/or data of the second input variables of the second mathematical model in a first data storage means; ([0009]: “The statistical usage data can thus be designed in such a way that the usage data can be stored in a resource-efficient manner on a storage unit (e.g. on a storage unit of a vehicle).”)
transmitting the stored data to a second data storage means physically located at another location. ([0036]: “… a simulation model … is created, … The simulation can be conducted on different platforms. At each point in time, tuples, groups, or datasets are determined from statistical usage data (150) and corresponding lifetime information. This process can be repeated for several different usage scenarios to provide comprehensive training data.”)
Since TWAICE, DING, and HUESSON are in the same technical field, i.e., battery management system, before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have modified TWAICE.s teaching on determining the aging state of the electrical energy storage unit, as discussed above, to clearly include saving data of the first input variables of the first mathematical model and/or data of the second input variables of the second mathematical model in a first data storage means; transmitting the stored data to a second data storage means physically located at another location in view of HUESSON with the motivation of 1) recording how the battery is used (see HUESSON [0033]) and 2) using measured statistical usage data as the training data to train a neural network at another location (see HUESSON [0036]-[0037]) (see MPEP 2143-I-(G)).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Di (China Patent No. CN112014735A) teaches predicting the aging state of the target battery cell using a pre-constructed battery cell aging life prediction model (for SoHR and SoHC prediction); wherein the battery cell aging life prediction model is based on the ambient temperature, discharge rate, and depth of discharge of the battery.
Cai (China Patent No. CN114636932B) teaches predicting aging state using cumulative discharge capacity at each preset voltage point within a preset discharge voltage range with a prediction model comprising a CNN and a LSTM.
Chemali (U.S. Patent No. US11637331B2) teaches an artificial neural network (ANN) based approach to determines, based on the received one or more battery attributes, a state-of-charge (SOC) and/or a state-of-health (SOH) estimate for the Li-ion battery. The ANN includes at least one of a recurrent neural network (RNN) and a convolutional neural network (CNN), and the series of values of the battery attributes includes at one of battery voltage values, battery current values, and battery temperature values.
Yang (Yang, N., Song, Z., Hofmann, H. and Sun, J., 2022. Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest. Journal of Energy Storage, 48, p.103857.) teaches the use of random forest to combine two models for estimating the state of health of lithium-ion batteries.
Zhang (Zhang, R., Zhou, X., Liu, T. and Jin, G., 2022, November. State-of-Health Estimation for Lithium-Ion Battery Based on Multi-Segment Fusion. In 2022 China Automation Congress (CAC) (pp. 6033-6038). IEEE.) teaches a state-of-health (SOH) estimation of lithium-ion batteries based on fusing multiple small charging segments to improve the estimation accuracy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUA MEI H CHEN whose telephone number is (571)270-0493. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached at 5712726334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HUA MEI HARRY CHEN/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights