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 .
Election/Restrictions
Claims 5 and 11-13 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 01/30/2026.
Specification
The disclosure is objected to because of the following informalities:
In [053], line 2, “The communication connection 196” should be --The communication connection 194-- to be consistent in the reference number.
Appropriate correction is required.
Claim Objections
Claims 7-10 are objected to because of the following informalities:
In claim 7, the limitations of sub-steps of “receiving” and “submitting” in lines 5-16 should be further indented to distinguish themselves from the main step of “training.” See MPEP 608.01(m) and 37 CFR 1.75(i) (“Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation”).
In claim 9, line 3, “training data sets of the training data sets” should be --training data sets of the plurality of training data sets-- for better clarity.
The other claim(s) not discussed above, or depending on the above claim(s), are objected to for inheriting the issue(s) from their linking claim(s).
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-4, 6-10, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim is directed to an abstract idea because it recites:
1. A computing system comprising:
at least one memory;
one or more hardware processing units coupled to the at least one memory; and
one or more computer readable storage media storing computer-executable instructions that, when executed, cause the computing system to perform operations comprising:
receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells, the first set of values comprising at least voltage measurement value, at least one present current measurement value, and at least one temperature measurement value;
submitting a first set of input values to a first state of charge estimation model, the first set of input values from the first set of values and at least one prior current value for the first set of one or more battery cells; and
receiving a first state of charge estimate for the first set of input values from the first state of charge estimation model.
The above bold-faced limitations are to input data to a mathematical model and obtain a result from the model. There are directed to an abstract idea involving mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites additional elements as the above underlined limitations. However, these are to invoke a generic computer (or conventional computer components) for its computing power to facilitate the application of the abstract idea. See MPEP 2106.05(f). Note that the hardware sensors are recited as the sources for data collection, which is an insignificant extra-solution activity (see MPEP 2106.05(g)) and/or a field of use (see MPEP 2106.05(h)). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two above, the additional element(s) in the claim are an insignificant extra-solution activity, to invoke a generic computer for its computing power to facilitate the application of the abstract idea, and/or to indicate the data source or environment, which is a field of use. Also, it is routine and conventional to invoke a computer for data processing. See MPEP 2106.05(d). Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claim 19 is similarly rejected by analogy to claim 1.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter (Step 1 - No) because a computer-readable storage media covers both non-statutory subject matter (e.g. transitory propagating signals) and statutory subject matter (e.g. non-transitory tangible media). See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). To address this issue, further limitation, such as adding "non-transitory" to the claim(s), is needed. The claim is also rejected by analogy to claim 1 regarding the abstract idea.
Dependent claims 2-4, 6-10, and 14-18 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
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.
Claims 1-4, 6-10, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Keene et al. (US 20230246458 A1; hereinafter “Keene”) in view of BILETSKA et al. (US 20160202324 A1; hereinafter “BILETSKA”).
Regarding claim 1, Keene teaches a computing system (i.e., “Systems and methods are provided for state-of-charge balancing in battery management systems for Si/Li batteries”; see Abstract) comprising:
at least one memory (i.e., “memory”; see [0122]);
one or more hardware processing units (i.e., “processor”) coupled to the at least one memory (see [0122]); and
one or more computer readable storage media (i.e., “medium”) storing computer-executable instructions that, when executed, cause the computing system to perform operations (i.e., see [0122]) comprising:
receiving a first set of values (i.e., “the ML model may be trained using a combination of data features relating to one or more of voltage, current, temperature”; see [0044]; “the ML model considers the voltage and current at the beginning of charge. The ML model is also given the values directly before and after charge current is applied”; see [0086]; note that the “voltage, current, temperature” are input data for the model to estimate SOCs during both training (see [0044]) and inference (see [0086]));
submitting a first set of input values to a first state of charge estimation model, the first set of input values from the first set of values (i.e., “the ML model may be trained using a combination of data features relating to one or more of voltage, current, temperature) and at least one prior current value for the first set of one or more battery cells value (i.e., “the complete voltage, current, and temperature history of the cell”; see [0044]; note that the “voltage, current, temperature” are input data for the model to estimate SOCs during both training (see [0044]) and inference (see [0086])); and
receiving a first state of charge estimate for the first set of input values from the first state of charge estimation model (i.e., “the SOC model used in the BMS 140 to calculate the SOC may be a machine-learning (ML) model trained on data acquired during operation of multiple cells and battery packs”; see [0044]; see, also, [0086] regarding estimating the SOCs using the trained SOC model).
Keene does not explicitly disclose (see only the underlined):
receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells.
But BILETSKA teaches:
receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells (i.e., “a voltage sensor CV for measuring the voltage across the terminals of the battery; a current sensor CI for measuring a current provided (or absorbed) by the battery, a first temperature sensor CT1 for measuring an internal temperature of the battery and a second temperature sensor CT2 for measuring an ambient temperature outside the temperature”; see [0057).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Keene in view of BILETSKA by receiving a first set of values from one or more sets of hardware sensors associated with a first set of one or more battery cells, as claimed. The rationale would be to facilitate the measurements of voltage, current and temperature of the cells.
Regarding claim 2, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to increase an amount of energy supplied to at least one battery cell of the first set of one or more battery cells; and
supplying energy to the at least one battery cell.
However, Keene teaches:
subsequent to receiving the first state of charge estimate for the first set of input values, controlling and balancing the energies of the cells (i.e., “State-of-charge (SOC) of one or more lithium-ion cells may be assessed, and based on the assessing of the SOC, the one or more lithium-ion cells may be controlled. The controlling may include setting or modifying one or more operating parameters of at least one lithium-ion cell, and the controlling may be configured to equilibrate the SOC of the one or more lithium-ion cells or to modify an SOC of at least one lithium-ion cell so that the one or more lithium-ion cells have a balanced SOC”; see Abstract).
It is well-known to supply energy (charge) to a lower SOC battery cell to help balance the cells. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the steps of subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to increase an amount of energy supplied to at least one battery cell of the first set of one or more battery cells; and supplying energy to the at least one battery cell, as claimed. The rationale would be to balance the cells.
Regarding claim 3, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to withdraw energy from at least one battery cell of the first set of one or more battery cells; and
withdrawing energy from the at least one battery cell.
However, Keene teaches:
subsequent to receiving the first state of charge estimate for the first set of input values, controlling and balancing the energies of the cells (i.e., “State-of-charge (SOC) of one or more lithium-ion cells may be assessed, and based on the assessing of the SOC, the one or more lithium-ion cells may be controlled. The controlling may include setting or modifying one or more operating parameters of at least one lithium-ion cell, and the controlling may be configured to equilibrate the SOC of the one or more lithium-ion cells or to modify an SOC of at least one lithium-ion cell so that the one or more lithium-ion cells have a balanced SOC”; see Abstract).
It is well-known to withdraw energy (discharge) from a higher SOC battery cell to help balance the cells. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the steps of subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to withdraw energy from at least one battery cell of the first set of one or more battery cells; and withdrawing energy from the at least one battery cell, as claimed. The rationale would be to balance the cells.
Regarding claim 4, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to disconnect at least one battery cell of the first set of one or more battery cells from a circuit; and
disconnecting the at least one battery cell from the circuit.
However, Keene teaches:
subsequent to receiving the first state of charge estimate for the first set of input values, controlling and balancing the energies of the cells (i.e., “State-of-charge (SOC) of one or more lithium-ion cells may be assessed, and based on the assessing of the SOC, the one or more lithium-ion cells may be controlled. The controlling may include setting or modifying one or more operating parameters of at least one lithium-ion cell, and the controlling may be configured to equilibrate the SOC of the one or more lithium-ion cells or to modify an SOC of at least one lithium-ion cell so that the one or more lithium-ion cells have a balanced SOC”; see Abstract).
It is well-known to use a circuit switch to connect/disconnect a battery cell for controlling the charging and discharging. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Keene by, subsequent to receiving the first state of charge estimate for the first set of input values, receiving a command to disconnect at least one battery cell of the first set of one or more battery cells from a circuit; and disconnecting the at least one battery cell from the circuit, as claimed. The rationale would be to balance the cells.
Regarding claim 6, Keene further teaches:
wherein the first state of charge estimation model comprises a machine learning model (i.e., “the SOC model used in the BMS 140 to calculate the SOC may be a machine-learning (ML) model”; see [0044]).
Regarding claim 7, Keene further teaches:
training the first state of charge estimation model, the training the first state of charge estimation model comprising:
receiving a plurality of training data sets, a given training data set of the plurality of training data sets comprising, for a second set of one or more battery cells, wherein the second set of one or more battery cells is the same as the first set of one or more battery cells or where one or more battery cells of the first set of one or more battery cells are not included in the second set of one or more battery cells, a second set of input values, the second set of input values comprising at least voltage measurement value, at least one current measurement value, at least one prior current measurement value, at least one temperature measurement value (i.e., “the ML model may be trained using a combination of data features relating to one or more of voltage, current, temperature … the complete voltage, current, and temperature history of the cell”; see [0044]; “ the ML model considers the voltage and current at the beginning of charge”; see [0086]),
submitting at least a portion of the plurality of training data sets to a machine learning algorithm to provide a machine learning model (i.e., “the ML model may be trained using a combination of data features relating to one or more of voltage, current, temperature … the complete voltage, current, and temperature history of the cell”; see [0044]; “ the ML model considers the voltage and current at the beginning of charge”; see [0086]).
Keene does not explicitly disclose (see only the underlined):
the second set of input values comprising at least voltage measurement value, at least one current measurement value, at least one prior current measurement value, at least one temperature measurement value and at least one state of charge estimate.
But BILETSKA further teaches:
providing reference SOC data corresponding to respective training input data for training an SOC estimation model (i.e., “constructing the regression models of said set by training on the basis at least of a plurality of time series of measurements of voltage across the terminals of said battery and of at least one other time series of measurements of another physical parameter of said battery or of its environment, and of corresponding reference values of the state of charge of said battery”; see [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Keene in view of BILETSKA such that the second set of input values comprising at least voltage measurement value, at least one current measurement value, at least one prior current measurement value, at least one temperature measurement value and at least one state of charge estimate, as claimed. The rationale would be to help training the SOC estimation model.
Regarding claims 8 and 9, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
(claim 8) filtering the plurality of training data sets by comparing an error value associated with a given state of charge estimate with a first threshold and not submitting training data sets of the plurality of training data sets to the machine learning model that do not satisfy the first threshold.
(claim 9) wherein the filtering further comprises comparing the at least one current measurement value to one or more second thresholds and not submitting training data sets of the training data sets to the machine learning model that do not satisfy at least one threshold of the one or more second thresholds.
However, it is well-known to clean measured data by removing outliers or noises, such as those deviate from a normal range (or trend) more than a threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Keene in view of BILETSKA by incorporating the steps of filtering the plurality of training data sets by comparing an error value associated with a given state of charge estimate with a first threshold and not submitting training data sets of the plurality of training data sets to the machine learning model that do not satisfy the first threshold; and comparing the at least one current measurement value to one or more second thresholds and not submitting training data sets of the training data sets to the machine learning model that do not satisfy at least one threshold of the one or more second thresholds, as claimed. The rationale would be to apply data cleaning to avoid the influence of outliers or noises.
Regarding claim 10, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
wherein the at least one state of charge estimate is produced by measuring coulombs provided to and withdrawn from battery cells of the second set of one or more battery cells.
But BILETSKA further teaches:
wherein the at least one state of charge estimate is produced by measuring coulombs provided to and withdrawn from battery cells of the second set of one or more battery cells (i.e., “The simplest and best known way of estimating the SoC is “coulometry”, which consists in calculating the amount of charge CF/E provided by/extracted from a battery relative to the maximum capacity Cmax of the battery”; see [0012]; “the use of said coulometric estimators as reference values of the state of charge of said battery for the construction or reconstruction by training of said regression models”; see [0043]).
Regarding claim 14, Keene further teaches:
combining the first state of charge estimate with at least a second state of charge estimate (i.e., “implementations based on the present disclosure may be combined with other solutions and control techniques, including those in which SOC may be calculated by other means”; see [0089]).
Regarding claims 15 and 16, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
(claim 15) wherein the combining the first state of charge estimate with the at least a second state of charge estimate is based at least in part on respective uncertainties associated with the first state of charge estimate and the at least a second state of charge estimate.
(claim 16) wherein the combining involves employ a Kalman filter.
But BILETSKA further teaches:
combining SOC estimation using a Kalman filter which considers estimation uncertainties (i.e., “The coulometric model for estimating the SoC can be improved by combining it with models of other measurable physical quantities by means of data fusion techniques such as Kalman filtering”; see [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Keene in view of BILETSKA such that wherein the combining the first state of charge estimate with the at least a second state of charge estimate is based at least in part on respective uncertainties associated with the first state of charge estimate and the at least a second state of charge estimate and that the combining involves employ a Kalman filter, as claimed. The rationale would be to help improving the SOC estimation (see BILETSKA, [0018]).
Regarding claims 17 and 18, the prior art applied to the preceding linking claim(s) teaches the features of the linking claim(s).
Keene does not explicitly disclose:
(claim 17) wherein the at least a second state of charge estimate is based on a coulomb counting technique.
(claim 18) wherein the coulomb counting technique involves measuring coulombs provided to, and coulombs.
But BILETSKA further teaches:
wherein the at least a second state of charge estimate is based on a coulomb counting technique (i.e., “The coulometric model for estimating the SoC can be improved by combining it with models of other measurable physical quantities by means of data fusion techniques such as Kalman filtering”; see [0018]).
wherein the coulomb counting technique involves measuring coulombs provided to, and coulombs (i.e., “The simplest and best known way of estimating the SoC is “coulometry”, which consists in calculating the amount of charge CF/E provided by/extracted from a battery relative to the maximum capacity Cmax of the battery. The amount of charge CF/E is estimated by integrating the current I(t) during the use of the battery”; see [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Keene in view of BILETSKA such that the at least a second state of charge estimate is based on a coulomb counting technique and that the coulomb counting technique involves measuring coulombs provided to, and coulombs, as claimed. The rationale would be to help providing a different SOC estimate.
Regarding claim 19, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings.
Regarding claim 20, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
ZHU et al. (CN 108334940 A) teaches a deep neural network SOC estimation model for battery cells, involving using both historical and current battery measurement data as input for the model to estimate the SOC for each cells.
Chemali et al. (US 20220271549 A1) teaches a neural network SOC estimation model, involving using battery voltage, current, and temperature, and time-averaged values of thereof, as input for the SOC model.
Tian et al. ("State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach" Applied Energy 291 (2021) 116812) teaches a deep neural network SOC estimation model, involving using 10-min charging voltage, current, and optionally, temperature, as the input to the model.
Sadykov et al. ("Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles" Energies 2023, 16, 1628) investigate a number of RNN-based SOC estimation model using sequential voltage, current and temperature data input, and validate the models using SOCs calculated by coulomb counting.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857